CN113376659A - GNSS (Global navigation satellite System) generated deception jamming detection method based on BP (Back propagation) neural network - Google Patents

GNSS (Global navigation satellite System) generated deception jamming detection method based on BP (Back propagation) neural network Download PDF

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CN113376659A
CN113376659A CN202110658870.8A CN202110658870A CN113376659A CN 113376659 A CN113376659 A CN 113376659A CN 202110658870 A CN202110658870 A CN 202110658870A CN 113376659 A CN113376659 A CN 113376659A
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祝雪芬
华腾
杨帆
汤新华
陈熙源
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Abstract

The invention discloses a GNSS (global navigation satellite system) generated deception detection method based on a BP (back propagation) neural network. The process of the BP neural network detection method comprises the following steps: firstly, performing data preprocessing on a GNSS signal, extracting a characteristic vector, and marking a signal which is not subjected to the generative deception jamming effect and a signal which is subjected to the generative deception jamming effect; dividing the sample into a training sample set and a testing sample set, and putting the training sample set into a BP neural network model for learning to obtain a model after learning; putting the test samples into a trained BP neural network model, and automatically classifying the test samples; when a new feature vector enters the classification model, the model will automatically determine whether a generative deception jamming signal is present. The method can quickly and automatically judge whether the generated deception jamming signal exists or not, and has high accuracy and efficiency.

Description

GNSS (Global navigation satellite System) generated deception jamming detection method based on BP (Back propagation) neural network
Technical Field
The invention relates to the field of GNSS satellite signals, in particular to a GNSS generated deception jamming detection method based on a BP neural network.
Background
Civil systems of Global Navigation Satellite Systems (GNSS) are vulnerable to spoofing interference due to the open signal structure. Deceptive jamming poses a huge threat to the security of GNSS services. Therefore, in order to prevent the navigation system from being subjected to the security threat of deception jamming, the detection of deception jamming signals has great significance for the normal operation and safe use of the navigation system.
The traditional detection method is limited by a model or needs additional hardware equipment, and the detection system is complex and has single detection parameter, so that deception components in satellite signals cannot be accurately reflected.
Disclosure of Invention
In order to solve the problems, the invention provides a GNSS generated deception jamming detection method based on a BP neural network, when a deception signal occurs, the generated deception signal is detected by comprehensively considering the characteristic quantity of an intermediate frequency signal and combining the BP neural network method. The method designs a pure software deception signal identification system, realizes intelligent identification of deception interference, avoids dependence of additional hardware equipment, and improves efficiency and accuracy.
The invention provides a GNSS generated deception jamming detection method based on a BP neural network, which comprises the following specific steps:
(1) preprocessing the intermediate frequency signal of the GNSS to obtain a feature vector used as the input quantity of the neural network, wherein the feature vector comprises an improved Ratio moving average value, an improved Ratio moving variance, an improved Delta moving average value, an improved Delta moving variance, an early-late code phase difference, a carrier-to-noise Ratio moving variance and a receiver clock difference change rate;
the step (1) specifically comprises the following steps:
(1.1) preprocessing the GNSS intermediate frequency signal, and inputting an original intermediate frequency signal;
(1.2) calculating to obtain an improved Ratio moving average value and an improved Ratio moving variance;
wherein, the expressions of the improved Ratio moving average value and the improved Ratio moving variance are as follows:
Figure BDA0003114336910000011
Figure BDA0003114336910000012
in the formula, Ratio ma and Ratio mv refer to an improved Ratio moving average and an improved Ratio moving variance, i denotes a sample number, w denotes a sliding window length, k denotes a sliding interval, n denotes a sliding window number, R denotes a sliding window numberd，improved(i) The expression of (a) is:
Figure BDA0003114336910000021
Figure BDA0003114336910000022
Figure BDA0003114336910000023
Figure BDA0003114336910000024
wherein, Ie，d(n)、Il，d(n) and Ip(n) the lead, lag and instantaneous outputs of the in-phase branch of the n-th coherent integrate-and-accumulate correlator, Qe，d(n)、Ql，d(n) and Qp(n) the lead, lag and instantaneous outputs of the quadrature arms of the correlator within the nth coherent integration, respectively;
