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WO2018146564A1 - Acquisition d'informations concernant un volume au moyen de réseaux sans fil - Google Patents

Acquisition d'informations concernant un volume au moyen de réseaux sans fil Download PDF

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Publication number
WO2018146564A1
WO2018146564A1 PCT/IB2018/050280 IB2018050280W WO2018146564A1 WO 2018146564 A1 WO2018146564 A1 WO 2018146564A1 IB 2018050280 W IB2018050280 W IB 2018050280W WO 2018146564 A1 WO2018146564 A1 WO 2018146564A1
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WIPO (PCT)
Prior art keywords
node
signal
signals
objects
target volume
Prior art date
Application number
PCT/IB2018/050280
Other languages
English (en)
Inventor
Gil Zwirn
Original Assignee
Gil Zwirn
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/430,033 external-priority patent/US10405222B2/en
Application filed by Gil Zwirn filed Critical Gil Zwirn
Publication of WO2018146564A1 publication Critical patent/WO2018146564A1/fr

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
    • G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
    • G08G1/147—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas where the parking area is within an open public zone, e.g. city centre
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
    • G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
    • G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Definitions

  • the present invention relates generally to employing wireless networks for acquiring information regarding terrain and/or objects within a volume.
  • Wireless networks are used to transfer information between two or more spatial locations which are not physically linked.
  • the information may be of any kind, e.g., voice, still or moving images, text and so forth.
  • the information is typically transferred using radio frequency (RF) and/or infrared radiation.
  • RF radio frequency
  • WLANs Wireless personal area networks
  • Bluetooth networks which interconnect devices within a relatively small area
  • Wireless local area networks (WLANs), linking two or more devices over a relatively short distance, usually providing internet connection through an access point; (c) Wireless mesh networks, whose nodes are organized in mesh topology, in which each node forwards messages on behalf of other nodes. Such networks automatically reroute around faulty nodes;
  • Wireless metropolitan area networks e.g., WiMax, which may connect several WLANs;
  • Wireless wide area networks (wireless WANs), which typically cover large areas, e.g., between neighboring towns;
  • Cellular networks or mobile networks distributed over areas called cells, each of which served by at least one fixed-location transceiver, known as a cell site or base station.
  • Each cell typically uses a set of radio frequencies and/or codes which is different from that of the immediate neighboring cells, so as to reduce interference.
  • multiple cells may provide coverage over wide geographic areas, enabling a large number of portable transceivers, such as mobile phones (including smart phones) and pagers, to communicate with each other and with fixed transceivers and telephones anywhere in the network, via base stations.
  • FDMA frequency division multiple access
  • TDMA time division multiple access
  • GSM global system for mobile communications
  • CDMA code division multiple access
  • GPRS general packet radio service
  • W-CDMA wideband code division multiple access
  • EDGE enhanced data rates for GSM evolution
  • CDMA2000 CDMA2000, orthogonal frequency division multiple access (OFDMA), and so forth
  • FDMA frequency division multiple access
  • TDMA time division multiple access
  • GSM global system for mobile communications
  • CDMA code division multiple access
  • GPRS general packet radio service
  • W-CDMA wideband code division multiple access
  • EDGE enhanced data rates for GSM evolution
  • CDMA2000 orthogonal frequency division multiple access
  • OFDMA orthogonal frequency division multiple access
  • the location of mobile devices e.g., cellular phones
  • wireless networks is sometimes estimated using these networks.
  • the estimation may be based on measurements made directly by the wireless network infrastructure and/or on external sources of information, e.g., global positioning system (GPS) trackers associated with the mobile devices.
  • GPS global positioning system
  • US2012/109853 by Culpepper, Smith and Vancleave, published on May 3, 2012, titled "Method and system for providing tracking services to locate an asset,” discloses a method and system for asset location.
  • Location data is received from a cellular transmitter associated with a selected asset, which location data includes data representative of a cellular receiver with which direct communication with the cellular transmitter is made.
  • the location data is then communicated to a tracking service system, which tracking service system includes a database representative of geographic locations associated with the plurality of cellular receivers.
  • the database is then queried with received location data so as to generate geographic tracking data associated with a location of a cellular receiver, the geographic tracking data including display data adapted to generate a map image including a representative of a location of the selected asset.
  • the geographic tracking data is then communicated to an associated security agency so as to allow for viewing of an image generated in accordance with the display data and at least one of tracking and interception of the selected asset.
  • location data is also received from a GPS location system associated with the cellular transmitter.
  • the covered area estimation is calculated from actual environment data received through a wireless cellular communication network, and possibly but not necessarily from external databases.
  • the covered area estimation comprises at least some of the following calculations: (i) estimation of base station location; (ii) estimation of transmission range; (iii) estimation of signal map; and (iv) estimation of area type.
  • the actual location of the mobile device is determined from the covered area estimation based on relative comparison between the actual environment data and estimations (i)-(iv) and weight numbers resulted from the comparison.
  • a database is stored in the server and updated whenever new environment data is received.
  • a further example is US patent application US2011/0059752, by Garin, Do and Zhang, published on March 10, 2011, titled "Concurrent wireless transmitter mapping and mobile station positioning,” which discloses a method for concurrently estimating locations for one or more mobile stations and one or more mobile transmitters, said method comprising: receiving at a computing platform a plurality of range measurements from one or more mobile stations with unknown positions, the plurality of range measurements comprising one or more range measurements to one or more wireless transmitters with unknown positions and one or more range measurements to one or more wireless transmitters with known positions; and concurrently estimating locations for the one or more mobile stations with unknown positions and for the one or more wireless transmitters with unknown positions.
