CN113376659B - 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

Info

Publication number
CN113376659B
CN113376659B CN202110658870.8A CN202110658870A CN113376659B CN 113376659 B CN113376659 B CN 113376659B CN 202110658870 A CN202110658870 A CN 202110658870A CN 113376659 B CN113376659 B CN 113376659B
Authority
CN
China
Prior art keywords
improved
neural network
gnss
variance
moving
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110658870.8A
Other languages
Chinese (zh)
Other versions
CN113376659A (en
Inventor
祝雪芬
华腾
杨帆
汤新华
陈熙源
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southeast University
Original Assignee
Southeast University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southeast University filed Critical Southeast University
Priority to CN202110658870.8A priority Critical patent/CN113376659B/en
Publication of CN113376659A publication Critical patent/CN113376659A/en
Application granted granted Critical
Publication of CN113376659B publication Critical patent/CN113376659B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/015—Arrangements for jamming, spoofing or other methods of denial of service of such systems
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a GNSS (global navigation satellite system) generative 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 GNSS signals, extracting characteristic vectors, and marking signals which are not subjected to generative deception interference and signals which are subjected to generative deception interference; 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 learned model; 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 early and late 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.
Drawings
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 FDA0003497613020000011
Figure FDA0003497613020000012
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 FDA0003497613020000013
Figure FDA0003497613020000014
Figure FDA0003497613020000015
Figure FDA0003497613020000016
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 FDA0003497613020000021
Figure FDA0003497613020000022
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 FDA0003497613020000023
(1.4) calculating to obtain an early-late code phase difference ELP;
Figure FDA0003497613020000024
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 FDA0003497613020000025
(1.6) calculating to obtain the change rate of the receiver clock error;
Figure FDA0003497613020000026
(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 FDA0003497613020000027
(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 model, and randomly extracting 70% of data of a GNSS signal sample for training and 30% of data for testing;
(4) after the test is passed, the three-layer BP neural network model is used for detecting whether the deception jamming occurs or not.
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 FDA0003497613020000031
wherein E iskWhich is indicative of an estimation error that is,
Figure FDA0003497613020000032
and
Figure FDA0003497613020000033
respectively an estimated value and a true value output by the neural network,
the optimization method selected by the neural network of the three-layer neural network model is a random gradient descent method, namely, parameters are updated from the negative gradient direction 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.
CN202110658870.8A 2021-06-15 2021-06-15 GNSS (global navigation satellite system) generated deception jamming detection method based on BP (back propagation) neural network Active CN113376659B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110658870.8A CN113376659B (en) 2021-06-15 2021-06-15 GNSS (global navigation satellite system) generated deception jamming detection method based on BP (back propagation) neural network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110658870.8A CN113376659B (en) 2021-06-15 2021-06-15 GNSS (global navigation satellite system) generated deception jamming detection method based on BP (back propagation) neural network

Publications (2)

Publication Number Publication Date
CN113376659A CN113376659A (en) 2021-09-10
CN113376659B true CN113376659B (en) 2022-05-13

Family

ID=77574377

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110658870.8A Active CN113376659B (en) 2021-06-15 2021-06-15 GNSS (global navigation satellite system) generated deception jamming detection method based on BP (back propagation) neural network

Country Status (1)

Country Link
CN (1) CN113376659B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115494526A (en) * 2022-08-29 2022-12-20 中山大学 GNSS deception jamming detection method and device, electronic equipment and storage medium
CN116087994B (en) * 2023-04-07 2023-07-07 中国人民解放军战略支援部队航天工程大学 Deception jamming detection method based on machine learning
CN116577808B (en) * 2023-07-11 2023-09-29 中国人民解放军战略支援部队航天工程大学 Navigation deception jamming detection method based on output of receiver correlator

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105550702B (en) * 2015-12-09 2019-07-02 哈尔滨工业大学 A kind of GNSS Deceiving interference recognition methods based on SVM
CN109188469B (en) * 2018-08-01 2022-07-08 南京航空航天大学 Deception signal parameter estimation method for GNSS signal receiver
CN109188470B (en) * 2018-09-11 2021-03-02 西安交通大学 GNSS deception jamming detection method based on convolutional neural network
CN109782304B (en) * 2019-01-29 2021-02-09 西安北斗星导航技术有限公司 GNSS deception jamming detection method and system in signal capture stage
CN110471091B (en) * 2019-08-29 2021-05-25 北京航空航天大学合肥创新研究院 Deception jamming detection method based on orthogonal component of correlator
CN111308514B (en) * 2020-04-01 2024-01-09 湖南航天电子科技有限公司 Satellite navigation spoofing detection method in wireless synchronous communication network
CN111399002B (en) * 2020-04-09 2022-06-07 西安交通大学 GNSS receiver combined interference classification and identification method based on two-stage neural network
CN112014805B (en) * 2020-08-12 2023-03-24 西安电子科技大学 Deception interference suppression method based on time diversity array MIMO radar

Also Published As

Publication number Publication date
CN113376659A (en) 2021-09-10

Similar Documents

Publication Publication Date Title
CN113376659B (en) GNSS (global navigation satellite system) generated deception jamming detection method based on BP (back propagation) neural network
Gross et al. Maximum-likelihood power-distortion monitoring for GNSS-signal authentication
CN113359158B (en) GNSS (Global navigation satellite System) generated deception jamming detection method based on SVM (support vector machine)
CN109188470B (en) GNSS deception jamming detection method based on convolutional neural network
CN110231633B (en) GNSS deception jamming identification and inhibition method and system based on LSTM in signal capture stage
WO2018076571A1 (en) Method and system for detecting abnormal value in lte network
CN107290741B (en) Indoor human body posture identification method based on weighted joint distance time-frequency transformation
Yan et al. Modeling and estimation of asynchronous multirate multisensor system with unreliable measurements
CN116577808B (en) Navigation deception jamming detection method based on output of receiver correlator
CN103995259B (en) Radar target adaptive-filtering fusion detection method under intensive interference environment
CN106443598A (en) Convolutional neural network based cooperative radar network track deception jamming discrimination method
CN105259540B (en) A kind of optimization method of multistation radar anti-active cheating formula interference
CN105550702B (en) A kind of GNSS Deceiving interference recognition methods based on SVM
CN105259541A (en) Method of confronting active deception jamming by multi-station radar
CN113406671A (en) GNSS forwarding type deception jamming detection method based on C/N0-MV
CN116660942B (en) Train satellite positioning deception jamming detection method and system based on deep learning
CN115494526A (en) GNSS deception jamming detection method and device, electronic equipment and storage medium
Zhu et al. Adversarial anomaly detection for marked spatio-temporal streaming data
Kumar et al. Localization with RSSI values for wireless sensor networks: An artificial neural network approach
CN116087994A (en) Deception jamming detection method based on machine learning
CN112213697A (en) Feature fusion method for radar deception jamming recognition based on Bayesian decision theory
CN105488413A (en) Malicious code detection method and system based on information gain
Tu et al. GNSS intermediate spoofing detection via dual‐peak in frequency domain and relative velocity residuals
CN105954740A (en) Polarization detection rapid implementation method based on local whitening
CN109031193A (en) Indoor illegal signal source positioning system and method based on signal arrival direction

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant