CN112130118B - Ultra-wideband radar signal processing system and method based on SNN - Google Patents
Ultra-wideband radar signal processing system and method based on SNN Download PDFInfo
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Abstract
The invention discloses an SNN-based ultra-wideband radar signal processing system and a processing method, which relate to the technical field of brain-like artificial intelligence and comprise an IR-UWB sensor for detecting and receiving a reflected pulse signal, wherein an IR-UWB module for carrying out time-event coding on the reflected pulse signal is arranged in the IR-UWB sensor, the IR-UWB module inputs coded data into an SNN array module for nerve mimicry calculation, and sends the data obtained after the nerve mimicry calculation to a corresponding processor module for analysis, and the processor module feeds back result information to the SNN array module after the analysis is completed. The method integrates perception (radar pulse signal detection) and cognition (nerve mimicry calculation), so that the traditional radar signal processing and signal reconstruction process is avoided, and the full event-driven signal transmission and model can directly output results.
Description
Technical Field
The invention relates to the technical field of brain-like artificial intelligence, in particular to an SNN-based ultra-wideband radar signal processing system and an SNN-based ultra-wideband radar signal processing method.
Background
An impulse Radio-UI tra Wideband (IR-UWB), hereinafter abbreviated as ultra Wideband, is a short-range wireless communication technology. Unlike conventional wireless communication technology, ultra wideband does not employ carrier modulation, but rather directly employs pulses with very narrow time widths (nanoseconds or sub-nanoseconds), which are transferred directly or through buffers to the antenna, and typically the system does not require more complex circuitry such as local oscillators and up-conversion mixers.
The IR-UWB signal has the characteristics of extremely short pulse duration, extremely high time domain resolution, low complexity, low cost, strong multipath interference resistance, wide frequency spectrum range (up to several GHz or more), penetrability, low power consumption and the like, and can be widely applied to the applications of the Internet of things, wireless positioning, short distance measurement, low-cost radars and the like.
The radar sensor based on IR-UWB generally integrates a plurality of components such as a transmitting end, an amplifier end, a receiving end and the like, is consistent with other radar principles, and can be reflected and received by the receiving end under the condition of encountering an obstacle after the UWB radar sensor transmits UWB signals through the transmitting end. Compared with the traditional radar, the radar sensor based on the IR-UWB has the advantages of high time domain precision, small volume, low power consumption and low cost, and is widely used for non-contact sensors such as human body monitoring, gesture recognition, human body activity analysis, target monitoring, human-computer interaction and the like, and meanwhile, the radar sensor based on the IR-UWB can be used for nondestructive internal detection and the like due to the penetrability of an IR-UWB signal. Received signals acquired using IR-UWB radar sensors are typically processed using fast and slow time sampling systems, as shown in fig. 1. In the fast time dimension, storing L samples in a period of time after the sampling time begins in a digital memory to obtain L distance gates; in the slow time dimension, pulsed radar transmits not only one pulse, but in the form of a set of pulses, the dimension in which these axes of repetition lie being the slow time axis.
In view of the received signal form of IR-UWB, conventional IR-UWB radar sensors generally process binary images in the XY direction of three-dimensional echoes. The signal processing method and steps are shown in fig. 2, and after the signal is detected and received, the received radar echo signal is first preprocessed. Preprocessing includes clutter suppression, background noise removal, etc., and in general, preprocessing may employ direct subtractive averaging, singular value decomposition, moving object detection, etc. for clutter filtering. And then, extracting signal characteristics by adopting different methods according to application scenes and working requirements. The extraction method varies according to the application type, and in multi-target detection, principal component analysis (PCA, principal Component Analysis) is generally adopted; in gesture recognition, time-frequency feature extraction or a Range-Doppler (RD) algorithm is generally used. After the characteristics are extracted, the characteristics are analyzed by adopting a mode recognition or machine learning method to finally obtain a result. Traditional machine learning methods include Nearest Neighbor classifiers (K-Nearest Neighbor), decision tree classifiers, support Vector Machines (SVMs), and the like. With the increase of computational power, analysis methods employing convolutional neural networks are also widely used in the feature extraction of IR-UWB radar echoes.
