CN117250636A - Spoofing interference detection method based on correlation value characteristics - Google Patents
Spoofing interference detection method based on correlation value characteristics Download PDFInfo
- Publication number
- CN117250636A CN117250636A CN202310880653.2A CN202310880653A CN117250636A CN 117250636 A CN117250636 A CN 117250636A CN 202310880653 A CN202310880653 A CN 202310880653A CN 117250636 A CN117250636 A CN 117250636A
- Authority
- CN
- China
- Prior art keywords
- frequency
- signal
- correlation value
- deception
- receiver
- 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.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 45
- 230000000737 periodic effect Effects 0.000 claims abstract description 15
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 13
- 230000001427 coherent effect Effects 0.000 claims description 9
- 230000010354 integration Effects 0.000 claims description 9
- 238000001228 spectrum Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000005070 sampling Methods 0.000 claims description 2
- 238000004088 simulation Methods 0.000 abstract description 6
- 238000004422 calculation algorithm Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 8
- 230000008901 benefit Effects 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000005311 autocorrelation function Methods 0.000 description 3
- 238000009795 derivation Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 208000003443 Unconsciousness Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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
- G01S19/13—Receivers
- G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
- G01S19/215—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service issues related to spoofing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a deception jamming detection method based on correlation value characteristics, which is based on theoretical deduction of correlation values of deception signals and real signals, wherein under traction deception jamming, due to the existence of Doppler frequency difference, correlation values represent the characteristics of periodic signals, spectrum analysis is carried out aiming at the correlation value periodic characteristics, and deception detection is carried out by combining peak detection; the invention discovers the influence of the relative Doppler frequency between the deception signal and the real signal on the tracking stage of the receiver through theoretical analysis, and theoretical deduction and simulation verify the characteristic that the correlation value of the receiver can show periodic variation under the condition that the relative Doppler frequency exists.
Description
Technical Field
The invention belongs to the technical field of satellite navigation positioning, and particularly relates to a satellite navigation receiver deception jamming detection method.
Background
Satellite navigation is the most widely used navigation mode at present, and along with the continuous development of satellite navigation systems of various countries, the satellite signal system at present is rich in system, numerous in frequency points and continuous in improvement of navigation positioning precision. However, due to the fact that satellite signals are disclosed and the power to the ground is low, the receiver is easily interfered, and with the development of software receivers and radio equipment, the actual deception interference on satellite signals is possible. Spoofing can cause the target to obtain a wrong positioning result under the unconscious condition, and threatens safety. Therefore, research on real-time spoofing detection algorithms is of great importance to satellite navigation systems to ensure the accuracy and reliability of navigation positioning.
As the spoofing detection algorithm is continuously perfected, the spoofing means is continuously upgraded, and the performance of the existing spoofing detection algorithm is greatly reduced for small spoofing changes. In the intermediate frequency signal processing stage, signal quality monitoring (Signal Quality Monitoring, SQM) is a common fraud monitoring method. The traditional SQM index uses three index outputs, so that the detection index is easily affected by noise, and the detection probability is relatively low. The indexes are detected through asymmetry introduced in the dragging process of the correlation peak, and for hidden spoofing attack, a spoofer shifts a tracking point of a target receiver to a real signal through slow traction, and the detection rate of the traditional SQM algorithm is further reduced due to the fact that the traction rate is slower and the asymmetry of the correlation peak is weaker.
The detection of the deception jamming is to find a proper detection amount based on the influence characteristics of the deception jamming on the receiver and determine a detection threshold, so that the influence of the deception jamming on the receiver is a key for determining the detection amount and the detection method of the deception jamming, and the influence characteristics of the carrier frequency difference on the target receiver are difficult to analyze generally when deception impact analysis is carried out or the deception jamming signals and real signals are assumed to have approximate carrier Doppler frequencies, and the simplification can highlight the influence of the deception signal code delay. Currently, the spoofing detection for the Doppler frequency is limited, and a target receiver needs to do specific circular motion, which has a great limit on the application range.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a deception jamming detection method based on correlation value characteristics.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a deception jamming detection method based on correlation value features is disclosed, which is based on the theoretical derivation of the correlation values of deception signals and real signals, wherein under the traction deception jamming, the correlation values show the characteristics of periodic signals due to the existence of Doppler frequency difference, the spectrum analysis is carried out aiming at the correlation value periodic features, and the deception detection is carried out by combining peak detection.
The output value of the correlator with the accumulated window length is needed before the deception jamming detection, and for the GPS L1C/A signal, the integral time of the receiver is generally set to be 1ms, the output frequency of coherent integral data is 1000Hz, and when the data with the window length is accumulated, window data processing is started. Firstly, carrying out least square fitting on window data, and carrying out trending by subtracting a fitting result from original data; performing FFT (fast Fourier transform) change on the data subjected to trend removal, and obtaining the frequency spectrum amplitude corresponding to each frequency point through amplitude adjustment; then calculating a spectrum amplitude average value, and obtaining a peak detection threshold value of the spectrum amplitude through a set peak-to-average ratio; and finally comparing the peak value of the frequency spectrum amplitude with a threshold value, and alarming the receiver when the peak value is larger than the threshold value, otherwise, not alarming. Real-time processing of the receiver is achieved by continuously accumulating cycles.
Theoretical derivation of the invention:
when the receiver is deceptively interfered, the received signal contains both the true signal and the deceptive signal, and assuming that a satellite signal is affected by deceptive interference, the received signal model may be represented as a superposition of the true signal and the deceptive signal, that is:
s(t)＝s au (t)+s sp (t) (1)
wherein t represents time, s au (t) and s sp (t) representing the true signal and the rogue signal of a single satellite, respectively. The intermediate frequency signal received by the receiver includes pseudo code, data code and carrier wave, which can be specifically described as:
s sp (t)＝A sp C(t+τ sp )D(t+τ sp )cos(2π(f IF +f d,sp )(t+τ sp )+φ sp ) (2)
s au (t)＝A au C(t+τ au )D(t+τ au )cos(2π(f IF +f d,au )(t+τ au )+φ au ) (3)
wherein A is au Is the signal amplitude of the real signal, A sp Signal amplitude for the spoofed signal; c is a ranging code, and a GPS L1C/A code is adopted in the text; d is a data code, namely a navigation message; τ au For true signal delay τ sp Delay for spoofing signals; f (f) IF Is the carrier center frequency, f d,au For the true signal Doppler frequency, f d,sp Doppler frequency for spoofing signals; phi (phi) au For the true signal initial carrier phase, phi sp To deceive the signal initial carrier phase, pi is the circumference ratio.
For intermediate frequency signals, coherent integration and low-pass filtering are carried out between the receiver and the locally copied ranging code through a homodromous branch (I) and a quadrature branch (Q), and then the output I of the instant branch of the receiver is obtained P The expression is:
wherein i is P (t) is the mixing result of the same-direction branch instant road, R au (τ) represents the real signal autocorrelation function value, R sp (τ) represents a spoofing signal autocorrelation function value;representing the difference between the real signal carrier frequency and the local replica signal carrier frequency, < >>Representing the difference between the spoofed signal carrier frequency and the local replica signal carrier frequency; />Representing the difference between the carrier phases of the real signal and the local replica signal,/or->Representing the difference in carrier phase between the spoofed signal and the locally replicated signal; t (T) coh For coherent integration time, for C/A codes, the coherent integration time is typically 1ms; t is t 1 Indicating the integration start time.
Similarly, Q-way coherent integration value Q P Expressed as:
wherein q is P And (t) is the mixing result of the orthogonal branch real-time channel.
At this time, the correlator output P of the instant branch is:
wherein V is au For the sum of the real signal amplitude, the product of the real signal amplitude, the autocorrelation function and the attenuation caused by the frequency error, i.e. the coefficient before the first cos function, V sp Then the sum of the spoofing signal amplitudes, i.e., the coefficient preceding the second term cos function in equation (6);the sum of the phases of the real signal and the deception signal is the value in the brackets of the first term sin function and the second term sin function respectively; f (f) △ 、θ △ The difference between the Doppler frequencies of the true signal and the spoofed signal and the initial phase are respectively:
wherein the difference f of Doppler frequencies △ The pull rate v is derived from the Doppler frequency effect due to the pull-type spoofing rate pull The difference in Doppler frequencies is:
wherein f L1 The L1 carrier frequency and c is the speed of light.