  • Wireless networks can also be used to estimate the location of multiple mobile devices as a function of time. Based on this information, one can create road maps, analyze traffic flow and provide dynamic route guidance for drivers.
  • US patent application US2010/211301, by McClellan, published on August 19, 2010, titled "System and method for analyzing traffic flow,” discloses a system and method for analyzing traffic flow, comprising receiving location reports from a plurality of mobile devices, each of the location reports identifying a current location and current speed for a particular mobile device. For each of the location reports, the system identifies a current street from a street mapping database using the current location. The system stores the current speeds for the mobile devices so that each of the current speeds is associated with a street in the street mapping database.
  • the current speeds may be stored in the street mapping database or in a separate database that is linked to the street mapping database.
  • US patent application US2010/057336 by Levine, Shinar and Shabtai, published on March 4, 2011, titled "System and method for road map creation," which discloses a system and method for creation of a road map, the system comprising a plurality of navigation devices; and an application server to receive from the plurality of navigation devices time series of location points, and to create a road map based on the time series of location points.
  • the method comprises receiving location points from a plurality of navigation devices, along with respective time stamps indicating the time of recordation of each of the location points; identifying at least one route according to the location points and respective time stamps; and creating a road map based on the at least one route.
  • US patent application US2011/098915 by Disatnik, Shmuelevitz and Levine, published on April 28, 2011, titled “Device, system, and method of dynamic route guidance," which discloses a device, system and method of dynamic route guidance.
  • the method may include: calculating an optimal route from a first location, in which a navigation device is located, to a destination point entered by a user of said navigation device; receiving from the navigation device a travel update, indicating that the navigation device is located in a second location, wherein the second location is on said optimal route; and based on real-time traffic information and real-time road information, determining that an alternate route, from the second location to the destination point, is now an optimal route to the destination point.
  • mobile devices connected to wireless networks can be used to map network performance parameters as a function of space and/or time.
  • US patent application US2006/246887 by Barclay, Benco, Mahajan, McRoberts and Ruggerio, published on November 2, 2006, titled "Mapping of weak RF signal areas in a wireless telecommunication system using customers' mobile units," discloses a wireless mobile device, which includes an RF transmitter and receiver, where the receiver monitors signal strength of an RF signal from a base station.
  • a control logic module compares the signal strength to a comparison level.
  • the control logic module creates and stores a record in a memory module.
  • the record includes a first signal strength level and parameters related to conditions existing at the time the comparing was done.
  • the control logic module creates and stores the record if the level of said signal strength is less than the comparison level.
  • US patent application US2008/169921 by Peeters, published on July 17, 2008, titled “Method and apparatus for wide area surveillance of a terrorist or personal threat,” discloses methods and apparatuses for the wide area detection of major threats, including chemical, radiological or biological threats, using modified personal wireless devices, such as mobile phones, personal digital assistants (PDAs) or watches, combined with micro- and nano- sensor technologies.
  • PDAs personal digital assistants
  • a "homeland security" chip is further provided, which combines the elements of geo-location, remote wireless communication and sensing into a single chip.
  • the personal electronic devices can be further equipped for detecting various medically related threats. Similarly modified personal devices can be used to detect external threats that are person-specific.
  • Another example is US patent US7,952,476, by Causey, Andrus, Luu, Jones and Henry, issued on May 31, 2011, titled "Mobile security system,” which discloses a mobile security system, wherein a detector communicates with a mobile device if an event has occurred.
  • the event may be of various types, such as fire or motion.
  • the mobile device may, among others, sound an alarm or communicate with a central monitoring system to notify emergency services of the occurrence.
  • the mobile device may also communicate with another communication device, such as another cell phone or a computer, using various forms of communication.
  • the detector may be an integral part of the mobile device, and may also be wholly separate.
  • Certain methods and systems known in the art employ sensors based on RF radiation for object detection outside the context of wireless networks.
  • the object detection is based on active sensing.
  • UK patent application GB2473743 by Bowring and Andrews, published on March 23, 2011, titled "Detecting hidden objects,” discloses a system and method for detecting and identifying hidden objects, for instance for airport security screening.
  • Low power plane-polarized microwave radiation is directed towards a person, and scattered radiation is detected by a detector sensitive to polarization in an orthogonal plane (cross-polarization).
  • the transmitted and received planes of polarization are varied, either by rotation of both transmitting and receiving antennas on a common platform, synchronized rotation of both, or switching between antennas having fixed polarizations.
  • the transmitted frequency is modulated over a broad range, using wide- band frequency modulation continuous wave (FMCW).
  • FMCW wide- band frequency modulation continuous wave
  • the output signal of the receiver over a period of time is compared with expected returns in a neural network to identify the nature of any hidden object, and can distinguish a large knife, small knife, handgun and so on.
  • An ultrasound sonar or stereoscopic camera may determine the distance to the person.
  • PCT application WO2009/090406 by Mehta, published on July 23, 2009, titled “Microwave imaging system,” which discloses a microwave imaging system for imaging a defined region, the system comprising a plurality of portable RF identification (RFID) tags, distributed around said region, for generating a plurality of RF signals and directing said signals to said defined region, and for receiving RF signals from said defined region; and means for transmitting the characteristics of said received signals to a remote processing station through a wireless communication channel, extracting image data from said received signals and constructing a corresponding image.
  • RFID portable RF identification
  • US patent US8, 179,310, by Westphal, issued on May 15, 2012, titled “Method for sensing a threat,” discloses a method for threat analysis based on the passive radar principle, using the transmitter in navigation satellites, a plurality of receiving stations, which are operated distributed over wide regions, and at least one evaluation center.
  • the receiving stations act as wake -up sensors, transmit their received signals to at least one evaluation center for comparison with expected signals from each navigation satellite, and sense a threat.
  • stationary or mobile radar systems can then be used to obtain more precise details relating to a conspicuous entity, making it possible to decide on currently required protective or defensive measures.
  • the method comprises the following operations: determining, in a distance-Doppler matrix of the radar, points relative to the deviations between the rays received directly from the transmitters and the rays reflected by the movable object; transferring to a map to be established a probable zone of location of singularities of the electromagnetic field transmitted or reflected by the ground; and crossing several probable zones during the movement of the movable object in order to obtain the location of the singularities.
  • signal strength values or other similar quality indications may be analyzed as they are received with packet data over a wireless network. The analysis may be used to determine the presence of a physical object substantially between communicating nodes in a wireless network. An output may be generated based on analyzed data.
  • US patent US6,745,038, by Callaway, Perkins, Shi and Patwari, issued on June 1, 2004, title "Intra-piconet location determination and tomography,” discloses a technique for intra-piconet location determination and tomography.
  • This technique uses received signal strength indicator (RSSI) values in conjunction with transmitted power levels to determine the relative location of each device within a small network employing frequency hopped spread spectrum transmission.
  • RSSI received signal strength indicator
  • the geometry of the devices in the network, as well as the path loss information between pairs of devices may be used to infer the location of absorbers and reflectors within the piconet. This absorption and reflection information may be used in creating the piconet tomography.
  • the approach described in this specification may be applied in conjunction with the Bluetooth PAN specification to determine device locations, mitigate the effects of multi-path, and perform indoor location and security functions, and other application functions requiring cost- effective location determination.
  • Embodiments of the present invention provide methods and devices for acquiring information regarding terrain and/or objects within a volume using wireless networks.
  • a method for traffic and/or parking monitoring using signals transmitted by wireless networks comprising:
  • node signal receivers receiving signals transmitted by one or more nodes of wireless networks using one or more receiving units
  • the transmitted signals are “node signals” and the signals received after traversing a medium are “node resultant signals”
  • each of said one or more node signal receivers is configured to receive signals associated with one or more transmitting nodes of wireless networks ("transmitting subject network nodes");
  • object compounding For each association result, compound said physical parameter measurements relating to the corresponding object records ("object compounding"), in order to obtain additional or more precise information regarding the corresponding physical object within said target volume, wherein the term "object record” refers to an output of either object detection or object compounding.
  • Fig. 1 is a schematic, pictorial illustration of a system for acquiring information regarding terrain and/or objects within a volume, in accordance with an embodiment of the present invention
  • Fig. 2 is a schematic, pictorial illustration of a system for acquiring information regarding terrain and/or objects within a volume, in accordance with an embodiment of the present invention
  • Fig. 3 is a schematic block diagram of detection based vehicle monitoring, in accordance with an embodiment of the present invention.
  • the blocks with dashed outlines, 250 and 260, are optional and their location within the block diagram flow may vary; and
  • Fig. 4 is a schematic block diagram of imaging based vehicle monitoring, in accordance with an embodiment of the present invention.
  • the blocks with dashed outlines, 350 and 360, are optional and their location within the block diagram flow may vary.
  • the present invention relates to methods and systems for acquiring information regarding terrain and/or objects within a volume ("target volume”) using wireless networks.
  • one or more wireless networks are provided.
  • one or more wireless networks are provided.
  • subject networks include at least two nodes, wherein one or more of the nodes of the subject networks (“transmitting subject network nodes”) transmit signals over time (“node signals").
  • the node signals traverse a medium, such as the atmosphere or free space, undergoing various physical phenomena, such as attenuation, reflection from various objects, scattering by various objects, refraction by various objects, diffraction, dispersion, multi-path, and so forth (the resulting signals are referred to as the "node resultant signals”), and are received by one or more receiving units (“node signal receivers").
  • the node resultant signals, received by the node signal receivers are analyzed by one or more processing units (“mapping units").
  • the system further includes one or more user units, capable of controlling the system and/or displaying its outputs.
  • the user units may employ any computing platform, such as a server, a desktop, a laptop, a tablet computer, a smart phone, and the like.
  • all the transmitting subject network nodes and all the node signal receivers are stationary. In other embodiments, at least one of the transmitting subject network nodes and/or at least one of the node signal receivers are mobile.
  • the subject network may be of any type known in the art, e.g., WPAN, WLAN, wireless mesh network, wireless MAN, wireless WAN, cellular network, mobile satellite communications network, radio network and/or television network.
  • the transmitting subject network nodes may be of any kind known in the art, e.g., base stations and/or mobile phones in a cellular network.
  • the node resultant signals corresponding to the different node signals may be differentiated based on any method known in the art.
  • each of the transmitting subject network nodes may employ a different frequency band, a different code type (e.g., linear frequency modulation, phase shift keying, frequency shift keying and so forth), a different set of code parameters, and/or a different polarization scheme (e.g., horizontal or vertical linear polarization, right-hand or left- hand circular polarization and so on), so that the resulting signal waveforms would be essentially orthogonal.
  • a different code type e.g., linear frequency modulation, phase shift keying, frequency shift keying and so forth
  • a different set of code parameters e.g., horizontal or vertical linear polarization, right-hand or left- hand circular polarization and so on
  • multiple access methods may be employed, e.g., time division multiple access (TDMA), frequency division multiple access (FDMA), code division multiple access (CDMA), or orthogonal frequency division multiple access (OFDMA).
  • TDMA time division multiple access
  • FDMA frequency division multiple access
  • CDMA code division multiple access
  • OFDMA orthogonal frequency division multiple access
  • the transmitting subject network nodes may employ the same waveform, but be sufficiently separated spatially (e.g., the transmitting subject network nodes may be distant from each other and/or transmit at separated spatial angles) to support reasonable differentiation and acceptable levels of mutual interference.
  • each node signal receiver may be one of:
  • node of the subject network which may be one of the transmitting subject network nodes or one of the other nodes;
  • a sensor configured to measure the node signals and/or the node resultant signals (“network sensor”).
  • the network sensor may be one of:
  • each node signal receiver may employ one or more of the following:
  • a single receive beam at any given time The receive beam may either point at a constant direction or change its direction over time (by mechanical and/or electronic steering).
  • the angular measurement accuracy of the single node signal receiver typically matches the receive beam width, and different objects typically cannot be separated based on their spatial angle with respect to the node signal receiver;
  • all node signals are produces as a part of the normal operation of the wireless network. In other embodiments, some or all of the node signals are especially produced for acquiring information regarding the target volume; for example, one or more nodes may transmit signals at time varying directions, scanning the target volume over time, and employing radar-like processing. In further embodiments, the analysis of the node resultant signals may be performed analogically, digitally, or using a combination thereof.
  • one or more mapping units are associated with at least one of the node signal receivers ("local mapping units").
  • one or more central mapping units analyze the outputs of the local mapping units and/or the node resultant signals.
  • all node resultant signals are processed by one or more central mapping units.
  • the central processing unit may be completely separated from the local mapping units. In other embodiments, one or more of the local mapping units may also serve the function of central mapping units. In further embodiments, the functions of the central mapping units may be divided between several local mapping units.
  • the spatial location as a function of time for one or more of the transmitting subject network nodes and/or one or more of the node signal receivers is either measured directly or can be estimated.
  • Location measurements can be made by means of any navigation system known in the art, e.g., using GPS and/or inertial navigation, wherein the resulting location information may or may not be filtered over time to enhance results. Additionally or alternatively, location estimation may be made employing any method known in the art, e.g., the methods of patent applications US2012/109853, US2010/120449 and/or
  • the subject network 100 comprises transmitting subject network nodes 11 and non-transmitting subject network nodes 12.
  • the node signals 20 traverse the medium, and the node resultant signals are received by the node signal receivers 30. These signals are then processed by the local mapping units 40 and/or central mapping unit 50.
  • wireless transmissions are marked by dash-dotted lines, and data lines, which may be wired or wireless, are marked by dotted lines.
  • the subject network 110 comprises transmitting subject network nodes 11, non- transmitting subject network nodes 12, and node signal receivers 15.
  • the node signals 20 traverse the medium, and the node resultant signals are received by the node signal receivers 15.
  • These signals are then processed by the local mapping units 40 and/or central mapping unit 50.
  • wireless transmissions are marked by dash- dotted lines, and data lines, which may be wired or wireless, are marked by dotted lines.
  • one or more of the following physical parameters is measured per node signal receiver and per transmitting subject network node, wherein the measurements may be made either for the entire node resultant signal or for certain time swaths thereof:
  • c is the speed of the signal's propagation within the medium, e.g., the speed of light
  • phase difference, ⁇ Phase difference between the transmitted node signal and the received node resultant signal.
  • the phase difference may be measured by applying a matched filter between the node signal and the node resultant signal.
  • the matched filter may be applied for one or more of these sections. Such techniques may necessitate phase synchronization between one or more of the transmitting subject network nodes and one or more of the node signal receivers.
  • the measured phase difference may be used to enhance the estimation of the distance traversed by the signal along its path through the medium, based on the following equation:
  • Power ratio, R p Power ratio, between the transmitted node signal and the received node resultant signal.
  • RSSI received signal strength indication
  • This power ratio is affected by the distance traversed by the node signal along its path through the medium (the local signal power is inversely proportional to the squared distance from the transmitting subject network node, and to the medium losses, which increase with distance), as well as by physical attributes of objects along the path of the node signal (e.g., reflection coefficients of surfaces from which the signal has been reflected, and/or attenuation coefficients of objects along the path of the signal);
  • Frequency difference, fo between the received node resultant signal and the transmitted node signal, i.e., the signal's Doppler shift.
  • the frequency of the received node resultant signal may be measured by any method known in the art. For example, in cases where a transmitted node signal comprises multiple pulses, and a node signal receiver employs digital processing, one may apply Fourier analysis to one or more time gates of a received node resultant signal. Time gates of the received signal are defined as time intervals of a certain duration, wherein time is measured with respect to the rise-time of the last transmitted pulse, so that the number of samples for each time gate equals the number of pulses for which measurements have been made. According to the Doppler effect, in cases where the node signal interacts with M objects along its path, the Doppler shift equals:
  • d/dt is the time derivative operator
  • R n , m is the distance between the n'th and the m'th object along the signal's path
  • the O'th object is the applicable transmitting subject network node
  • the (M+l)'th object is the applicable node signal receiver
  • the remaining objects are ordered according signal's interaction sequence with them.
