CN119907096A - A wireless indoor positioning data processing method and system for multi-scenario applications - Google Patents
A wireless indoor positioning data processing method and system for multi-scenario applications Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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- 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
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- 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
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses a wireless indoor positioning data processing method and system for multi-scene application, wherein the method comprises the following steps of acquiring positioning points or fingerprint points and collecting signal intensity data received from wireless equipment; the method comprises the steps of analyzing and detecting signal intensity data by utilizing a wavelet algorithm, obtaining a wavelet coefficient diagram by utilizing the wavelet algorithm and determining the position of invalid data if the detection result has valid signal intensity data and the invalid data, filtering and reorganizing data, analyzing the reorganized signal intensity data by utilizing the wavelet algorithm and detecting whether abnormal data exists or not, and taking the reorganized signal intensity data as optimal positioning data if the abnormal data does not exist. The method solves the problem that the time variability of the signals affects the positioning result based on a multiple wavelet algorithm, and has the advantages of high precision, easiness in implementation and the like.
Description
Technical Field
The invention relates to the technical field of indoor positioning, in particular to a wireless indoor positioning data processing method and system.
Background
With the continuous development of computer technology and network technology, the demand for location-based information services is increasingly being studied and applied. In particular, the areas of large shops, exhibition halls and the like are large, and a complete indoor positioning system is required for navigation, positioning and other services.
The current GPS technology can meet the requirements of outdoor positioning and the like, but cannot be well applied to indoor environments because GPS signals are shielded by indoor walls. The current indoor positioning technology is mainly based on WLAN, bluetooth, RFID, zigBee and the like. Positioning is completed mainly through measuring distance, matching fingerprint information and other methods. The WLAN technology and the fingerprint information matching method are widely applied due to low equipment cost and high positioning accuracy.
The fingerprint positioning method based on the WLAN needs to collect more fingerprint point wireless signal data information, however, due to the influence of factors such as indoor environment transformation, personnel walking, instability of equipment and the like, the received signal strength has larger time variability, the wireless signal characteristics with larger errors can bring larger positioning errors, and the popularization of the current WLAN fingerprint positioning is problematic.
Therefore, how to solve the influence caused by signal time variability becomes a great technical problem of popularization of the current WLAN positioning technology.
Disclosure of Invention
In view of the above, the invention provides a wireless indoor positioning data processing method and system for multi-scene application, which solve the problem that the time-varying property of the current signal affects the positioning accuracy.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, a method for processing wireless indoor positioning data for multi-scene application includes the following steps:
S1, acquiring a positioning point or a fingerprint point, and acquiring signal strength data received from wireless equipment;
s2, analyzing and detecting the signal intensity data by utilizing a wavelet algorithm;
s3, if the detection result has effective signal intensity data and invalid data, a wavelet coefficient diagram is obtained by utilizing a wavelet algorithm, the position of the invalid data is determined, and data filtering and data recombination are carried out;
s4, analyzing the recombined signal intensity data by utilizing a wavelet algorithm and detecting whether abnormal data exist or not;
S5, if no abnormal data exist, the recombined signal strength data are used as optimal positioning data.
S6, if abnormal data exist, analyzing the abnormal data, correcting the abnormal data based on the reliability of the front and rear data, and taking the corrected signal strength data as optimal positioning data.
Further, in step S1, the signal intensity data is { S 1,s2,...,sN } sequence, wherein N is not less than 2.
Further, step S2, analyzing and detecting the signal intensity data by utilizing a wavelet algorithm, specifically comprises the following steps:
performing wavelet decomposition on the signal intensity data by utilizing a wavelet algorithm to obtain wavelet coefficients of all scales and frequencies;
a threshold range of wavelet coefficients is generated using a graph-based method, and valid data and invalid data are detected based on wavelet coefficients of the threshold range.
Further, the wavelet algorithm is utilized to carry out wavelet decomposition on the signal intensity data to obtain wavelet coefficients of various scales and frequencies, and the method specifically comprises the following steps:
Taking { s 1,s2,...,sN } as an input signal sequence of a wavelet algorithm, and taking data as an initial approximation coefficient a 0 [ i ]:
a0[i]＝si,i=0,1,...,N
Applying a low-pass filter h [ i ] and a high-pass filter g [ i ] layer by layer from the 0 th layer, and performing downsampling to decompose the low-pass filter h [ i ] and the high-pass filter g [ i ] into approximation coefficients and detail coefficients of a plurality of scales;
obtaining an approximation coefficient a j [ i ] and a detail coefficient c j [ i ] of the j-th layer:
wherein j represents the decomposition layer number, k represents the intermediate integer variable, and the value is k epsilon [1, N ].