When the spoofing signal is present, the receiver begins tracking the spoofing signal due to the power advantage of the spoofing signal and the local carrier frequency is adjusted to coincide with the spoofing signal carrier frequency. The correlator output is now at a frequency f △ Is a periodic signal of (a). The noise is neglected in the theoretical derivation process, in practice, due to the influences of temperature, mechanical vibration crystal oscillator and the like, various noises exist in a receiver, and the noise item eta output by the correlator can be considered to meet the condition that the mean value is zero and the variance is sigma 2 Is a normal distribution of (c).
Thus, when there is fraud in the receiver, the correlator output is a periodic signal with white noise, and when there is no fraud in the receiver, the correlator output is a constant signal with white noise.
Simulating the situation that the receiver is deceptively, the deceptively signal does not exist in the previous 10s, and the deceptively signal with the power advantage of 6dB and the traction rate of 1m/s is directly added at the 10 s. As shown in fig. 2, after adding the spoofing signal, the correlator output has a significant periodic signal, consistent with the analysis result of equation (6).
As shown in fig. 1, a spoofing interference detection method based on correlation value characteristics includes the following steps:
step (1), selecting a correlation value by adopting a sliding window, setting the coherent integration time of a receiver to be 1ms for GPS L1C/A signals, outputting the coherent integration data with the output frequency of 1000Hz, and starting window data processing when the data with the window length is accumulated; the sliding distance is equal to the window length, and no data overlap exists between two adjacent windows; since the frequency domain analysis is needed to judge whether the periodic signal exists or not later, the window period needs to be ensured to be larger than the period of the periodic signal; let window period be T w The traction rate v being derived from Doppler frequency effects pull The difference in the Doppler frequencies brought about is expressed as:
wherein f △ Is the difference between the Doppler frequency and the initial phase of the real signal, f L1 The carrier frequency is L1, c is the speed of light;
traction rate v according to equation (8) pull The requirements are satisfied:
when the sliding window length is set to 1000, i.e., the window period is 1s, the minimum traction rate detectable at this time is 0.19m/s according to equation (9);
step (2), performing time domain processing of sliding window data: because the influence of the direct current component on the frequency spectrum needs to be avoided during the frequency spectrum analysis, trending treatment is needed in the time domain; performing second-order polynomial fitting based on a least square method, subtracting a fitting result from an original signal, removing a direct current component, and retaining noise;
step (3), frequency domain analysis: the amplitude of the signal at each frequency spectrum is obtained through fast Fourier change, the sequence X (k) is obtained after supposing that the sequence X (N) with the length of M is subjected to FFT, the FFT conversion point number is the same as the time domain sequence point number, and is marked as N, and the frequency resolution Deltaf is as follows:
wherein Fs is sampling frequency in Hz; the amplitude corresponding to the kth frequency point is:
step (4), peak detection: through the sufficient statistics of the Gaussian white noise, the maximum peak-to-average ratio PAR of the Gaussian white noise is 12dB under the 99% probability; calculating the average value mu of the frequency domain amplitude, and setting the threshold value Th as the average value multiplied by the peak-to-average ratio:
Th＝PAR*μ (12)
when the peak value of the frequency domain amplitude is larger than the threshold value, the receiver is considered to be deceptively interfered at a constant speed.
Compared with the prior art, the invention has the following beneficial effects:
(1) The influence of the relative Doppler frequency between the deception signal and the real signal on the tracking stage of the receiver is found through theoretical analysis, and theoretical deduction and simulation verify that the correlation value of the receiver can show periodic variation under the condition that the relative Doppler frequency exists.
(2) The invention provides a novel deception detection mode, which detects periodic signals from correlation values to detect uniform-speed traction type interference deception in a targeted manner, and can still keep good detection performance for traction type deception with smaller traction rate, and when the window period is set to be 1s, theory and simulation show that the minimum traction rate which can be detected is 0.2m/s.
(3) The invention does not need to change the existing structure of the receiver, does not need additional hardware assistance, is easy to realize on a commercial receiver, and has certain engineering application value.
