CN107290758B - GNSS interference signal multi-stage identification and detection system and method - Google Patents
GNSS interference signal multi-stage identification and detection system and method Download PDFInfo
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Abstract
The invention discloses a GNSS interference signal multistage identification detection system which comprises a radio frequency front end module, a primary detector, a capture processing module, a secondary detector, a multi-channel tracking processing module, a tertiary detector, an interference judgment module and a positioning calculation module, wherein the output ends of the primary detector, the secondary detector and the tertiary detector are all connected to the interference judgment module. Further discloses a GNSS interference signal multilevel identification detection method. By the system or the method, the influence characteristics of different interference signals in each link of signal processing of the receiver are comprehensively analyzed, interference characteristic parameters are extracted, a detection algorithm in each link is optimized, the detection accuracy is improved, the system and the method can synchronously work with a radio frequency front end in the receiving signal processing and baseband signal capturing and tracking stages, and the advantages of real-time performance, rapidity and low cost of interference detection are guaranteed.
Description
Technical Field
The invention belongs to the field of GNSS interference signal detection, and particularly relates to a multi-stage identification and detection system and method for GNSS interference signals.
Background
The rapid development of the internet of things and intelligent equipment promotes people to put forward higher requirements on the reliability of the positioning and navigation technology. As a positioning System widely used in daily life, a Global Navigation Satellite System (GNSS) has reliability affected by many factors. Satellite signals propagate to the ground over distances of many twenty thousand kilometers, with a power of only-130 dBm, and are susceptible to various intentional and unintentional interferences. These disturbances are classified into spoofing disturbances and squashing disturbances. The deception jamming is to transmit jamming signals similar to satellite signals to deceive the receiver, so that the receiver resolves wrong positioning information; the suppression type interference is to suppress satellite signals through high-power interference signals such as in-band noise, continuous waves, sweep waves and the like, so that the GNSS receiver cannot work normally.
In the GNSS interference detection, an energy detection method and the output of a front-end AGC module are mainly used to detect the existence of interference, but the type of the interference cannot be detected; the detection of the narrow-band interference is also carried out by adopting an improved DFT algorithm, but the detection is only suitable for the narrow-band interference; the method of using multiple correlators can monitor the signal quality, but the reason for the signal abnormality cannot be realized.
The automatic interference detection based on the parameters such as the output power of the correlator, the carrier phase fluctuation, the pseudo-range change and the like can detect the interferences such as gaussian white noise, continuous waves, pulse waves and the like, but the parameters required by the detection are in a signal tracking part and limited by the capturing success rate, and further research is needed in the realization of the detection work.
Generally, the application range of a large amount of interference detection technologies at present is single, the comprehensiveness is still insufficient on the detection of different types of suppression interference and deception interference, and the identification and detection of various interferences cannot be realized. The final objective of interference detection is to suppress interference, but at present, a large number of interference suppression algorithms only have a good suppression effect on one or more types of interference, and cannot suppress various types of interference well, so that it is difficult to suppress interference accurately and comprehensively when the type of interference cannot be determined. Therefore, the detection algorithm with the interference identification capability also ensures the subsequent inhibition of different interferences, and lays a foundation for improving the comprehensiveness of the inhibition scheme.
Disclosure of Invention
In the process of normal satellite signal propagation, other radio waves in the environment can emit various continuous wave, sweep wave, noise and other signals, so that the signals received by the antenna have different interferences. Some interference in a fixed frequency band may be different from a normal signal in an intermediate frequency stage of radio frequency front end processing and can be identified; however, interferences such as noise interference, spoofing interference and the like are invisible in the previous link, and the difference from the normal signal can be found only in the acquisition and tracking stage.