(1.3) calculating to obtain an improved Delta moving mean value and an improved Delta moving variance;
the expressions of the improved Delta moving mean value and the improved Delta moving variance are as follows:
Figure BDA0003114336910000025
Figure BDA0003114336910000026
in the formula, DeltaMA and DeltaMV refer to improved Delta moving mean and improved Delta moving variance, Deltad，improved(i) The expression of (a) is:
Figure BDA0003114336910000027
(1.4) calculating to obtain an early-late code phase difference ELP;
Figure BDA0003114336910000028
wherein, Ie(n)、Qe(n)、Il(n) and Ql(n) the leading and lagging outputs of the I/Q branch correlator in the nth coherent integration respectively;
(1.5) calculating to obtain carrier-to-noise ratio mobile variance C/N0(i)MV；
Figure BDA0003114336910000029
(1.6) calculating to obtain the change rate of the receiver clock error;
Figure BDA00031143369100000210
(1.7) taking the improved Ratio moving average, the improved Ratio moving variance, the improved Delta moving average, the improved Delta moving variance, the early-late code phase difference, the carrier-to-noise Ratio moving variance and the receiver clock error change rate as an eigenvector x, wherein the sample eigenvector is as follows:
Figure BDA0003114336910000031
(1.8) preprocessing the data further comprises missing value processing, deviation calculation, taking the mean value of all observed satellite characteristic values, data standardization and principal component analysis;
(2) marking the signals at each moment according to the known cheating application moment, wherein the moment point without the cheating signal is marked as 0, and the moment point with the cheating signal is marked as 1;
(3) constructing a three-layer BP neural network, and randomly extracting 70% of data of a GNSS signal sample for training and 30% of data for testing;
(4) after passing the test, the model can be used to detect whether a spoofing disturbance has occurred.
As a further improvement of the invention, the step (3) specifically comprises the following steps:
(3.1) constructing a three-layer neural network model;
the specific three-layer model is an input layer model, a hidden layer model and an output layer model, the criterion function adopts a mean square error, and the expression is as follows:
Figure BDA0003114336910000032
wherein E iskWhich is indicative of an estimation error that is,
Figure BDA0003114336910000033
and
Figure BDA0003114336910000034
respectively an estimated value and a true value output by the neural network,
the optimization method selected by the neural network is a random gradient descent method, namely, parameters are updated from the direction of the negative gradient of the target, and for a given learning rate eta, the method comprises the following steps:
Figure BDA0003114336910000035
the number of selected input layer nodes is 7, the number of selected output layer nodes is 1, and the number of selected hidden layer nodes is 10;
the selected activation functions include, but are not limited to, a sigmod function, a tanh function, a relu function, and a leakyRelu function;
and (3.2) randomly drawing 70% of signal samples as training samples and 30% of signal samples as testing samples, and putting the samples into a three-layer neural network model for training and testing.
Compared with the prior art, the invention has the following remarkable advantages:
the invention provides a GNSS (global navigation satellite system) generated deception detection method based on a BP (back propagation) neural network. The method comprises the steps of firstly extracting intermediate frequency signals of a GNSS satellite, preprocessing the intermediate frequency signals and obtaining eigenvectors, wherein the eigenvectors comprise an improved Ratio moving average value, an improved Ratio moving variance, an improved Delta moving average value, an improved Delta moving variance, an early-late code phase difference, a carrier-to-noise Ratio moving variance and a receiver clock difference change rate. And then marking the intermediate frequency signals of each time point, marking the signal before the cheating signal is applied as 0 and marking the signal after the cheating signal is applied as 1 according to the known cheating signal application time. A three-layer neural network model is constructed, and 70% of signals are randomly drawn to be used for training the model, and 30% of signals are used for testing the model. The model passing the test can be used to detect GNSS generated spoofed signals. Compared with the traditional method, the method has the advantages that the intelligent identification of the deception jamming is realized, the dependence of additional hardware equipment is avoided, and the efficiency and the accuracy are improved.
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FIG. 1 is a schematic flow diagram of one embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a GNSS generated deception jamming detection method based on a BP neural network. The method designs a pure software deception signal identification system, realizes intelligent identification of deception interference, avoids dependence of additional hardware equipment, and improves efficiency and accuracy.