Drawings
FIG. 1 is a block diagram of an algorithm of the present invention;
FIG. 2 is a graph of correlator output under simulated pull-type spoofing in accordance with the present invention;
fig. 3 is a time-frequency domain diagram of a single-window inner correlator output in accordance with the present invention, wherein: FIG. 3 (a) no spoofing, FIG. 3 (b) there is spoofing;
FIG. 4 is a graph of correlator output detection in the present invention;
fig. 5 is a graph of detection results at a spoof pull rate threshold in accordance with the present invention, wherein: FIG. 5 (a) traction rate 0.1m/s, and FIG. 5 (b) traction rate 0.2m/s;
fig. 6TEXBAT test results diagram, wherein: fig. 6 (a) scenario 2 is static spoofing and fig. 6 (b) scenario 5 is dynamic spoofing.
Detailed Description
The invention will be further illustrated with reference to examples.
Simulation experiment
And generating each group of simulation fraud scenes through the fraud interference software simulation platform to carry out algorithm test.
When spoofing does not exist, the time domain is noise, the peak value of the frequency domain amplitude is smaller than the threshold value, the receiver does not alarm, when the spoofing signal exists, the time domain presents a periodic signal, the peak value of the frequency domain amplitude exceeds the threshold value a lot, and the receiver alarms at the moment and successfully detects traction type spoofing interference.
FIG. 4 is an overall detection graph of a spoofing scenario with a simulated power advantage of 6dB, and a pull rate of 5 m/s. And 10s before and after the deception is added are selected for drawing and alarm analysis, so that the recognition result of the algorithm is accurate, and false alarms and missed detection are avoided. The deception signal is added at 10s, the detection result is alarmed at 11s, the detection time of the algorithm is the time corresponding to one window data, the window length selected by the group of data is 1000, and the detection time corresponds to 1 s.
Fig. 5 simulates a spoofing scenario where a spoofing pull rate threshold can be detected, with a power advantage of 6dB, and pull rates of 01m/s and 0.2m/s, respectively.
As can be seen from fig. 5, the lowest traction rate that can be detected is 0.2m/s when the window period is 1s, which is in accordance with the theoretical calculation of equation (9).
Actual measurement experiment
TEXBAT (Texas Spoofing Test Battery) is a high fidelity GPS L1C/a code spoofing dataset acquired by the auspices division team at the university of texas, usa, which contains a variety of realistic spoofing scenarios. The invention selects two traction type deception scenes in data for detection algorithm verification, and the specific scenes are shown in table 1:
table 1: TEXBAT scene description
The peak-to-average ratio is set to be 14dB, namely the amplitude ratio of the peak value to the average value is 25.1, the threshold value of the peak value is 25.1 mu, the sliding window length is 1000, the detection result is shown in fig. 6, the scene 2 starts to be pulled at 135s as shown in fig. 6, and as can be seen from fig. 6 (a), the detection effect of the deception detection method for the pull-type deception is good. Whereas for the dynamic process of scenario 5, the spoof pulling process is 145-205 s, it can be seen from fig. 6 (b) that for the pulling process, the spoof detection method can always alarm.
For the spoofing traction process, the statistical miss rate and false alarm rate are shown in table 2.
Table 2TEXBAT data spoofing detection results
Comprehensive test results can show that the detection algorithm provided herein can effectively detect the traction process of the deception signal. When the window length is set to 1000 (1 s), the omission ratio of simulation and measured data is extremely low, and detection of the deception traction process is well achieved.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (3)
1. The spoofing interference detection method based on the correlation value characteristics is characterized by comprising the following steps of:
selecting a correlation value by adopting a sliding window, and starting window data processing when data with the window length is accumulated for GPS L1C/A signals; the sliding distance is equal to the window length, and no data overlap exists between two adjacent windows; since the frequency domain analysis is needed to judge whether the periodic signal exists or not later, the window period needs to be ensured to be larger than the period of the periodic signal;
from Doppler frequency effects, the traction rate v pull The brought DuoduDifference f of the Doppler frequencies △ Expressed as:
wherein f L1 The carrier frequency is L1, c is the speed of light;
let window period be T w Traction rate v according to equation (8) pull The requirements are satisfied:
step (2), performing time domain processing of sliding window data: because the influence of the direct current component on the frequency spectrum needs to be avoided during the frequency spectrum analysis, trending treatment is needed in the time domain; performing second-order polynomial fitting based on a least square method, subtracting a fitting result from an original signal, removing a direct current component, and retaining noise;
step (3), frequency domain analysis: the amplitude of the signal at each frequency spectrum is obtained through fast Fourier change, the sequence X (k) is obtained after supposing that the sequence X (N) with the length of M is subjected to FFT, the FFT conversion point number is the same as the time domain sequence point number, and is marked as N, and the frequency resolution Deltaf is as follows:
wherein Fs is sampling frequency in Hz; the amplitude corresponding to the kth frequency point is:
step (4), peak detection: through the sufficient statistics of the Gaussian white noise, the maximum peak-to-average ratio PAR of the Gaussian white noise is 12dB under the 99% probability; calculating the average value mu of the frequency domain amplitude, and setting the threshold value Th as the average value multiplied by the peak-to-average ratio:
Th＝PAR*μ (12)
when the peak value of the frequency domain amplitude is larger than the threshold value, the receiver is considered to be deceptively interfered at a constant speed.
