Interference signal classification and identification feature extraction method and system for satellite navigation system

A satellite navigation system and feature extraction technology, applied in the field of interference signal classification and identification feature extraction, can solve the problems of poor single-tone interference identification performance, unreliable navigation, non-existence, etc., so as to improve timeliness and accuracy, and improve resistance to Interfering performance, improving the effect of low recognition rate problems

Active Publication Date: 2020-12-15
HARBIN ENG UNIV +1
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Problems solved by technology

[0002] Affected by complex electromagnetic interference in space, satellite navigation systems are facing huge challenges in terms of availability and reliability. Unreliable navigation incidents caused by interference have become common occurrences. Scholars at home and abroad have proposed many anti-interference methods, but there is no one This method can deal with all types of interference, so it is necessary to analyze the types of interference signals, and adopt different anti-interference methods according to different types of interference signals, so as to provide the necessary guarantee for the smooth construction and normal operation of the satellite navigation system
[0003] In 2010, Meng, X.Y. et al. published a paper titled "An Intelligent Anti-jamming Frequency Hopping System" at the "2010 First International Conference on Pervasive Computing, Signal Processing and Applications" conference. The interference type identification simulation was carried out in the frequency hopping system, and in the time domain , frequency domain, and transform domain to extract multiple features to realize the identification of various interference types, but the method uses a decision tree classifier with manually set thresholds to reduce the accuracy of interference identification
However, the performance of the algorithm for identifying single-tone interference is worse than that of the decision tree algorithm.
[0005] In 2014, Huang Hao and others published a paper titled "Interference Identification Method Based on Fractal Box Dimension and Wavelet Packet Energy" in the journal "Journal of Air Force Early Warning Academy". The characteristic parameters of the classifier, and design a BT-SVM classifier for interference recognition, but the box dimension is greatly affected by noise, and the recognition rate is not high when the signal-to-noise ratio is low

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  • Interference signal classification and identification feature extraction method and system for satellite navigation system

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Embodiment 1

[0051] The present invention provides Sevcik fractal dimension D and spectrum flatness F based on frequency domain se A feature extraction method for the classification and identification of interference signals for satellite navigation systems based on the two-dimensional feature parameter extraction. This method combines the frequency domain Sevcik fractal dimension D and spectrum flatness F se Composition of two-dimensional feature parameter vector T = [D, F se ], and then use the SVM classifier for classification recognition.

[0052] A satellite navigation system-oriented interference signal classification and identification feature extraction method is characterized in that it comprises the following steps:

[0053] Step 1: Sampling the received signal y(t) to obtain the sampling sequence {y(n)} of the received signal, n=1,...,N;

[0054] Step 2: Perform FFT transformation on the sampling sequence {y(n)} of the received signal to obtain the frequency spectrum {f(n)} of...

Embodiment 2

[0069] Aiming at the problem that the decision tree recognition algorithm needs to extract multiple features and the recognition algorithm based on the box dimension feature in the SVM recognition algorithm has a low recognition rate in the case of low JNR, the present invention provides a satellite navigation system-oriented interference signal classification and recognition Feature extraction system, the system extracts the frequency domain Sevcik fractal dimension D and spectrum flatness F of the interference signal se Two feature parameters are combined into a binary feature vector, and then classified and recognized by SVM, which effectively improves the timeliness and accuracy of the recognition algorithm.

[0070] A satellite navigation system-oriented interference signal classification and identification feature extraction system, comprising an FFT converter 1, a selector 2, a subtractor 3, a divider 4, a frequency domain Sevcik fractal dimension calculation module 5, a...

Embodiment 3

[0084] figure 2 It is a flow chart of realization of six kinds of frequency-domain Sevcik fractal dimensions of interference signals respectively extracted corresponding to the present invention, which is composed of a differencer, a squarer, an adder, a divider, a square root converter and a logarithmic converter. figure 2 The meanings of the symbols are as follows:

[0085] L i : The distance between any two points in the received signal spectrum sequence;

[0086] L: the length of the waveform;

[0087] D: Frequency-domain Sevcik fractal dimension of the received signal.

[0088] A kind of interference signal classification identification feature extraction system facing satellite navigation system of the present invention extracts the Sevcik fractal dimension in the frequency domain of the received signal as the first characteristic parameter, and the extraction process of this feature is as follows:

[0089] 1) Let the waveform signal consist of a series of points (...

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Abstract

The invention belongs to the technical field of satellite navigation system anti-interference, and particularly relates to an interference signal classification and identification feature extraction method and system for a satellite navigation system. According to the interference signal classification and identification feature extraction method for the satellite navigation system, accurate identification of six interference types can be realized, the problem of excessive feature parameters of a decision tree classification method is solved, and meanwhile, the problem of low identification rate of a box-dimension-based classification algorithm under the condition of low JNR is solved; and the anti-interference performance of the satellite navigation system can be improved. According to the interference signal classification and identification feature extraction system for the satellite navigation system provided by the invention, the two feature parameters of the frequency domain Sevcik fractal dimension D and the frequency spectrum flatness Fse of the interference signal are extracted and combined into the two-bit feature vector, and then classification and identification are performed through the SVM, so that the timeliness and accuracy of an identification algorithm are effectively improved.

Description

technical field [0001] The invention belongs to the technical field of anti-jamming of satellite navigation systems, and in particular relates to a method and system for extracting features of classification and identification of jamming signals oriented to satellite navigation systems. Background technique [0002] Affected by complex electromagnetic interference in space, satellite navigation systems are facing huge challenges in terms of availability and reliability. Unreliable navigation incidents caused by interference have become common occurrences. Scholars at home and abroad have proposed many anti-interference methods, but there is no one This method can deal with all types of interference, so it is necessary to analyze the type of interference signal, and adopt different anti-interference methods according to the type of interference signal, so as to provide the necessary guarantee for the smooth construction and normal operation of the satellite navigation system. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S19/21G01S19/37
CPCG01S19/21G01S19/37Y02D30/70
Inventor 薛睿刘靖唐怀玉柴慧斯
Owner HARBIN ENG UNIV
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