Based on the above consideration, the invention provides a multi-stage identification and detection technology for GNSS interference signals. The specific technical scheme is as follows:
the invention discloses a GNSS interference signal multistage identification detection system, which comprises a radio frequency front end module, a primary detector, a capture processing module, a secondary detector, a multi-channel tracking processing module, a tertiary detector, an interference judgment module and a positioning calculation module, wherein the input end of the radio frequency front end module is connected with a satellite antenna, the first output end of the radio frequency front end module is connected with the input end of the capture processing module, and the second output end of the radio frequency front end module is connected with the input end of the primary detector; the first output end of the capture processing module is connected with the input end of the multi-channel tracking processing module, and the second output end of the capture processing module is connected with the input end of the secondary detector; a first output end of the multi-channel tracking processing module is connected to the positioning resolving module, and a second output end of the multi-channel tracking processing module is connected to an input end of the three-level detector; the output ends of the first-level detector, the second-level detector and the third-level detector are all connected to the interference judging module.
Furthermore, the primary detector is used for detecting single-frequency or multi-frequency interference signals, the secondary detector is used for detecting noise interference signals and partial deception interference signals, and the tertiary detector is used for detecting deception interference signals and pulse interference signals.
Further, the detection algorithm adopted by the first-level detector is as follows: by extracting the time domain variation value and the frequency domain spectrum value of the intermediate frequency signal, difference and high-order statistics are carried out to obtain characteristic parameters reflecting the respective characteristics of different interference signals. For example, the primary detector extracts the time domain and the frequency domain of the intermediate frequency signal received from the radio frequency front end module, performs short-time fourier transform, obtains the change rule of the signal spectrum along with time through window function movement, and extracts the frequency of the interference signal from the change rule.
Further, the detection algorithm adopted by the secondary detector is as follows: by extracting the parameters of the signal in the capturing processing stage, the characteristic parameters of the characteristics of other interference signals which are not resolved in the reaction S2 are obtained after integration and difference processing. For example, the two-stage detector increases the signal-to-noise ratio of the signal by increasing the time of coherent integration and non-coherent integration, and performs noise interference detection by configuring the integration time. The secondary detector is also used for detecting part of deception jamming signals, and the adoption of multi-threshold setting or multi-correlation peak detection can realize obvious deception jamming and cross-correlation jamming on deception distance.
Further, the secondary detector is also used to detect a portion of the spoofed interfering signal.
Furthermore, the secondary detection algorithm adopts a multi-threshold setting method or a multi-correlation peak method to detect partial deception jamming.
Further, the detection algorithm adopted by the three-stage detector is as follows: by extracting the parameters of the signal processed in the tracking stage, the characteristic parameters reflecting the characteristics of the residual interference signals which are not resolved in S2 and S3 are obtained after statistic calculation and distribution fitting. For example, the three-stage detector samples the signal received from the multi-channel tracking processing module and the local correlation signal through a correlator, obtains the slope of a curve of adjacent sampling points, and detects a spoof signal and an impulse interference signal according to the distribution of signal correlation values and the change of a signal correlation curve.
Further, the radio frequency front end module is configured to process the received satellite signal into an intermediate frequency signal, and includes a band pass filter, an amplifier, a mixer, an a/D converter, and an automatic gain controller.
The invention also discloses a GNSS interference signal multilevel identification detection method, which comprises the following steps:
s1, satellite signals received by an antenna are processed by a radio frequency front end module to obtain intermediate frequency signals,
s2, extracting characteristic parameters of single-frequency interference or multi-frequency interference from the intermediate-frequency signal through a primary detector, and inputting the characteristic parameters to an interference judging module;
s3, no interference signal is detected in S2, and characteristic parameters of noise interference are extracted through a secondary detector in a capturing stage and input to an interference judging module;
s4, no interference signal is detected in S2 and S3, and characteristic parameters of pulse interference and deceptive interference are extracted through a three-stage detector in the tracking stage and input to an interference judging module;
s5, the interference discrimination module compares the characteristic parameters input by each stage of detector with the recognized characteristics of various interference signals to detect the final interference type. If no interference is detected in any of S2-S4, the signal is considered not to be interfered, and the positioning calculation is directly carried out.
