CN111093163B - Passive target classification method based on channel state information - Google Patents
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Abstract
The invention relates to a passive target classification method based on channel state information, which comprises the following steps in sequence: collecting data; preprocessing data; training a neural network; and (5) classifying results. The method has the advantages of low deployment cost, high classification accuracy, no need of manually extracting features and privacy protection; by using more stable channel state information as a base signal instead of the received signal strength which fluctuates greatly with time, for the height classification with fine granularity, the channel state information can better reflect the difference of the target height compared with the received signal strength indicating value.
Description
Technical Field
The invention relates to the technical field of passive sensing in a wireless network, in particular to a passive target classification method based on channel state information.
Background
The target classification has important application value in the fields of safety monitoring, emergency rescue, border patrol, intelligent interaction and the like. There are three methods widely used in the field of object classification, namely a video image-based method, a Synthetic Aperture Radar (SAR) -based method, and a sensor-based method. The most notable method is a video image and Synthetic Aperture Radar (SAR) based method. However, the cost of using either approach is relatively high, which hinders the possibility of deploying them for low-cost perception, and the use of video image-based approaches may carry the risk of privacy disclosure; the sensor-based method, which uses a mixture of various sensors such as acoustic, passive infrared, magnetic field and electrostatic sensors for object classification, can greatly reduce costs compared to the two aforementioned methods, however, the classification accuracy of the method needs to be further improved.
The passive sensing technology has no problems of high deployment cost and privacy disclosure. Currently, sensing methods based on wireless signals are receiving wide attention. The Passive sensing (DFP) senses the surrounding environment by using radio signals, senses the information of motion, position, height and the like of people by the influence of users on the radio signals, and can directly make up for the defects that a video image method has monitoring dead angles and monitoring cameras cannot be arranged in some places. Moreover, the technologies can be realized on common commercial Wi-Fi, so that the deployment complexity and the installation cost of detection devices such as video monitors and cameras and the labor cost of monitoring are directly reduced. Therefore, the passive sensing technology has great potential in the aspects of safety protection, smart home and the like. The theoretical basis for classification of objects using passive sensing is: the transmitted RF signal consists of several components and these components arrive at the receiving end from different paths forming a superposition. Since the RF signal can be easily blocked, reflected or scattered by different targets, each target will cause a different form of signal change. By identifying and interpreting these changes, the types of different objects can be detected and classified.
Most of the existing passive target classification systems adopt the received signal strength indication as a base signal, and professor Yangzhou of Qinghua university has pointed out that the measured values of the received signal strength indication at the same position and different moments under the same condition have great difference, which leads to the problem of insufficient classification precision caused by using the signal. Increasing the accuracy requires more wireless transceiver nodes to be deployed, which necessarily results in increased costs, and therefore creates a trade-off between classification accuracy and cost. In addition, most passive target classification systems adopt a method of manually extracting signal features, and find features which can easily distinguish different targets in a time domain, a frequency domain and the like, which can cause a problem of high cost.
Disclosure of Invention
The invention aims to provide a method for balancing classification precision and cost by using channel state information to replace a received signal strength indicator as a base signal; a passive object classification method based on channel state information reduces overhead by using a neural network model with autonomous learning data features instead of manually extracting features.
In order to achieve the purpose, the invention adopts the following technical scheme: a passive object classification method based on channel state information, the method comprising the sequential steps of:
(1) data collection:
(1a) deploying a pair of wireless signal transceivers in a monitoring area, fixing K positions for identifying and classifying in the monitoring area, and setting a packet sending rate of 100 HZ;
(1b) each target stands still at K positions for a period of time S respectively, and channel state information H of different targets is collected; the targets are persons standing with different heights and weights;
(2) data preprocessing:
(2a) for data collected by each target at each position, 4000 data packets of a middle stable state are taken, and channel state information comprises amplitude information and phase information;
(2b) carrying out denoising pretreatment on data by adopting continuous wavelet transform;
(3) training a neural network classification model: using 60% of the data after the step (2) as a training set, 20% as a verification set and 20% as a test set, and using the BP neural network to use the data of the training set for training the neural network to obtain a classification model;
(4) and (4) classification results: after the training of the classification model is finished, the data of the test set is sent into the classification model, and then the classification of the test target can be obtained.