With the rapid development of artificial intelligence, the method is widely applied to machine vision, and extends from image recognition classification to microwave vision represented by radar application, and the method of deep learning and Artificial Neural Network (ANN) is generally used for performing pattern recognition and classification on radar signals. The method is also used for IR-UWB radar sensor (infrared-ultra-wideband radar sensor), including Convolutional Neural Network (CNN), cyclic neural network (RNN), long-term short-term memory network (LSTM), support Vector Machine (SVM), etc.
In summary, artificial intelligence based on ANN has a great demand for hardware resources, which greatly increases the difficulty of deploying artificial intelligence algorithms in low power devices. However, as neuromorphic calculations began to rise, it became possible to implement low-power, high-efficiency artificial intelligence calculations (an order of magnitude improvement in energy efficiency) aimed at mimicking the human brain's way of operation. The present patent therefore proposes a new improvement over the prior art techniques described above, in particular in the processing of radar signals.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an ultra-wideband radar signal processing system and an ultra-wideband radar signal processing method based on SNN, which avoid the processes of traditional radar signal processing and signal reconstruction by integrating perception (radar pulse signal detection) and cognition (nerve mimicry calculation), and directly output results by using signal transmission and models driven by full events.
In order to achieve the above object, the present invention provides the following technical solutions:
the ultra-wideband radar signal processing system based on SNN comprises an IR-UWB sensor for detecting and receiving a reflected pulse signal, wherein an IR-UWB module for carrying out time-event coding on the reflected pulse signal is arranged in the IR-UWB sensor, the IR-UWB module inputs coded data into an SNN array module for nerve mimicry calculation, the data obtained after the nerve mimicry calculation is sent to a corresponding processor module for analysis, and the processor module feeds back result information to the SNN array module after the analysis is completed;
or, the system also comprises a neural network state machine which can interact with the SNN array module, wherein the neural network state machine adopts WTA rules, the number of the state machines in the neural network state machine is determined according to the number of the required classifications, and the neural network state machine is trained through a pulse time domain sequence recognition training algorithm.
The processing method of the SNN-based ultra-wideband radar signal processing system comprises the following steps:
the IR-UWB sensor detects and receives the reflected pulse signal;
the IR-UWB module in the step (2) carries out time-event coding on the reflected pulse signals received in the step (1) to obtain coded data;
the SNN array module receives the encoded data in the step (2), adds time domain expression into the encoded data through neuro-mimicry calculation to form a dynamic pulse sequence, and transmits the dynamic pulse sequence to the processor module for analysis;
and (4) feeding back analysis result information to the SNN array module after the processor module completes analysis so as to realize real-time updating of a network.
Preferably, the neuromorphic calculation in step (3) is specifically implemented by a pulsed neural network model.
Advantageous effects
The SNN-based ultra-wideband radar signal processing system and the SNN-based ultra-wideband radar signal processing method provided by the invention have the following advantages:
1. because of the sparsity of the pulse sequence and the event-driven characteristic, the acquisition, transmission and processing of a large amount of redundant data are omitted in the system, and the IR-UWB radar has low energy consumption, so that the system has extremely low power consumption.
The IR-UWB radar directly adopts pulses with very narrow time width (nanosecond or sub-nanosecond level) to modulate, and the time domain precision is extremely high, so that the IR-UWB radar has high real-time characteristic.
The SNN can process pulse data naturally, and for pulse sequences generated by the IR-UWB radar, the SNN can directly input pulse signals into a network without processing steps such as signal type conversion, feature extraction and the like.
In ir-UWB radar, the pulses are transferred to the antenna directly or through a buffer, and in general, the system does not need more complex circuitry, and the system has lower complexity and is suitable for low-cost deployment and application.
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 in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a received signal obtained by a conventional IR-UWB radar sensor;
FIG. 2 is a diagram of a conventional IR-UWB radar sensor signal processing method;
FIG. 3 is a schematic diagram of an ultra wideband radar signal processing system with a supervised learning impulse neural network for an SNN-based ultra wideband radar signal processing system according to the present invention;
FIG. 4 is a schematic diagram of an ultra wideband radar signal processing system of an unsupervised learning impulse neural network of the SNN-based ultra wideband radar signal processing system of the present invention;
FIG. 5 is a schematic diagram of ultra wideband radar signal processing of a supervised learning impulse neural network of an SNN-based ultra wideband radar signal processing system according to the present invention;
fig. 6 is an ultra wideband radar signal processing schematic diagram of an unsupervised learning impulse neural network of the SNN-based ultra wideband radar signal processing system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. 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.