  • the Doppler shift equals:
  • ( ⁇ ) is the dot-product operator
  • V N is velocity vector of the applicable transmitting subject network node
  • V R is the velocity vector of the applicable node signal receiver
  • R N is the unit vector of the signal's path through the medium just outside the applicable transmitting subject network node
  • R R is the unit vector of the signal's path through the medium just outside the applicable node signal receiver, wherein all vectors are defined with respect to the same predefined coordinate system ("reference coordinate system");
  • node resultant signal direction i.e., the unit vector of the node resultant signal's path through the medium just outside the applicable node signal receiver, and/or its projection on one or more predefined axes.
  • This direction and/or its projections may be measured using any method known in the art. For example, when an applicable node signal receiver supports two or more concurrent receive beam configurations, one may employ monopulse techniques, which are commonly used in radar systems. Additionally or alternatively, when an applicable node signal receiver supports two or more different receive beam configurations, each of which employed at a different time, one may make use of a predefined scanning pattern, e.g., conical scan, which is ubiquitous in radar systems.
  • the two or more receive beam configurations may differ from each other in at least one of the following parameters:
  • interferometric methods and/or multilateration methods may be employed. Note that, in cases where the node signal interacts with one or more objects along its path, the direction from which the node resultant signal arrives relates to the section of the path from the last object with which the node signal interacts to the node signal receiver; and/or
  • the received node resultant signal is the coherent sum of the signals resulting from the different signal paths, each of which is referred to as a "node resultant signal component".
  • one or more of signal attributes (a)-(e) may be measured for one or more node resultant signal components.
  • one may employ any method known in the art for separating the different received signal components, or for extracting the signal attributes directly from multiple received signal components. For example, one or more of the following methods may be used:
  • the criteria for a peak to be discernible may include, for example: the peak height provides a signal-to-noise ratio which exceeds a certain threshold; the ratio between the peak height and the maximal peak height exceeds a certain threshold; the peak width is lower than a certain threshold; the ratio between the peak height and the peak width exceeds a certain threshold; and so forth.
  • Multiple autocorrelation peaks may be indicative of multiple node resultant signal components.
  • Such specific parts of the signal may include, for example, control information for data packets, which typically is part of the packets' header and/or trailer.
  • the output may be processed in a way similar to that of the autocorrelation output of method (i) herein above;
  • the signal attribute measurement may also involve comparing two or more node resultant signals so as to extract one or more physical attributes, each of which may be relative of absolute; wherein the term "relative physical attributes" in this context refers to the ratio and/or difference between the values of such physical attributes, associated with two or more node resultant signals. For example, one may apply cross-correlation between two or more node resultant signals or certain time swaths thereof, and detect discernible peaks in the output ("cross-correlation peaks").
  • the cross-correlation peaks may then be used, for instance, for estimating the difference in time duration ("relative time duration") from node signal transmission by the applicable transmitting subject network node to node resultant signal reception by the applicable node signal receiver, associated with two or more node resultant signals.
  • the time duration measurements may be used, for instance, for multilateration.
  • the signal attribute measurement may be performed analogically, digitally, or using a combination thereof.
  • the signal attribute measurement may be performed by the node signal receivers. This may also be done by one or more local mapping units associated with the applicable node signal receivers. Additionally or alternatively, analog or digital data from one or more node signal receivers may be transferred to one or more central mapping units, configured to perform the signal attribute measurements in part or in whole. The central mapping unit may then apply additional processing to these measurements. In further embodiments, signal attribute measurements made by one or more local mapping units may be transferred to a central mapping unit, which may apply additional processing to these measurements.
  • mapping units local mapping units and/or central mapping units.
  • the current locations and/or location history may be employed by the mapping units to estimate the values for one or more of the signal attributes for direct paths between the transmitting subject network nodes and the node signal receivers ("nominal signal attribute values"), without any objects along the node signals' path except for the nominal medium, wherein the nominal medium may be, e.g., the atmosphere or free space.
  • the mapping units may compound the nominal signal attribute values with the measured signal attribute values, to provide information regarding physical phenomena within the medium ("medium attributes"). For instance, one may compute for at least one of the node signal receivers and at least one of the transmitting subject network nodes, for either the applicable node resultant signals or for one or more node resultant signal components, for either the entire node resultant signal or for certain time swaths thereof:
  • This distance difference equals the path delay times the speed of signal's propagation within the medium.
  • the measured distance may be based on the time difference signal attribute and/or on the phase difference signal attribute.
  • the physical distance may be computed either as the geometric distance or as the optic distance within the medium, taking into account refraction effects within the medium that do not result from objects along the node signal's path (e.g., atmospheric refraction, caused by spatial variations in the local atmospheric temperature, pressure and humidity levels);
  • the measured power ratio signal attribute divided by the power ratio between the transmitted node signal and the expected node resultant signal.
  • the result is the attenuation factor resulting from objects along the node signal path, which may be caused by actual attenuation, reflections with reflection coefficients lower than 1.0, scattering and the like.
  • the expected node resultant signal may be computed based on the transmitted signal power and the expected reduction in power as a function of distance traversed through the medium, using either the measured distance or the physical distance;
  • the measured power ratio signal attribute divided by the power ratio between the transmitted node signal and the node resultant signal expected based on the assumption that the node signal traverses along a straight line or along an optic path, taking into account refraction effects within the medium that do not result from objects along the node signal's path.
  • the result (“path delay attenuation factor”) may be used to estimate the distance R traversed by the node signal along its path through the medium, in cases where the time difference signal attribute is not computed.
  • the distance R may be approximately derived from the path delay attenuation factor by imposing a certain assumption. For example, if we attribute the entire path delay attenuation factor to losses within the medium due to path delay, we can estimate the difference between R and D. This assumption may be appropriate, e.g., for the earliest node resultant signal component; and/or
  • the node resultant signal direction attribute provides information regarding affecting parameter (ii).
  • the mapping units may analyze one or more node resultant signals and/or signal attributes and/or medium attributes for one or more transmitting subject network nodes and one or more node signal receivers, either at a specific time swath or as a function of time, to extract information regarding objects along the signal's paths ("mapping information").
  • the mapping information may include at least one of: digital terrain models (DTM), digital surface models (DSM), as well as detection, localization, characterization, classification and/or tracking data of objects within volumes and/or over terrains, said information may or may not be time dependent and/or space dependent.
  • DTM digital terrain models
  • DSM digital surface models
  • detection, localization, characterization, classification and/or tracking data of objects within volumes and/or over terrains said information may or may not be time dependent and/or space dependent.
  • objects here relates to static and/or dynamic objects, each of which may be inanimate or animate, e.g., animals, human beings, various vehicles, buildings and so forth.
  • mapping information may be produced using at least one of the following methods:
  • the a-priori information may include:
  • External information such as DTM and/or DSM databases.
  • Any forward problem method known in the art may be employed, e.g., ray tracing and/or any wave propagation model appropriate for the frequency band, the network configuration (e.g., area-to-area versus point-to-point) and the medium configuration (e.g., models for indoor versus outdoor applications); (c) Applying a forward problem method, using a-priori information and/or certain assumptions regarding the terrain and/or volume, as in method (b) above, and comparing the measured node resultant signals and/or the signal attributes and/or the medium attributes to computed values;
  • inverse problem method Applying an inverse problem method to one or more measurable physical parameters, such as the local attenuation coefficient and/or the local reflection coefficient and/or spatial location, each of which may or may not be time dependent.
  • Any inverse problem method known in the art may be employed, e.g., Hough transform based algorithms, such as computed tomography, microwave tomography and/or diffraction tomography.
  • (f) Compounding one or more node resultant signals, so as to extract information regarding objects along the signals' paths. This may be performed, for example, using interferometry. Additionally or alternatively, one may employ multilateration techniques for certain objects, e.g., based on relative time duration measurements. Another example is treating two or more node signal receivers as elements of a receiving array, and applying any beamforming technique known in the art to the node resultant signals.
  • node resultant signals and/or signal attributes and/or medium attributes measured at multiple spatial configurations of the transmitting subject network nodes and/or the node signal receivers, are employed for producing mapping information.
  • node resultant signals and/or signal attributes and/or medium attributes measured when the at least one of the objects within the target volume is in different locations, are employed for producing mapping information.
  • mapping information An example for an inverse problem method for producing the mapping information (“multi-path reconstruction method”):
  • This method assumes that the earliest node resultant signal component, which is still affected by multi-path (i.e., excluding the component associated with the direct path from the applicable transmitting subject network node to the applicable node signal receiver), referred to herein as the "first multi-path signal component", is the result of a exactly a single reflection along the signal path ("single reflection assumption").
  • the possible spatial locations of the reflecting surface producing the first multi- path signal component (“component reflecting surface) may be defined using at least one of the following criteria:
  • the component reflecting surface is located over an ellipsoid surface, whose foci correspond to the locations of the applicable transmitting subject network node and the applicable node signal receiver, wherein the ellipsoid is the figure formed from all points whose sum of distances from the two foci equals the measured distance for the first multi-path signal component;
  • the node resultant signal direction for the first multi-path signal component corresponds to the spatial angle between the component reflecting surface and the node signal receiver;
  • the path Doppler shift defines a group of allowable spatial angles between the component reflecting surface and the node signal receiver.
  • the method employs first multi-path signal components corresponding to multiple node resultant signals, and registers the possible spatial locations of reflecting surfaces for each of the first multi-path signal components over a three-dimensional space, in a manner similar to that of the Hough transform. Actual reflective surfaces are located where registrations from a relatively high number of first multi-path signal components are present. Note that this technique compounds information from a large number of node resultant signals, so that outliers being a consequence of the inaccuracy of the single reflection assumption are inherently rejected;
  • (c) After generating a map of the spatial location of reflective surfaces, their reflection coefficient and/or the attenuation along paths between them may be estimated. For example, for each first multi-path signal component, one may assign to the corresponding reflective surface a reflection coefficient corresponding to the path delay attenuation factor. For reflective surfaces affecting the first multi-path signal component of more than one node resultant signal, one may assume that the maximal estimation of the reflection coefficient over all node signals is correct, and assign the remaining power loss to attenuation along the signal's path.