Further, the method for generating the threshold range of the wavelet coefficient by using the graph base method and detecting the effective data and the ineffective data based on the wavelet coefficient of the threshold range comprises the following steps:
sequencing a detail wavelet coefficient set { c 1,c2,...,cN } of each layer according to ascending order to determine a median value Med, and calculating a quartile range IQR, IQR=Q3-Q1, wherein Q1 represents a first quartile and Q3 represents a third quartile;
Obtaining an upper limit c H =q3+1.5×iqr of a wavelet coefficient threshold range, and a lower limit c L =q3-1.5×iqr of the wavelet coefficient threshold range;
If it is Then the signal s i corresponding to c i is invalid signal strength data;
If c i∈[cL,cH ], (i=1, 2,..n), then the signal s i corresponding to c i is valid signal strength data.
Further, in step S3, a wavelet coefficient diagram is obtained by utilizing a wavelet algorithm, and the position of the invalid data is determined, and data filtering and data recombination are performed, which specifically comprises:
constructing a wavelet coefficient diagram by utilizing a wavelet algorithm;
Searching and removing position segment data with larger fluctuation of wavelet coefficients in the wavelet coefficient diagram;
and reorganizing the rest position segment data.
Further, step S4, analyzing the recombined signal intensity data by using a wavelet algorithm and detecting whether abnormal data exist, specifically comprises the following steps:
performing wavelet decomposition on the recombined signal intensity data by utilizing a wavelet algorithm to obtain a recombined wavelet coefficient diagram;
and searching whether a drift position exists or not, and taking data corresponding to the drift position as abnormal data.
Further, in step S6, the reliability of the abnormal data based on the front and rear data is analyzed and corrected, and the corrected signal intensity data is used as the optimal positioning data, which specifically includes:
And analyzing the abnormal data, and outputting a signal strength data segment with stronger signal strength as optimal positioning data based on the reliability of the front data and the back data.
In a second aspect, a wireless indoor positioning data processing system for multi-scenario applications includes the following modules:
The data acquisition module is used for acquiring positioning points or fingerprint points and acquiring signal intensity data received from the wireless equipment;
the analysis and detection module is used for analyzing and detecting the signal intensity data by utilizing a wavelet algorithm;
the data processing module is used for obtaining a wavelet coefficient diagram by utilizing a wavelet algorithm and determining the position of invalid data if the detection result has valid signal intensity data and invalid data, and carrying out data filtering and data recombination;
The data anomaly detection module is used for analyzing the recombined signal intensity data by utilizing a wavelet algorithm and detecting whether the anomaly data exist;
The data output module is used for taking the recombined signal strength data as optimal positioning data if no abnormal data exists;
And if abnormal data exist, analyzing the abnormal data, correcting the abnormal data based on the reliability of the front and rear data, and taking the corrected signal strength data as optimal positioning data.
Compared with the prior art, the invention discloses a wireless indoor positioning data processing method and system for multi-scene application, which are used for removing invalid data and abnormal data by collecting signal intensity data and performing wavelet analysis for a plurality of times, solving the problem of data drift, outputting stable signal intensity data and improving positioning accuracy.
The invention is mainly oriented to various scene problems in the indoor in the wireless indoor positioning method based on the fingerprint method, but the traditional method can not solve the problems. The problem that the accuracy of a positioning result is affected by environmental change, personnel walking and other factors in the traditional wireless indoor positioning method based on the fingerprint method is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a wireless indoor positioning data processing method for multi-scenario application according to an embodiment of the present invention.
Fig. 2 is a graph of signal strength data after wavelet analysis according to an embodiment of the present invention.
Fig. 3 is a wavelet coefficient diagram after wavelet analysis according to an embodiment of the present invention.
Fig. 4 is a signal strength data diagram after removing invalid data according to an embodiment of the present invention.
Fig. 5 is a second wavelet coefficient diagram of the data drift phenomenon according to an embodiment of the present invention.
Fig. 6 is a second wavelet coefficient diagram without data drift according to an embodiment of the present invention.
Fig. 7 is a schematic view illustrating a position of a door in an open state according to an embodiment of the present invention.
Fig. 8 is a schematic view illustrating a position of a door in a closed state according to an embodiment of the present invention.
Fig. 9 is a graph of signal intensity data before and after a gate state change according to an embodiment of the present invention.