The ultra-wideband radar signal processing system based on SNN comprises two forms of supervised learning and unsupervised learning, wherein the supervised learning form is shown in figure 3, and comprises an IR-UWB sensor for detecting and receiving a reflected pulse signal, an IR-UWB module for performing time-event coding on the reflected pulse signal is arranged in the IR-UWB sensor, the IR-UWB module inputs coded data into an SNN array module for performing nerve mimicry calculation, and sends the data obtained after the nerve mimicry calculation to a corresponding processor module for analysis, and the processor module feeds back result information to the SNN array module after the analysis is completed;
the non-supervision learning form also comprises a neural network state machine which can realize interaction with the SNN array module on the basis of supervision learning, wherein the neural network state machine adopts WTA rules, the number of the state machines in the neural network state machine is determined according to the number of the required classification, and the neural network state machine is trained through a pulse time domain sequence recognition training algorithm.
The processing method of the SNN-based ultra-wideband radar signal processing system comprises the following steps:
the IR-UWB sensor detects and receives the reflected pulse signal;
the IR-UWB module in the step (2) carries out time-event coding on the reflected pulse signals received in the step (1) to obtain coded data;
the SNN array module receives the encoded data in the step (2), adds time domain expression into the encoded data through neuro-mimicry calculation to form a dynamic pulse sequence, and transmits the dynamic pulse sequence to the processor module for analysis; the nerve mimicry calculation is realized by a pulse neural network model;
and (4) feeding back analysis result information to the SNN array module after the processor module completes analysis so as to realize real-time updating of a network.
Specifically, as shown in fig. 5, the algorithm flow of supervised learning is that the IR-UWB module performs time-event encoding according to the echo of the radar, and the encoded data may be directly input to perform neuro-mimicry calculation.
The process of the neuro-mimicry calculation is performed by using a pulse neural network model, and the calculation is mainly divided into the following two processes:
one is the calculation of the existing output, the pulse passes through each node of each layer of neural network and generates output, and finally the neural network generates a final output which is irrelevant to our target application and is the result of self-calculation;
secondly, according to the type of the output pulse sequence which we want to obtain, new weight is obtained through analysis and result output and calculation through neuro-mimicry, the process is Surrogate Propagation (agent propagation), and the result of the next self-calculation after learning is gradually similar to the target result;
the two processes are repeated, and the supervised identification and classification of the targets can be realized according to application scenes. In addition, the pulse neural network can be customized according to the application scene, and the larger the network is, the more pattern types can be identified, and more differentiated classifications can be accommodated. The model ensures that the identification object can be accurately identified at any position and any direction of the IR-UWB input matrix.
Specifically, taking gesture recognition under supervised learning as an example, firstly, according to the echo of a radar, an IR-UWB module carries out time-event coding, and the coded data can be directly input to carry out nerve mimicry calculation.
The SNN neural network for gesture recognition has a simple structure, and is only one input layer, one hidden layer and one output layer.
The process of the neuro-mimicry calculation is performed by using a pulse neural network model, and the calculation is mainly divided into the following two processes:
one is the calculation of the existing output, the pulse passes through each node of each layer of neural network and generates output, and finally the neural network generates a final output, and the quantity of the neurons of the output layer is determined by the action to be identified.
According to the type of the output pulse sequence which we want to obtain, for example, we can design different time sequence impulse response sequences according to different gestures, calculate the loss function through analysis and result output, calculate new weight through the loss function and response input and output, the process is Surrogate Gradient Learning rules (agent gradient learning rule), and the result of the next self-calculation after learning is gradually similar to the target result.
The two processes are repeated, the supervised identification and classification of the targets can be realized according to the application scene, and in the repeated process, the learning rate is gradually reduced, so that the model is ensured not to stay in the local optimal solution.