  • Detection of objects within the target volume may be performed by analyzing mapping information. This may be done using one or more of the following:
  • the node signal receivers for the node resultant signals associated with at least one of the transmitting subject network nodes, one may apply matched filtering between the transmitted waveform and the received node resultant signals ("transmit-receive matched filtering"). Strong reflectors on the signal path from the applicable transmitting subject network node to the node signal receiver are expected to produce relatively high power in the transmit- receive matched filtering output. Reflectors can thus be detected by applying a local and/or a global threshold to the power of the transmit-receive matched filtering output.
  • the mapping units may classify objects within the target volume. Any classification and/or target filtering method known in the art may be employed for these purposes. For instance:
  • Object characteristics may include, for instance, estimated object dimensions, motion velocity, reflection coefficient, attenuation coefficient and so forth.
  • the comparison to reference models may be based on any technique known in the art, for example:
  • mapping units may detect and handle only objects of specific types ("relevant objects"), e.g., humans, and not respond to other types of objects.
  • relevant objects e.g., humans
  • classification methods may be used for these purposes as well.
  • mapping units may have to cope with electronic counter measures (ECM). Any method known in the art may be applied to detect and cope with ECM. Some examples for techniques for detecting ECM:
  • Noise jammers e.g., spot, sweep or barrage jammers, may be detected based on their signal pattern as a function of space and/or time; and/or
  • Phantom objects produced by deceptive jammers, may be discerned from true objects based on self consistency checks of the signal attributes associated with such objects. For example, mismatch between the time derivative of the measured distance from one or more of the node signal receivers and the measured Doppler shifts for these node signal receivers may be indicative of phantom objects.
  • the waveform of noise jammers may be estimated, and the subject network's waveforms may be adjusted so as not to be affected by the noise jammers; and/or (b) Detected objects which are determined to be phantom objects may be discarded.
  • the systems and methods of the present invention may be used for a wide variety of applications. Many of these applications are relevant for smart cities. Some examples for applications:
  • Security systems which may detect, localize, characterize, classify and/or track objects within volumes and/or over terrains.
  • the security systems may also detect and/or classify carried objects, such as concealed weapons, explosives and/or drugs.
  • the coverage volumes of these security systems may match the type of subject network or networks used.
  • WPANs may be employed for personal security systems; WLANs for home security systems or for security systems for large buildings or facilities, such as shopping centers, airport terminals, oil rigs and the like; and cellular networks for securing large areas, e.g., borders, defense zones surrounding certain facilities or agricultural areas, as well as large buildings, such as shopping centers, airport terminals and so forth;
  • Obstacle detection for moving vehicles e.g., airplanes, trains, trucks, busses and cars.
  • the subject network may be installed on the moving vehicle itself, and/or on other platforms, each of which may be mobile or immobile; and
  • Terrain and/or volume mapping systems e.g., for cartography. Such systems are typically designed to acquire information regarding immobile objects, whereas mobile elements are discarded.
  • One of the advantages of the systems and methods of the current invention is that the information regarding the terrain and/or the objects within the volume may be acquired using wireless networks, which are very common nowadays. One may use existing networks, and/or add new ones. In some embodiments, only software changes to a wireless network system may be required. In other embodiments, only hardware changes are required, or a combination of hardware and software changes.
  • one or more base stations for cellular or WLAN networks may be added in order to enhance the system's performance, e.g., for improving the object location accuracy.
  • the performance of the wireless network as a communication system may improve as well.
  • One of the possible uses of the present invention is traffic and/or parking monitoring ("vehicle monitoring").
  • vehicle monitoring may comprise (“detection based vehicle monitoring”):
  • Step 210 For each node signal receiver, apply matched filtering between the received node resultant signal and one or more of the waveforms of the transmitting subject network nodes, to obtain the "matched node resultant signals";
  • Step 220 For each matched node resultant signal, apply object detection, and for each output of object detection, measure one or more physical parameters;
  • Step 230 If possible, associate one or more of the outputs of object detection with one or more of the following:
  • Step 240 For each association result of step 230, compound the physical parameter measurements relating to the corresponding object records ("object compounding"), in order to obtain additional or more precise information regarding the corresponding physical object within the target volume, wherein the term "object record” refers to an output of either object detection or object compounding.
  • vehicle monitoring may further comprise one or more of the following (each of the following steps may be applied after any of steps 220, 230, or 240):
  • Step 250 For one or more object records, analyze the associated physical parameter measurements to obtain object classification and/or recognition;
  • Step 260 Discard object records whose classification and/or recognition outputs are irrelevant for vehicle monitoring.
  • any of the waveforms of the transmitting subject network nodes in step 210 may be one or more of the following:
  • applying object detection in step 220 comprises applying a global and/or a local energy threshold to the matched node resultant signal.
  • applying object detection in step 220 may comprise:
  • node sequences select several consecutive transmission sequences of the transmitting subject network node, used for matched filtering in step 210 ("node sequences").
  • Each node sequence may be, for instance, a pulse of a group of pulses;
  • applying object detection in step 220 may further comprise suppressing reflections from immobile objects, by applying a high-pass filter to the matched node resultant signal.
  • one or more of the physical parameters measured in step 220 may include information regarding one or more of the following:
  • the association in step 230 comprises looking for objects with sufficiently similar attributes, wherein the attributes may include one or more of the following:
  • the compounding of the physical parameter measurements in step 240 when relating to multiple measurements made essentially at the same time, may comprise one or more of the following:
  • the compounding of the physical parameter measurements in step 240 when relating to multiple measurements made at different times, may comprise one or more of the following: (a) Using any filter known in the art, e.g., a Kalman filter, to estimate the behavior of some of the object's attributes as a function of time, e.g., its location and dynamic properties; and
  • the object classification and/or recognition in step 250 may employ any method known in the art, e.g., neural networks, deep learning, hidden Markov models (HMM), and the like. Additionally or alternatively, one may employ one or more of the methods described herein in the section entitled "Object Classification”.