In the step (1a), the wireless signal transceiver works in an ISM frequency band of 2.4GHZ, the wireless signal transceiver comprises a signal transmitter and a signal receiver, the wireless router is used as the signal transmitter, and a notebook computer equipped with a wireless network card is used as the signal receiver.
When the channel state information is collected in step (1b), the channel is divided into a plurality of subcarriers with different center frequencies by using the OFDM technique, and the number of the subcarriers is 30.
In the step (3), the method for obtaining the classification model by adopting the BP neural network training comprises the following steps:
the BP neural network comprises an input layer, a hidden layer and an output layer, the number of neurons of the input layer and the output layer is determined according to input training set data and classification requirements, the number of neurons of the hidden layer is two times or three times of the number of neurons of the input layer, an activation function between the layers is a sigmoid function, and the expression of the activation function is as follows:
the first derivative of the activation function can be derived:
the training steps are as follows:
firstly, a random initialization weight theta is a vector in a (0,1) range, and the dimensionality is determined by the number of front and rear nerve units;
secondly, forward propagation is achieved, i.e. for each input sample x(i)Calculating the activation value a layer by layer(l)Where l is the layer of the neural network;
hidden layer activation value: a is(2)＝f((-θ(1))Tx(i)) (3)
In the formula, a(2)For input sample x(i)An activation value after the hidden layer is activated by the function;
output layer activation value: a is(3)＝f((θ(2))Ta(2))＝hθ(x(i)) (4)
a(3)，hθ(x(i)) Are all input samples x(i)An activation value after the output layer has been activated by the activation function; theta is an expression form after vectorization, and the function represented by f (-) is a sigmoid function;
then, writing codes and calculating a cost function J (theta) according to the following formula;
in the above formula, m is the number of training samples, K is the number of neural units in the output layer, L is the total number of layers of the neural network, and SlThe number of the cells of the l layer is, and lambda is a regularization parameter;
the sample value of the kth neuron of the ith sample in the output layer;
then, the partial derivative of J (theta) is calculated by an error inverse propagation algorithm
Partial derivatives D calculated with error backpropagation using gradient detection comparisonsij lAnd the error between the calculated partial derivative numgard is numerically estimated:
|Dij l-numgrad|<ε,ε∈(0,0.01) (6)
finally, find θ that minimizes the cost function using fmicg function:
at this point, the training of the entire neural network is completed.
The wireless signal transceiver supports an IEEE 802.11a/b/g/n wireless protocol.
According to the technical scheme, the beneficial effects of the invention are as follows: the method has the advantages of low deployment cost, high classification accuracy, no need of manually extracting features and privacy protection; the passive method has the challenges of selecting a base signal and extracting the characteristics of easily distinguished targets, and the method improves the classification precision by using more stable channel state information to replace the received signal strength which fluctuates greatly along with time as the base signal and simultaneously using a neural network method with the characteristics of self-learning data to extract and classify the characteristics.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a diagram of height and weight information of a volunteer participating in an experiment according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of specific locations of wireless transceivers and test point settings in an experimental scene office environment;
FIG. 4 is a table of confusion matrices for target classification results in an office environment;
FIG. 5 is a comparison result of whether to perform denoising in an experimental environment and the classification accuracy of the method based on the received signal strength indication.
Detailed Description
As shown in fig. 1, a passive object classification method based on channel state information includes the following sequential steps:
(1) data collection:
(1a) deploying a pair of wireless signal transceivers in a monitoring area, fixing K positions for identifying and classifying in the monitoring area, and setting a packet sending rate of 100 HZ;
(1b) each target stands still at K positions for a period of time S respectively, and channel state information H of different targets is collected; the targets are persons standing with different heights and weights;
(2) data preprocessing:
(2a) for data collected by each target at each position, 4000 data packets of a middle stable state are taken, and channel state information comprises amplitude information and phase information;
(2b) carrying out denoising pretreatment on data by adopting continuous wavelet transform;
(3) training a neural network classification model: using 60% of the data after the step (2) as a training set, 20% as a verification set and 20% as a test set, and using the BP neural network to use the data of the training set for training the neural network to obtain a classification model;
(4) and (4) classification results: after the training of the classification model is finished, the test set data is sent into the classification model, and then the classification of the test target can be obtained.
In the step (1a), the wireless signal transceiver works in an ISM frequency band of 2.4GHZ, the wireless signal transceiver comprises a signal transmitter and a signal receiver, the wireless router is used as the signal transmitter, and a notebook computer equipped with a wireless network card is used as the signal receiver.