As shown in FIG. 6, the algorithm flow of the unsupervised learning is that firstly, according to the echo of the radar, the IR-UWB module carries out time-event coding, and the coded data can be directly input to carry out nerve mimicry calculation; the process of the neuro-mimicry calculation is performed by using a pulse neural network model, and the calculation is mainly divided into the following two processes:
one is the calculation of the existing output, the pulse passes through each node of each layer of neural network and generates output, and finally the neural network generates a final output which is irrelevant to our target application and is the result of self-calculation, and the output can enable the neural network to generate a fixed state;
secondly, after generating a plurality of neural network state machines according to the required classification, signals are input into the whole neural network state machine system at intervals, the process is called initialization of the neural network, after each time a signal is input, a control neuron restores the state of the whole neural network, and meanwhile, the same neural network state machine is ensured not to be continuously excited in two continuous samples (the signals required to be input are respectively in different types) through a special weight attenuation structure; when all state machines are excited at least once, after the weights of all the neural network state machines are updated once, the control neurons are removed, various random pulse samples are input to the neural network state machines, and the weight attenuation structures are removed by phase inversion in some modes, so that the related neural network state machines can be continuously excited (such as when continuous signal input of the same type is met), and the weights are continuously updated. The termination condition of the process is that none of the signals is tuned to be classified into another neural network state machine, the method being such that all of the same class of inputs excite a fixed neural network state;
the two processes are repeated, and the unsupervised identification and classification of the targets can be realized according to the application scene. Different from supervised learning, a self-supervised learning structure of a neural network state machine is added, the neural network state machine adopts a WTA (Winner-Take-All) rule, the number of the state machines in the neural network state machine is determined according to the number of required classification, and the neural network state machine is trained through a pulse time domain sequence recognition training algorithm, for example, if 5 gestures are required to be recognized, 5 neural network state machines are generated.
In summary, the invention integrates sensing (radar pulse signal detection) and cognition (nerve mimicry calculation), avoids the processes of traditional radar signal processing and signal reconstruction, and directly outputs results through signal transmission and model driven by full events, and under the framework of the system:
1. the relative bandwidth of-10 db of the signal generator is more than 20% of the central frequency, or the bandwidth of-10 db of the signal exceeds 500MHz, then the signal is UWB (Ultra Wide Band, UWB) technology, which is a wireless carrier communication technology, which does not adopt sinusoidal carriers, but utilizes nanosecond non-sinusoidal narrow pulse to transmit data, so that the UWB technology occupies a very wide frequency spectrum range. Wherein the definition of the relative bandwidth η is:
f in h For the upper bound of the signal spectrum, f l Is the lower bound of the signal spectrum.
2. After the echo signal is detected, directly generating an Event sequence (Event lndex), directly entering a nerve mimicry calculation unit without transformation, and performing intelligent processing by using a pulse neural network;
3. performing SNN using a neuro-mimicry architecture, including but not limited to general purpose computers and servers, GPUs, FPGAs, application specific integrated circuit chips, or novel brain-like computing devices and architectures including memristors, and the like;
4. SNN model training methods are applied to typical applications such as gesture recognition, heartbeat and breath recognition, human motion recognition and the like.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (2)
1. Ultra-wideband radar signal processing system based on SNN, its characterized in that: the device comprises an IR-UWB sensor for detecting and receiving a reflected pulse signal, wherein an IR-UWB module for carrying out time-event coding on the reflected pulse signal is arranged in the IR-UWB sensor, the IR-UWB module inputs coded data into an SNN array module for nerve mimicry calculation, the data obtained after the nerve mimicry calculation is sent to a corresponding processor module for analysis, and the processor module feeds back result information to the SNN array module after the analysis is completed;
or, the system also comprises a neural network state machine which can realize interaction with the SNN array module, wherein the neural network state machine adopts WTA rules, determines the number of the state machines in the neural network state machine according to the number of the required classifications, and trains the neural network state machine through a pulse time domain sequence recognition training algorithm;
the process of the neuro-mimicry calculation is performed by using a pulse neural network model, and the calculation is mainly divided into the following two processes:
firstly, the pulse passes through each node of each layer of neural network and generates output, and finally the neural network generates final output, and the quantity of the neurons of the output layer is determined by the action to be identified;
secondly, different time sequence impulse response sequences are designed according to different gestures, a loss function is calculated through analysis and result output, and new weights are calculated through the loss function and response input and output.
2. The method for processing the SNN-based ultra-wideband radar signal processing system according to claim 1, wherein: the method comprises the following steps:
the IR-UWB sensor detects and receives the reflected pulse signal;
the IR-UWB module in the step (2) carries out time-event coding on the reflected pulse signals received in the step (1) to obtain coded data;
the SNN array module receives the encoded data in the step (2), adds time domain expression into the encoded data through neuro-mimicry calculation to form a dynamic pulse sequence, and transmits the dynamic pulse sequence to the processor module for analysis;
and (4) feeding back analysis result information to the SNN array module after the processor module completes analysis so as to realize real-time updating of a network.
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