2. The method for detecting fraud based on correlation value features of claim 1,
in the step (1), if the coherent integration time of the receiver is set to 1ms, the output frequency of the coherent integration data is 1000Hz.
3. The method for detecting fraud based on correlation value features of claim 1,
in the step (1), when the sliding window length is set to 1000, i.e., the window period is 1s, the minimum traction rate that can be detected according to the formula (9) is 0.19m/s.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310880653.2A CN117250636A (en) | 2023-07-18 | 2023-07-18 | Spoofing interference detection method based on correlation value characteristics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310880653.2A CN117250636A (en) | 2023-07-18 | 2023-07-18 | Spoofing interference detection method based on correlation value characteristics |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117250636A true CN117250636A (en) | 2023-12-19 |
Family
ID=89127088
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310880653.2A Pending CN117250636A (en) | 2023-07-18 | 2023-07-18 | Spoofing interference detection method based on correlation value characteristics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117250636A (en) |
-
2023
- 2023-07-18 CN CN202310880653.2A patent/CN117250636A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6363637B2 (en) | System and method for detecting spoofing of radio signals | |
| CN102156282B (en) | Method for detecting radar target based on micro-Doppler effect | |
| Wu et al. | False-target recognition against interrupted-sampling repeater jamming based on integration decomposition | |
| Carmillet et al. | Detection of phase-or frequency-modulated signals in reverberation noise | |
| CN106125053A (en) | Pulse Doppler radar polarization anti jamming method | |
| CN104199001A (en) | Velocity-deception-jamming-resistant phase encoding method for cognitive radar | |
| CN113447959B (en) | GNSS deception jamming detection method and related device based on Doppler frequency | |
| CN103630916B (en) | Code capturing method based on dual-FFT frequency domain filtering | |
| KR20160094728A (en) | Appratus and method for generating global satellite system solution | |
| Li et al. | Estimation of micro-motion parameters based on cyclostationary analysis | |
| CN105005057A (en) | Beidou navigation system D1 navigation message capture method | |
| EP2007020A2 (en) | Method and apparatus for detecting lock status of a GPS signal | |
| CN110336583A (en) | A kind of quick catching device and method of Direct Sequence Spread Spectrum Signal | |
| CN110275190B (en) | Beidou signal high-precision capturing method based on table lookup method | |
| CN117250636A (en) | Spoofing interference detection method based on correlation value characteristics | |
| CN106019250A (en) | Repeating false target discriminating method based on angular glint | |
| CN103185879A (en) | Method for detecting single-channel synthetic aperture radar moving target with high radial velocity target | |
| Huang et al. | Low-observable maneuvering target detection based on Radon-advanced discrete chirp Fourier transform | |
| Lei et al. | Wavelet-based method for weak satellite signal acquisition | |
| Wu et al. | A block zero-padding method based on DCFT for L1 parameter estimations in weak signal and high dynamic environments | |
| Singh et al. | Multiple target detection and estimation of range and Doppler for OFDM-RADAR system | |
| CN114910931A (en) | Induced deception detection method based on weighted second-order central moment | |
| CN106849992B (en) | A kind of detection method of the direct sequence signal based on Generalized Quadratic power spectrum | |
| CN116520357B (en) | GNSS receiver deception jamming detection method and device based on sparse decomposition | |
| CN104217119A (en) | Maneuvering target self-adaption compensation detecting method based on S-Method time-frequency decomposition |
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 |