Fig. 10 is a schematic diagram of a wireless indoor positioning data processing system for multi-scenario application according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The embodiment of the invention discloses a wireless indoor positioning data processing method for multi-scene application, which comprises the following steps of S1-S6:
S1, acquiring a positioning point or a fingerprint point, and acquiring signal strength data received from wireless equipment;
the method comprises the steps of collecting received signal strength data, namely RSS data, of a positioning point or a fingerprint point from a certain AP (namely wireless WiFi equipment), wherein the fingerprint point data mainly refers to the step of collecting the received signal strength data at a fixed position in an offline (off-line) stage.
In this embodiment, the data set continuously collected by the locating point or the fingerprint point from a certain AP (i.e. the wireless WiFi device) is { s 1,s2,...,sN }, wherein s i, (i=1, 2,., N), n.gtoreq.2, representing the i-th RSS value collected. Due to the reasons of unstable equipment, environmental change, personnel shielding and the like in the acquisition process, data abnormal values or data drifting phenomena possibly exist in the data set, so that large errors exist in the acquired data values.
S2, analyzing and detecting the signal intensity data by utilizing a wavelet algorithm;
In this embodiment, the data set { s 1,s2,...,sN } is subjected to wavelet decomposition to obtain wavelet coefficients of various scales and frequencies, a threshold range of the wavelet coefficients is generated by using a graph base method, and the data corresponding to the wavelet coefficients exceeding the threshold range are considered as invalid RSS signal data;
the wavelet algorithm is used for analyzing the signal set sequence { s 1,s2,...,sN }, wherein { s 1,s2,...,sN } is an input signal sequence, and the initial approximation coefficient a 0 [ i ] is the signal itself:
a0[i]＝si,i=0,1,...,N
Layer-by-layer decomposition, namely, starting from layer 0, applying a low-pass filter h [ i ] and a high-pass filter g [ i ] layer by layer, and downsampling the result to obtain an approximation coefficient a j [ i ] and a detail coefficient c j [ i ] of the j-th layer:
wherein j represents the number of decomposition layers and k represents.
Repeatedly decomposing to obtain the required number of decomposing layers.
The final signal may be decomposed into detail coefficients of multiple scales, which may reflect local variations of the signal or detail information.
The present embodiment analyzes the j-layer detail wavelet coefficient set, and for the first-layer detail wavelet coefficient set { c 1,c2,...,cN }, the wavelet coefficient threshold range [ c L,cH ] can be obtained by using a graph-based method. The specific process of the graph-based method is as follows:
The detail wavelet coefficient set { c 1,c2,...,cN } is ordered in ascending order to determine the median value Med, the first quartile Q1 is calculated to be the value at the 25% position, and the third quartile Q3 is calculated to be the value at the 75% position. The quartile range IQR is calculated, iqr=q3-Q1. The upper limit c H = q3+1.5 x iqr in the wavelet coefficient threshold range and the lower limit c L = q3-1.5 x iqr in the wavelet coefficient threshold range.
If it isThe signal s i corresponding to c i is invalid RSS signal data.
If c i∈[cL,cH ], (i=1, 2,..n), then the signal s i corresponding to c i is valid signal strength data.
S3, if the detection result has effective signal intensity data and invalid data, a wavelet coefficient diagram is obtained by utilizing a wavelet algorithm, the position of the invalid data is determined, and data filtering and data recombination are carried out;
if the detection result does not have effective signal intensity data, repeating the step S1, acquiring the data again to obtain a new data set, and sequentially executing the steps S1-S3;
if the detection result has valid signal strength data and no invalid data, the signal strength data is used as optimal positioning data;
In this embodiment, referring to fig. 2, a wavelet coefficient map is constructed based on the wavelet coefficients obtained in step S2, and referring to fig. 3, the wavelet coefficients from the 13 th time of data acquisition to the 16 th time of data acquisition are large, so that RSS data corresponding to the large wavelet coefficients can be regarded as invalid data values by the graph-based method.
The abnormal data values are filtered and recombined to obtain a new data sequence, in this embodiment, 26 data points remain in the new data sequence, and the new data sequence is plotted with reference to fig. 4, and the obtained new data sequence cannot be used as stable positioning data due to the drift phenomenon.
S4, analyzing the recombined signal intensity data by utilizing a wavelet algorithm and detecting whether abnormal data exist or not;
in this embodiment, the new data sequence still has outliers, and a second wavelet coefficient map can be obtained by wavelet analysis, and this wavelet analysis is not used to remove outliers, but is used to determine the drift position.
If there is a drift phenomenon, a second wavelet coefficient map showing the existence of a data drift phenomenon is shown in fig. 5, and if there is no drift phenomenon, a second wavelet coefficient map showing the existence of no data drift phenomenon is shown in fig. 6.
S5, if no abnormal data exist, the recombined signal strength data are used as optimal positioning data;
s6, if abnormal data exist, analyzing the abnormal data, correcting the abnormal data based on the reliability of the front and rear data, and taking the corrected signal strength data as optimal positioning data.
According to the embodiment, the drift position is determined according to the abnormal data position, the reliability of the data before and after analysis is generally output by taking the data segment with stronger signal strength as a signal, and the data segment is the optimal positioning data.
The method solves the problem that the accuracy of the positioning result is affected by environmental change, personnel walking and other factors in the traditional wireless indoor positioning method based on the fingerprint method. For the RSS sequence with drift phenomenon, the wavelet analysis can analyze the invalid data position and delete the invalid data position, find the drift position according to the abnormal data and output stable RSS signal data.
The method is mainly oriented to various scene problems in the indoor in the wireless indoor positioning method based on the fingerprint method, but the traditional method cannot solve the problems. By using the method, the indoor positioning error can be reduced by about 50% in a complex environment of personnel, and the engineering applicability of the wireless indoor positioning method is improved.
Referring to fig. 7, a user a collects a wireless signal from an AP1 during positioning, a person B is just on the door side, and the door is in an open state. Then, the gate is closed, as shown in fig. 8, the probability that the wireless signal intensity data collected by the user a from the AP1 in the positioning process is abnormal and drifting occurs, as shown in fig. 9, the problem cannot be well solved by adopting the conventional filtering algorithm, and the accurate positioning of the a can be obtained by adopting the method of the invention to delete the invalid signal and analyze the abnormal signal.
Example two
The embodiment of the invention discloses a wireless indoor positioning data processing system for multi-scene application, which is shown by referring to FIG. 10 and comprises the following modules:
The data acquisition module is used for acquiring positioning points or fingerprint points and acquiring signal intensity data received from the wireless equipment;
the analysis and detection module is used for analyzing and detecting the signal intensity data by utilizing a wavelet algorithm;
the data processing module is used for obtaining a wavelet coefficient diagram by utilizing a wavelet algorithm and determining the position of invalid data if the detection result has valid signal intensity data and invalid data, and carrying out data filtering and data recombination;
The data anomaly detection module is used for analyzing the recombined signal intensity data by utilizing a wavelet algorithm and detecting whether the anomaly data exist;
The data output module is used for taking the recombined signal strength data as optimal positioning data if no abnormal data exists;
And if abnormal data exist, analyzing the abnormal data, correcting the abnormal data based on the reliability of the front and rear data, and taking the corrected signal strength data as optimal positioning data.
The system can solve the problem that the positioning result is influenced by the time variability of signals generated by environmental change, human body shielding and other reasons, and has the advantages of high precision, easiness in implementation and the like.
The system can be used for tracking the positions of personnel or articles in intelligent buildings and intelligent families, improves management efficiency, provides navigation service for customers in large markets or supermarkets to help the customers to find the articles quickly, and is an effective tool for improving space perception capability and optimizing user experience in various scenes in transportation hubs such as airports and stations in industrial manufacturing and logistics storage.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The wireless indoor positioning data processing method for the multi-scene application is characterized by comprising the following steps of:
S1, acquiring a positioning point or a fingerprint point, and acquiring signal strength data received from wireless equipment;
s2, analyzing and detecting the signal intensity data by utilizing a wavelet algorithm;
s3, if the detection result has effective signal intensity data and invalid data, a wavelet coefficient diagram is obtained by utilizing a wavelet algorithm, the position of the invalid data is determined, and data filtering and data recombination are carried out;
s4, analyzing the recombined signal intensity data by utilizing a wavelet algorithm and detecting whether abnormal data exist or not;
S5, if no abnormal data exist, the recombined signal strength data are used as optimal positioning data.
2. The method for processing wireless indoor positioning data for a multi-scenario application of claim 1, further comprising:
s6, if abnormal data exist, analyzing the abnormal data, correcting the abnormal data based on the reliability of the front and rear data, and taking the corrected signal strength data as optimal positioning data.
3. The method for processing wireless indoor positioning data for multi-scene application according to claim 1, wherein in step S1, the signal intensity data is { S 1,s2,...,sN } sequence, wherein N is greater than or equal to 2.