  • HMM hidden Markov models
  • vehicle monitoring may comprise ("imaging based vehicle monitoring”):
  • Step 310 At certain time increments, apply an inverse problem method to the received node resultant signal, to obtain target volume maps;
  • Step 320 Apply image processing to the target volume maps, to detect objects within them, and for each detected object, extract one or more physical attributes;
  • Step 330 If possible, associate detected objects in different volume maps, expected to correspond to the same physical object within the target volume, wherein the different volume maps relate to different times;
  • Step 340 For each association result of step 330, compound the physical attributes relating to the corresponding detected objects, in order to obtain additional and/or more precise information regarding the objects.
  • vehicle monitoring may further comprise one or more of the following (each of the following steps may be applied after any of steps 320, 330, or 340):
  • Step 350 For one or more detected objects, analyze the associated physical attributes (before or after compounding), to obtain object classification and/or recognition; and
  • Step 360 Discard detected objects whose classification and/or recognition outputs are irrelevant for vehicle monitoring.
  • the object detection in step 320 may be based on one or more of the following:
  • the physical attributes in step 320 may include one or more of the following:
  • the association in step 330 comprises looking for objects with sufficient similarity in one or more of the physical attributes.
  • the compounding of the physical attributes in step 340 comprises one or more of the following: (a) Using any filter known in the art, e.g., a Kalman filter, to estimate the behavior of some of the object's attributes as a function of time, e.g., its location and dynamic properties; and
  • the object classification and/or recognition in step 350 may employ any method known in the art, e.g., neural networks, deep learning, hidden Markov models (HMM), and the like. Additionally or alternatively, one may employ one or more of the methods described herein in the section entitled "Object Classification”.
  • HMM hidden Markov models
  • the outputs of vehicle monitoring, or certain functions thereof may be stored in a database for future analysis.
  • the outputs of vehicle monitoring, or certain functions of these outputs may be displayed to one or more users on one or more user units.
  • the outputs of vehicle monitoring may undergo one or more of the following processing:
  • Traffic analysis providing information regarding the distribution of vehicle location and velocity as a function of space and time. This information may be used, for instance, for planning optimal driving routes, or as a reference for urban development design;
  • Traffic analysis providing information regarding traffic accidents and/or traffic law violations. This information may be used, for example, by law enforcement officials;
  • a soon to be vacant parking spot can be characterized as a parking spot occupied by a vehicle that has not moved for more than a predefined time, and that currently shows vehicle motion; and (d) Parking analysis, providing information regarding illegally parked vehicles.
  • Such vehicles may be, for instance, parked in illegal locations, or exceeding the parking payment time swath. This information may be employed by parking officials.
  • the systems of the present invention may be integrated with additional sensors, providing supplementary information to the mapping units.
  • the additional sensors may include sensors traditionally employed in security and surveillance systems, such as motion sensors, photo-electric beams, shock detectors, glass break detectors, still and/or video cameras, which may be optic and/or electro-optic, other electro-optic sensors, radars, lidar systems, and/or sonar systems.
  • systems of the present invention may be integrated with other systems, to provide combined functionality.
  • a system of the present invention may be integrated with another type of obstacle detection system, e.g., based on image processing of information acquired by one of more video cameras.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Engineering & Computer Science (AREA)
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Abstract

La présente invention concerne un procédé d'acquisition d'informations concernant un terrain et/ou des objets dans un volume, ledit procédé comprenant : l'émission de signaux au cours du temps ("signaux de nœud") depuis un ou de plusieurs nœuds d'un réseau sans fil ("réseau sujet") ; la réception des signaux de nœud après qu'ils aient traversé un milieu ("signaux résultants de nœud") au moyen d'une ou plusieurs unités de réception ("récepteurs de signal de nœud") ; mesure d'un ou plusieurs attributs physiques ("attributs de signal") pour un ou plusieurs des signaux résultants de nœud, au moins l'un des attributs de signal étant d'au moins l'un des types suivants : (a) différence de temps entre l'émission de signal de nœud par le nœud de réseau sujet émetteur applicable et la réception de signal résultant de nœud par le récepteur de signal de nœud applicable ; (b) déphasage entre le signal de nœud émis et le signal résultant de nœud reçu ; (c) rapport de puissance entre le signal de nœud émis et le signal résultant de nœud reçu ; (d) différence de fréquence entre le signal résultant de nœud reçu et le signal de nœud émis (décalage Doppler) ; et/ou (e) direction depuis laquelle le signal résultant de nœud est arrivé, et/ou sa projection sur un ou plusieurs axes prédéfinis ; estimation de l'emplacement spatial en fonction du temps pour un ou plusieurs des nœuds de réseau sujet d'émission et/ou un ou plusieurs des récepteurs de signal de nœud ; et analyse d'un ou plusieurs des signaux résultants de nœud et/ou un ou plusieurs des attributs de signal pour extraire des informations concernant des objets le long des trajets du signal ("informations cartographiques").
PCT/IB2018/050280 2017-02-10 2018-01-17 Acquisition d'informations concernant un volume au moyen de réseaux sans fil WO2018146564A1 (fr)

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