In the step (1b), when the channel state information is collected, the channel is divided into a plurality of subcarriers with different center frequencies by using the OFDM technique, and the number of the subcarriers is 30.
In the step (3), the method for obtaining the classification model by adopting the BP neural network training comprises the following steps:
the BP neural network comprises an input layer, a hidden layer and an output layer, the number of neurons of the input layer and the output layer is determined according to input training set data and classification requirements, the number of neurons of the hidden layer is two times or three times of the number of neurons of the input layer, an activation function between the layers is a sigmoid function, and the expression of the activation function is as follows:
the first derivative of the activation function can be derived:
the training steps are as follows:
firstly, a random initialization weight theta is a vector in a (0,1) range, and the dimensionality is determined by the number of front and rear nerve units;
secondly, forward propagation is achieved, i.e. for each input sample x(i)Calculating the activation value a layer by layer(l)Where l is the layer of the neural network;
hidden layer activation value: a is a(2)＝f((-θ(1))Tx(i)) (3)
In the formula (I), the compound is shown in the specification,a(2)for input sample x(i)An activation value after the hidden layer is activated by the function;
output layer activation value: a is(3)＝f((θ(2))Ta(2))＝hθ(x(i)) (4)
a(3)，hθ(x(i)) Are all input samples x(i)An activation value after the output layer has been activated by the activation function; theta is an expression form after vectorization, and the function represented by f (-) is a sigmoid function;
then, writing codes and calculating a cost function J (theta) according to the following formula;
in the above formula, m is the number of training samples, K is the number of neural units in the output layer, L is the total number of layers of the neural network, and SlIs the unit number of the l layer, and lambda is a regularization parameter;
the sample value of the kth neuron of the ith sample in the output layer;
then, the partial derivative of J (theta) is calculated by an error inverse propagation algorithm
Partial derivatives D calculated with error backpropagation using gradient detection comparisonsij lAnd the error between the calculated partial derivative numgard is numerically estimated:
|Dij l-numgrad|<ε,ε∈(0,0.01) (6)
finally, find θ that minimizes the cost function using fmicg function:
at this point, the training of the entire neural network is completed.
The whole training process is to obtain a classification model and find theta which minimizes the cost function.
The wireless signal transceiver supports an IEEE 802.11a/b/g/n wireless protocol.
Example one
Firstly, collecting data, namely collecting data at four positions of a hexagonal mark of experimenters with different heights and weights shown in figure 2 in an experimental scene shown in figure 3, wherein an experimental platform selects a router as a transmitting end and is 80cm away from the ground; and a notebook computer provided with an Inter 5300 network card and an open source tool CSI-Tools is used as a receiving end and is 50cm away from the ground. Connect to the designated router in an unencrypted manner and generate approximately 100 packets per second using ping-i 0.01 commands. Each experimenter fixes the position for 1 minute, approximately 6000 data packets can be collected, 4000 data packets in a middle stable state are taken, channel state information comprises amplitude and phase information, and original phase information is disordered and irregular, so that the amplitude is only extracted from the 4000 data packets. The experiment is divided into 4 height segments, namely 150-.
Secondly, preprocessing the data of different experimenters at different positions, namely denoising by adopting a wavelet transform method. After preprocessing, 60% of the 4000 points were used as training set, 20% as test set, and the remaining 20% as validation set.
FIG. 4 is a diagram of a neural network model with a network structure of 30-90-4 obtained by preprocessing collected data, labeling the data of experimenters with different height segments with 1-4 labels, and sending the data to neural network training and repeatedly training. Through experimental tests, the ratio of the classification times of the correct height to the total times of the tests is counted to obtain the confusion matrix of different height segments shown in the figure 4, and the average classification accuracy reaches 92%.
Fig. 5 compares the influence of 3 base signals of RSSI, CSI original noisy signal and CSI denoised signal on the high classification performance, and the 3 types of data are respectively used as the data sets of the neural network, and the result is shown in fig. 5. for the RSSI signal, training is repeated for 10 times, and the average classification accuracy of the neural network is 41.24%. For the CSI noisy signal, the average classification accuracy of the neural network is 83.90%, and similarly, for the CSI denoised signal, the average classification accuracy is 90.30%. The result shows that the adoption of the CSI as the base signal is superior to the RSSI, and the feasibility of denoising by adopting wavelet transformation is further proved.
In conclusion, the method has the advantages of low deployment cost, high classification accuracy, no need of manually extracting features and privacy protection; the passive method has the challenges of selecting a base signal and extracting the characteristics of easily distinguished targets, and the method improves the classification precision by using more stable channel state information to replace the received signal strength which fluctuates greatly along with time as the base signal and simultaneously using a neural network method with the characteristics of self-learning data to extract and classify the characteristics.
Claims (4)
1. A passive target classification method based on channel state information is characterized in that: the method comprises the following steps in sequence:
(1) data collection:
(1a) deploying a pair of wireless signal transceivers in a monitoring area, fixing K positions for identifying and classifying in the monitoring area, and setting a packet sending rate of 100 HZ;
(1b) each target stands still at K positions for a period of time S respectively, and channel state information H of different targets is collected; the targets are persons standing at different heights and weights;
(2) data preprocessing:
(2a) for data collected by each target at each position, 4000 data packets of a middle stable state are taken, and channel state information comprises amplitude information and phase information;
(2b) carrying out denoising pretreatment on data by adopting continuous wavelet transform;
(3) training a neural network classification model: using 60% of the data after the step (2) as a training set, 20% as a verification set and 20% as a test set, and using the BP neural network to use the data of the training set for training the neural network to obtain a classification model; dividing the height into 4 height segments, namely 150-159cm, 160-169cm, 170-179cm and 180-189cm, preprocessing the collected data, respectively marking the data of experimenters with different height segments with 1-4 labels, sending the data into a neural network for training, and obtaining a neural network model with a network structure of 30-90-4 through repeated training so as to realize the height classification of the target;
(4) and (4) classification results: after the training of the classification model is finished, the data of the test set is sent into the classification model to obtain the classification of the test target;
in the step (3), the method for obtaining the classification model by adopting the BP neural network training comprises the following steps:
the BP neural network comprises an input layer, a hidden layer and an output layer, the number of neurons of the input layer and the output layer is determined according to input training set data and classification requirements, the number of neurons of the hidden layer is two times or three times of the number of neurons of the input layer, an activation function between the layers is a sigmoid function, and the expression of the activation function is as follows:
the first derivative of the activation function can be found:
the training steps are as follows:
firstly, randomly initializing a weight theta to be a vector in a (0,1) range, wherein the dimensionality is determined by the number of front and rear nerve units;
secondly, forward propagation is achieved, i.e. for each input sample x(i)Calculating the activation value a layer by layer(l)Where l is the layer of the neural network;
hidden layer activation value: a is(2)＝f((-θ(1))Tx(i)) (3)
In the formula, a(2)For input samples x(i)An activation value after the hidden layer is activated by the function;
output layer activation value: a is(3)＝f((θ(2))Ta(2))＝hθ(x(i)) (4)
a(3)，hθ(x(i)) Are all input samples x(i)An activation value after the output layer has been activated by the activation function; theta is an expression form after vectorization, and the function represented by f (-) is a sigmoid function;
then, writing codes and calculating a cost function J (theta) according to the following formula;
in the above formula, m is the number of training samples, K is the number of neural units in the output layer, L is the total number of layers of the neural network, and SlThe number of the cells of the l layer is, and lambda is a regularization parameter;
the sample value of the kth neuron of the ith sample in the output layer;
then, the partial derivative of J (theta) is calculated by an error inverse propagation algorithm
Partial derivatives D calculated with error backpropagation using gradient detection comparisonsij lAnd the error between the calculated partial derivative numgard is numerically estimated:
|Dij l-numgrad|<ε,ε∈(0,0.01) (6)
finally, find θ that minimizes the cost function using fmicg function:
at this point, the training of the entire neural network is completed.
2. The passive object classification method based on channel state information according to claim 1, characterized in that: in the step (1a), the wireless signal transceiver works in an ISM frequency band of 2.4GHZ, the wireless signal transceiver comprises a signal transmitter and a signal receiver, the wireless router is used as the signal transmitter, and a notebook computer equipped with a wireless network card is used as the signal receiver.
3. The passive object classification method based on channel state information according to claim 1, characterized in that: when the channel state information is collected in step (1b), the channel is divided into a plurality of subcarriers with different center frequencies by using the OFDM technique, and the number of the subcarriers is 30.
4. The passive object classification method based on channel state information according to claim 1, characterized in that: the wireless signal transceiver supports an IEEE 802.11a/b/g/n wireless protocol.
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