Further, the detection algorithm adopted by the first-level detector is as follows: by extracting a time domain variation value and a frequency domain spectrum value of the intermediate frequency signal, obtaining characteristic parameters reflecting respective characteristics of different interference signals after difference and high-order statistics are carried out; the detection algorithm adopted by the secondary detector is as follows: the parameters of the signals in the capturing processing stage are extracted, and the characteristic parameters reflecting the characteristics of other unresolved interference signals in S2 are obtained after integration and difference processing; the detection algorithm adopted by the three-level detector is as follows: by extracting the parameters of the signal processed in the tracking stage, the characteristic parameters reflecting the characteristics of the residual interference signals which are not resolved in S2 and S3 are obtained after statistic calculation and distribution fitting.
Furthermore, partial characteristic parameters of deception interference can be extracted through the secondary detector and input to the interference discrimination module.
Furthermore, the secondary detection algorithm adopts a multi-threshold setting method or a multi-correlation peak method to detect partial deception jamming.
The GNSS interference signal multistage identification detection system and the GNSS interference signal multistage identification detection method have the following beneficial effects:
1) the method comprehensively analyzes the influence characteristics of different interference signals in each link of signal processing of the receiver, extracts interference characteristic parameters, optimizes a detection algorithm in each link and improves the detection accuracy.
2) And the system and the radio frequency front end synchronously work in the stages of receiving signal processing and capturing and tracking baseband signals (namely, processing and resolving signals of a receiver), so that the advantages of real-time performance, rapidity and low cost of interference detection are ensured.
3) The scheme of the flow detection has the functions of correcting and filling up the detection result of the previous detection link in the subsequent link, and is low in complexity and high in reliability of the detection result.
4) The method realizes a set of flow detection scheme from signal acquisition to signal calculation, greatly weakens the influence of interference on the subsequent positioning calculation link, and further ensures the anti-interference capability and stability of the receiver.
5) The detected interference type can adopt different interference detection schemes aiming at the interference type, so that the success rate of the system for inhibiting the interference is improved; meanwhile, the type of the interference is detected, and different strategies can be pertinently adopted in the processing link of the subsequent receiver to reduce the influence of the interference and improve the positioning resolving precision.
Drawings
FIG. 1 is a block diagram of a multi-stage GNSS interference signal identification and detection system
Detailed Description
As shown in fig. 1, the loop structure of the multi-stage GNSS interference signal identification and detection system is as follows:
the antenna receives satellite signals and inputs the satellite signals to the radio frequency front end of the receiver, and the output of the front end is divided into two paths: one path of the signal is input into a first-level detector, and the output of the first-level detector is input into an interference judgment module; the output of the other front-end processing is input to the capture processing module. The captured output is also divided into two paths: one path of the signal is input into a secondary detector, and the output of the secondary detector is input into an interference judgment module; and the output of the other path of acquisition processing is input into the multi-channel tracking processing module. The output of the tracking processing module is also divided into two paths: one path of the signal is input into a three-level detector, and the output of the three-level detector is input into an interference judgment module; and the output of the other path of tracking processing is input into a positioning resolving module. And finally, outputting the interference type of the GNSS by an interference judging module.
And extracting characteristic parameters of corresponding interference types in the signals at the three levels, inputting the characteristic parameters into an interference judgment module, and then uniformly processing and judging to finally obtain the types of the interference.