4. The method for processing wireless indoor positioning data for multi-scene application according to claim 3, wherein step S2 comprises analyzing and detecting the signal intensity data by wavelet algorithm, and specifically comprises:
performing wavelet decomposition on the signal intensity data by utilizing a wavelet algorithm to obtain wavelet coefficients of all scales and frequencies;
a threshold range of wavelet coefficients is generated using a graph-based method, and valid data and invalid data are detected based on wavelet coefficients of the threshold range.
5. The method for processing wireless indoor positioning data for multi-scene application of claim 4, wherein the wavelet coefficients of each scale and frequency are obtained by performing wavelet decomposition on the signal strength data by using a wavelet algorithm, comprising the following steps:
Taking { s 1,s2,...,sN } as an input signal sequence of a wavelet algorithm, and taking data as an initial approximation coefficient a 0 [ i ]:
a0[i]＝si,i=0,1,...,N
Applying a low-pass filter h [ i ] and a high-pass filter g [ i ] layer by layer from the 0 th layer, and performing downsampling to decompose the low-pass filter h [ i ] and the high-pass filter g [ i ] into approximation coefficients and detail coefficients of a plurality of scales;
obtaining an approximation coefficient a j [ i ] and a detail coefficient c j [ i ] of the j-th layer:
wherein j represents the decomposition layer number, k represents the intermediate integer variable, and the value is k epsilon [1, N ].
6. The method for processing wireless indoor positioning data for multi-scene application according to claim 5, wherein the method for generating a threshold range of wavelet coefficients by using a graph base method and detecting valid data and invalid data based on the wavelet coefficients of the threshold range comprises the steps of:
sequencing a detail wavelet coefficient set { c 1,c2,...,cN } of each layer according to ascending order to determine a median value Med, and calculating a quartile range IQR, IQR=Q3-Q1, wherein Q1 represents a first quartile and Q3 represents a third quartile;
Obtaining an upper limit c H =q3+1.5×iqr of a wavelet coefficient threshold range, and a lower limit c L =q3-1.5×iqr of the wavelet coefficient threshold range;
If it is Then the signal s i corresponding to c i is invalid signal strength data;
If c i∈[cL,cH ], (i=1, 2,..n), then the signal s i corresponding to c i is valid signal strength data.
7. The method for processing wireless indoor positioning data for multi-scene application as claimed in claim 6, wherein in step S3, a wavelet coefficient diagram is obtained by utilizing a wavelet algorithm, and positions of the invalid data are determined for data filtering and data reorganization, and the method specifically comprises:
constructing a wavelet coefficient diagram by utilizing a wavelet algorithm;
Searching and removing position segment data with larger fluctuation of wavelet coefficients in the wavelet coefficient diagram;
and reorganizing the rest position segment data.
8. The method for processing wireless indoor positioning data for multi-scene application of claim 7, wherein the step S4 comprises analyzing the recombined signal intensity data by wavelet algorithm and detecting whether abnormal data exist, and the method specifically comprises:
performing wavelet decomposition on the recombined signal intensity data by utilizing a wavelet algorithm to obtain a recombined wavelet coefficient diagram;
and searching whether a drift position exists or not, and taking data corresponding to the drift position as abnormal data.
9. The method for processing wireless indoor positioning data for multi-scene application according to claim 2, wherein in step S6, the abnormal data is analyzed and corrected based on the reliability of the front and rear data, and the corrected signal strength data is used as the optimal positioning data, and the method specifically comprises:
And analyzing the abnormal data, and outputting a signal strength data segment with stronger signal strength as optimal positioning data based on the reliability of the front data and the back data.
10. A wireless indoor positioning data processing system for multi-scene applications, comprising the following modules:
The data acquisition module is used for acquiring positioning points or fingerprint points and acquiring signal intensity data received from the wireless equipment;
the analysis and detection module is used for analyzing and detecting the signal intensity data by utilizing a wavelet algorithm;
the data processing module is used for obtaining a wavelet coefficient diagram by utilizing a wavelet algorithm and determining the position of invalid data if the detection result has valid signal intensity data and invalid data, and carrying out data filtering and data recombination;
The data anomaly detection module is used for analyzing the recombined signal intensity data by utilizing a wavelet algorithm and detecting whether the anomaly data exist;
The data output module is used for taking the recombined signal strength data as optimal positioning data if no abnormal data exists;
And if abnormal data exist, analyzing the abnormal data, correcting the abnormal data based on the reliability of the front and rear data, and taking the corrected signal strength data as optimal positioning data.
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