As a specific embodiment of the present invention, the present invention provides a GNSS generative spoofing detection method based on a BP neural network, a flowchart is shown in fig. 1, and the specific steps are as follows;
the method comprises the following steps:
preprocessing a GNSS intermediate frequency signal to obtain a feature vector used as a neural network input quantity, wherein the feature vector comprises an improved Ratio moving average value, an improved Ratio moving variance, an improved Delta moving average value, an improved Delta moving variance, an early-late code phase difference, a carrier-to-noise Ratio moving variance and a receiver clock difference change rate;
the method specifically comprises the following steps:
(1.1) preprocessing the GNSS intermediate frequency signal, and inputting an original intermediate frequency signal;
(1.2) calculating to obtain an improved Ratio moving average value and an improved Ratio moving variance;
wherein, the expressions of the improved Ratio moving average value and the improved Ratio moving variance are as follows:
Figure BDA0003114336910000041
Figure BDA0003114336910000042
in the formula, Ratio ma and Ratio mv refer to an improved Ratio moving average and an improved Ratio moving variance, i denotes a sample number, w denotes a sliding window length, k denotes a sliding interval, n denotes a sliding window number, R denotes a sliding window numberd，improved(i) The expression of (a) is:
Figure BDA0003114336910000043
Figure BDA0003114336910000051
Figure BDA0003114336910000052
Figure BDA0003114336910000053
wherein, Ie，d(n)、Il，d(n) and Ip(n) the lead, lag and instantaneous outputs of the in-phase branch of the n-th coherent integrate-and-accumulate correlator, Qe，d(n)、Ql，d(n) and Qp(n) the lead, lag and instantaneous outputs of the quadrature arms of the correlator within the nth coherent integration, respectively;
(1.3) calculating to obtain an improved Delta moving mean value and an improved Delta moving variance;
the expressions of the improved Delta moving mean value and the improved Delta moving variance are as follows:
Figure BDA0003114336910000054
Figure BDA0003114336910000055
in the formula, DeltaMA and DeltaMV refer to improved Delta moving mean and improved Delta moving variance, Deltad，improved(i) The expression of (a) is:
Figure BDA0003114336910000056
(1.4) calculating to obtain an early-late code phase difference ELP;
Figure BDA0003114336910000057
wherein, Ie(n)、Qe(n)、Il(n) and Ql(n) the leading and lagging outputs of the I/Q branch correlator in the nth coherent integration respectively;
(1.5) calculating to obtain carrier-to-noise ratio mobile variance C/N0(i)MV；
Figure BDA0003114336910000058
(1.6) calculating to obtain the change rate of the receiver clock error;
Figure BDA0003114336910000059
(1.7) taking the improved Ratio moving average, the improved Ratio moving variance, the improved Delta moving average, the improved Delta moving variance, the early-late code phase difference, the carrier-to-noise Ratio moving variance and the receiver clock error change rate as an eigenvector x, wherein the sample eigenvector is as follows:
Figure BDA0003114336910000061
(1.8) preprocessing the data further comprises missing value processing, deviation calculation, taking the mean value of all observed satellite characteristic values, data standardization and principal component analysis;
marking the signals at all moments according to the known cheating application moments, wherein the moment point not applied with the cheating signals is marked as 0, and the moment point applied with the cheating signals is marked as 1;
constructing a three-layer BP neural network, randomly extracting 70% of data of the GNSS signal sample for training, and extracting 30% of data for testing;
the method specifically comprises the following steps:
(3.1) constructing a three-layer neural network model;
the specific three-layer model is an input layer model, a hidden layer model and an output layer model, the criterion function adopts a mean square error, and the expression is as follows:
Figure BDA0003114336910000062
wherein E iskThe error is represented by the number of bits in the error,
Figure BDA0003114336910000063
and
Figure BDA0003114336910000064
respectively an estimated value and a true value output by the neural network,
the optimization method selected by the neural network is a random gradient descent method, namely, parameters are updated from the direction of the negative gradient of the target, and for a given learning rate eta, the method comprises the following steps:
Figure BDA0003114336910000065
the number of selected input layer nodes is 7, the number of selected output layer nodes is 1, and the number of selected hidden layer nodes is 10;
the selected activation functions include, but are not limited to, a sigmod function, a tanh function, a relu function, and a leakyRelu function;
(3.2) randomly extracting 70% of signal samples as training samples and 30% of signal samples as testing samples, and putting the samples into a three-layer neural network model for training and testing;
example (c): the TEXBAT data set is a generative deception jamming experiment data set, the data set records an intermediate frequency signal when generative deception is carried out, the first 100s of the signal have no deception, the total time of the signal is 100s, and a confusion matrix for carrying out deception detection experiments by using BP neural networks with different activation functions is recorded in Table 1.
Figure BDA0003114336910000066
And step four, after the test is passed, the model can be used for detecting whether the deception jamming occurs.
The above description is only one of the preferred embodiments of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made in accordance with the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (2)

1. A GNSS generated deception jamming detection method based on a BP neural network comprises the following specific steps:
(1) preprocessing the intermediate frequency signal of the GNSS to obtain a feature vector used as the input quantity of the neural network, wherein the feature vector comprises an improved Ratio moving average value, an improved Ratio moving variance, an improved Delta moving average value, an improved Delta moving variance, an early-late code phase difference, a carrier-to-noise Ratio moving variance and a receiver clock difference change rate;
the step (1) specifically comprises the following steps:
(1.1) preprocessing the GNSS intermediate frequency signal, and inputting an original intermediate frequency signal;
(1.2) calculating to obtain an improved Ratio moving average value and an improved Ratio moving variance;
wherein, the expressions of the improved Ratio moving average value and the improved Ratio moving variance are as follows:
Figure FDA0003114336900000011
Figure FDA0003114336900000012
in the formula, Ratio ma and Ratio mv refer to an improved Ratio moving average and an improved Ratio moving variance, i denotes a sample number, w denotes a sliding window length, k denotes a sliding interval, n denotes a sliding window number, R denotes a sliding window numberd，improved(i) The expression of (a) is:
Figure FDA0003114336900000013
Figure FDA0003114336900000014
Figure FDA0003114336900000015
Figure FDA0003114336900000016
wherein, Ie，d(n)、Il，d(n) and Ip(n) the lead, lag and instantaneous outputs of the in-phase branch of the n-th coherent integrate-and-accumulate correlator, Qe，d(n)、Ql，d(n) and Qp(n) the lead, lag and instantaneous outputs of the quadrature arms of the correlator within the nth coherent integration, respectively;
(1.3) calculating to obtain an improved Delta moving mean value and an improved Delta moving variance;
the expressions of the improved Delta moving mean value and the improved Delta moving variance are as follows:
Figure FDA0003114336900000017
Figure FDA0003114336900000018
in the formula, DeltaMA and DeltaMV refer to improved Delta moving mean and improved Delta moving variance, Deltad，improved(i) The expression of (a) is:
Figure FDA0003114336900000021
(1.4) calculating to obtain an early-late code phase difference ELP;
Figure FDA0003114336900000022
wherein, Ie(n)、Qe(n)、Il(n) and Ql(n) the leading and lagging outputs of the I/Q branch correlator in the nth coherent integration respectively;
(1.5) calculating to obtain carrier-to-noise ratio mobile variance C/N0(i)MV；
Figure FDA0003114336900000023
(1.6) calculating to obtain the change rate of the receiver clock error;
Figure FDA0003114336900000024
(1.7) taking the improved Ratio moving average, the improved Ratio moving variance, the improved Delta moving average, the improved Delta moving variance, the early-late code phase difference, the carrier-to-noise Ratio moving variance and the receiver clock error change rate as an eigenvector x, wherein the sample eigenvector is as follows:
Figure FDA0003114336900000025
(1.8) preprocessing the data further comprises missing value processing, deviation calculation, taking the mean value of all observed satellite characteristic values, data standardization and principal component analysis;
(2) marking the signals at each moment according to the known cheating application moment, wherein the moment point without the cheating signal is marked as 0, and the moment point with the cheating signal is marked as 1;
(3) constructing a three-layer BP neural network, and randomly extracting 70% of data of a GNSS signal sample for training and 30% of data for testing;
(4) after passing the test, the model can be used to detect whether a spoofing disturbance has occurred.
2. The GNSS generative deception jamming detection method based on the BP neural network as claimed in claim 1, wherein:
the step (3) specifically comprises the following steps:
(3.1) constructing a three-layer neural network model;
the specific three-layer model is an input layer model, a hidden layer model and an output layer model, the criterion function adopts a mean square error, and the expression is as follows:
Figure FDA0003114336900000031
wherein E iskWhich is indicative of an estimation error that is,
Figure FDA0003114336900000032
and
Figure FDA0003114336900000033
respectively an estimated value and a true value output by the neural network,
the optimization method selected by the neural network is a random gradient descent method, namely, parameters are updated from the direction of the negative gradient of the target;
the number of selected input layer nodes is 7, the number of selected output layer nodes is 1, and the number of selected hidden layer nodes is 10;
the selected activation functions include, but are not limited to, a sigmod function, a tanh function, a relu function, and a leakyRelu function;
and (3.2) randomly drawing 70% of signal samples as training samples and 30% of signal samples as testing samples, and putting the samples into a three-layer neural network model for training and testing.
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