The working principle of the multi-stage identification and detection system of the GNSS interference signal comprises the following two stages:
the first stage is the determination of each stage of detection algorithm:
s11, on the basis of the signal processing process of the GNSS software receiver, expanding the process of improving the signal processing to detect the GNSS interference signal;
s12, extracting time domain variation values and frequency domain spectrum values of the intermediate frequency signals, carrying out difference and high order statistics to obtain detection parameters reflecting respective characteristics of different interference signals, and confirming a first-order detection algorithm;
s13, extracting parameters of the signals in the capturing processing stage, performing integration and difference processing to obtain detection parameters of the characteristics of other unresolved interferences in S12, and confirming a secondary detection algorithm;
and S14, extracting parameters of signal processing in the tracking stage, performing statistic calculation and distribution fitting to obtain detection parameters of the characteristics of the residual interference for distinguishing in S12 and S13, and confirming the three-stage detection algorithm.
And in the second stage, based on the detection algorithms of all stages determined in the first stage, the interference type is judged:
s21, processing the satellite signal received by the antenna by a radio frequency front end to obtain an intermediate frequency signal, extracting different parameters of single-frequency interference and multi-frequency interference by the intermediate frequency signal through a first-stage detection algorithm determined in the first stage S12, and comparing the parameters with a signal model of an interference judgment module to judge the type;
if no interference signal is detected in S22 and S21, in the capturing stage, the interference discrimination module detects and identifies the interference type again by using the parameters of the secondary detection algorithm determined in the first stage S13, so that the noise interference and partial deception interference can be discriminated;
if no interference signal is detected in S23 and S22, in the tracking stage, the interference discrimination module processes the parameters of the three-stage detection algorithm determined in the first stage S14 to detect the interference signal, and can distinguish impulse interference and deceptive interference;
and S24, if no interference is detected in S21, S22 and S23, the signals are considered not to be interfered, and the positioning calculation is directly carried out.
In the above steps, the first, second and third level detectors provide the initial detection parameters of various signals, and the interference judging module compares the characteristic types of the interference signals according to the extracted characteristic parameters of various signals input and input by the detectors of each level with the signal model of the interference judging module, so as to detect the final interference types. That is, if the input characteristic parameter matches a recognized characteristic of a certain interference signal, the input signal is considered to contain such interference.
For example, the primary detection finds that the spectrum at the intermediate frequency of the signal varies greatly, and if the spectrum is outside the error tolerance range, the intermediate frequency spectrum value is input to the interference detection module; if no abnormity is found in the secondary detection module, inputting 0 to the interference judgment module; and if the instantaneous correlation value of the signal is found to be periodically unlocked in the three-stage detection module, inputting the period T into the interference judgment module. And the final interference judgment module judges according to three groups of parameters: continuous wave interference of frequency alignment signal frequency is detected in the first-stage detection, interference is not found in the second-stage detection, and pulse interference with the period of T exists in the third-stage detection. Therefore, the signals mainly suffer from two interferences, namely continuous waves and pulses, namely single-frequency interference signals and pulse interference signals exist in the signals.
Based on the above principle, the detection algorithm adopted by each level in the GNSS interference signal multi-level identification and detection system is as follows:
the first-level detection algorithm: the method comprises the steps of carrying out short-time Fourier transform (STFT) on a digital intermediate-frequency signal obtained after radio frequency front-end processing, observing time domain and frequency domain characteristics of the signal at the same time, obtaining the influence condition of interference on the time domain and the frequency domain, obviously increasing the frequency spectrum on the time domain and increasing the frequency spectrum at a certain section of frequency on the frequency domain, and sequentially determining the frequency point or the frequency section of the interference.
Digital intermediate frequency signal s obtained after radio frequency front end processingIF(n) is:
in which the intermediate frequency signal of the satellite i
Comprises the following steps:
where n represents the time sequence of the discrete signal, t represents the propagation time delay, A is the amplitude of the signal, C (n) is the C/A code broadcast by the satellite, D (n) is the navigation data code, ω isIFThe intermediate frequency of the signal is theta, the phase offset is theta, the propagation delay is tau, N (n) the propagation error and the noise in the signal are N (n), and J (n) the interference signals to which the signal is subjected, such as continuous wave interference, frequency sweep interference, band-limited noise, impulse interference, deception interference and the like.
The short-time Fourier transform can observe the time domain and frequency domain characteristics of the signal at the same time, obtain more information quantity and better observe the signal characteristics, so the short-time Fourier transform is carried out on the formula (1):
where ω (n) is a window function and m is a discrete time series. And (4) obtaining the change rule of the signal frequency spectrum along with the time n through the window function movement. The frequency of the interference signal is extracted from the signal, and single-frequency or multi-frequency interference can be detected.
And (3) secondary detection algorithm: the time of coherent integration and non-coherent integration is increased, the signal-to-noise ratio of the signal can be increased, and noise interference is detected by reasonably configuring the integration time. Specifically, when the integration time of the acquisition is short, the noise is obvious, even some signals are submerged by the noise, and by increasing the time of coherent integration and non-coherent integration, the success rate of acquisition is increased, so that the noise interference is detected.
The multi-threshold setting and the multi-correlation peak detection can detect the deception interference and the cross-correlation interference with obvious deception distance. Therefore, the secondary detection algorithm may also combine multiple threshold setting and multiple correlation peaks to detect spoofing and cross-correlation interference.
The multi-threshold setting method comprises the following steps: the receiver searches for a satellite signal with normal strength of 44dB & Hz, and searches for a weak signal with strength higher than 39dB & Hz when the acquisition threshold is lowered, and if the Doppler frequency shift of the weak signal is found to be different from the frequency shift of a strong signal acquired before by an integral multiple of 1KHz in the process of detecting the weak signal, the signals can be deception signals or mutual interference. And similarly, gradually reducing the detection threshold and gradually searching weak signals at all levels.
Because the principle of the multi-correlation peak method is similar to that of the multi-threshold method, the description is omitted here.
And (3) a three-level detection algorithm: receiving the signal x output from the capture module1(t) locally estimated signal similar to the input signal
The correlation operation is performed by an instantaneous correlator, i.e. a 0-delay correlator, to obtain a correlation function of
In addition, other recurrent signals delayed relative to input signals delta tau, 2 delta tau and the like are locally generated, and the input signals and the delayed signals are simultaneously correlated through corresponding delay correlators to obtain correlation values:
different delay signals correspond to different sampling points, so that the slope of the curve of adjacent sampling points can be obtained according to the correlation values output by the correlators with different delays:
Lr1＝Rr1-R0，Lr2＝Rr2-R1(7)
fitting a correlation curve according to correlation values of different delays, judging the symmetry, smoothness and the like of the signal correlation curve according to slope judgment and the like, wherein if the correlation curve is asymmetric left and right or the slope jumps for multiple times, deception interference exists in the signal; if the distribution of the instantaneous correlation value does not conform to the normal distribution and periodic out-of-lock jumping exists, the existence of the pulse interference signal is indicated.
While the invention has been described with respect to a preferred embodiment, it is not intended to be limited to the embodiment, but rather to the embodiment in any form or material, it is to be understood that various modifications and additions may be made therein by those skilled in the art without departing from the scope of the invention as defined in the appended claims. Those skilled in the art can make various changes, modifications and equivalent arrangements, which are equivalent to the embodiments of the present invention, without departing from the spirit and scope of the present invention, and which may be made by utilizing the techniques disclosed above; meanwhile, any changes, modifications and variations of the above-described embodiments, which are equivalent to those of the technical spirit of the present invention, are also within the scope of the technical solution of the present invention.
Claims (9)
1. A GNSS interference signal multi-stage identification detection system is characterized by comprising a radio frequency front end module, a primary detector, a capture processing module, a secondary detector, a multi-channel tracking processing module, a tertiary detector, an interference judgment module and a positioning calculation module, wherein,
the input end of the radio frequency front-end module is connected with the satellite antenna, the first output end of the radio frequency front-end module is connected with the input end of the capturing processing module, and the second output end of the radio frequency front-end module is connected with the input end of the primary detector;
the first output end of the capture processing module is connected with the input end of the multi-channel tracking processing module, and the second output end of the capture processing module is connected with the input end of the secondary detector;
the first output end of the multi-channel tracking processing module is connected to the positioning resolving module, and the second output end of the multi-channel tracking processing module is connected with the input end of the three-level detector;
the output ends of the first-stage detector, the second-stage detector and the third-stage detector are connected to the interference judging module;
the primary detector is used for detecting single-frequency or multi-frequency interference signals, the secondary detector is used for detecting noise interference signals, and the tertiary detector is used for detecting deception interference signals and pulse interference signals.
2. The detection system of claim 1, wherein the primary detector employs a detection algorithm that is: and obtaining characteristic parameters reflecting the characteristics of the single-frequency or multi-frequency interference signals by extracting the time domain variation value and the frequency domain spectrum value of the intermediate frequency signal and performing difference and high-order statistics.
3. The detection system of claim 1, wherein the detection algorithm employed by the secondary detector is: and extracting parameters of the signals in a capturing processing stage, and performing integration and difference processing to obtain characteristic parameters reflecting the characteristics of the noise interference signals.
4. The detection system of claim 1, wherein the detection algorithm employed by the three-level detector is: by extracting the parameters of the signals processed in the tracking stage, the characteristic parameters reflecting the characteristics of the deception jamming signals and the pulse jamming signals are obtained after statistic calculation and distribution fitting.
5. The detection system of claim 1, wherein the secondary detector is further configured to detect a portion of the spoofed interfering signal.
6. The detection system of claim 5, wherein the algorithm employed by the secondary detector further comprises; and obtaining characteristic parameters reflecting the characteristics of part of deception jamming signals by adopting a multi-threshold setting method or a multi-correlation peak method.
7. The detection system of any one of claims 1 to 6, wherein the radio frequency front end module is configured to process the received satellite signal into an intermediate frequency signal, the radio frequency front end module comprising a band pass filter, an amplifier, a mixer, an A/D converter and an automatic gain controller.
8. A GNSS interference signal multi-stage identification detection method is characterized by comprising the following steps:
s1, satellite signals received by an antenna are processed by a radio frequency front end module to obtain intermediate frequency signals;
s2, extracting characteristic parameters reflecting characteristics of single-frequency interference or multi-frequency interference from the intermediate-frequency signal through a primary detector, and inputting the characteristic parameters to an interference judging module;
s3, no interference signal is detected in S2, and characteristic parameters reflecting noise interference characteristics are extracted through a secondary detector in a capturing stage and input to an interference judging module;
s4, no interference signal is detected in S2 and S3, characteristic parameters of reaction pulse interference and deceptive interference characteristics are extracted through a three-stage detector in the tracking stage, and the characteristic parameters are input to an interference judging module;
s5, the interference discrimination module compares the characteristic parameters input by each stage of detector with the recognized characteristics of various interference signals to detect the final interference type.
9. The detection method of claim 8, wherein the detection algorithm employed by the primary detector is: obtaining characteristic parameters reflecting single-frequency or multi-frequency interference characteristics by extracting time domain variation values and frequency domain spectrum values of the intermediate frequency signals and performing difference and high-order statistics; the detection algorithm adopted by the secondary detector is as follows: extracting parameters of the signals in a capturing processing stage, and performing integration and difference processing to obtain characteristic parameters reflecting the characteristics of noise interference signals; the detection algorithm adopted by the three-level detector is as follows: by extracting the parameters of the signals processed in the tracking stage, the characteristic parameters of the characteristics of the reaction pulse interference and the deception interference signals are obtained after statistic calculation and distribution fitting.
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|---|---|---|---|---|
| EP2930536A2 (en) * | 2014-04-09 | 2015-10-14 | The Mitre Corporation | Positioning, navigation, and timing device interference and spoofing detector with timing mitigation |
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| CN107290758A (en) | 2017-10-24 |
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