CN112162245B - Radar broadband interference identification method based on time-frequency power spectrum projection - Google Patents
Radar broadband interference identification method based on time-frequency power spectrum projection Download PDFInfo
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- CN112162245B CN112162245B CN202011048232.6A CN202011048232A CN112162245B CN 112162245 B CN112162245 B CN 112162245B CN 202011048232 A CN202011048232 A CN 202011048232A CN 112162245 B CN112162245 B CN 112162245B
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- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The invention relates to a radar broadband interference identification method based on time-frequency power spectrum projection. Aiming at the defects of ambiguity and subjectivity caused by more dependence on manual judgment and setting in the conventional radar interference type confirmation and anti-interference measure selection, a method for accurately classifying four types of typical broadband interference, namely aiming frequency interference, frequency sweep interference, broadband suppression interference and comb spectrum interference is provided, a three-dimensional time-frequency power spectrum of a broadband echo signal is constructed, the time-frequency power spectrum is binarized and projected to a frequency domain, and the processes of waveform moment calculation, coagulation parameter measurement, interference classification identification and the like are carried out on projected energy. The invention effectively enhances the automatic selection capability of anti-interference measures and improves the survival capability and the fighting efficiency of the radar in the complex electromagnetic environment.
Description
Technical Field
The invention relates to an interference identification method, which is mainly applied to the field of radar interference signal classification and identification.
Background
With the continuous development of electronic warfare equipment and special technologies in the field of ECM, various radar broadband active interference patterns with specific interference effects are proposed in succession, and new challenges are provided for the sensing capability and the anti-interference capability of modern radars to battlefield interference environments.
And the knowledge of the interference environment is the basic premise for the radar to accurately resist active interference. The interference environment cognition needs to extract, analyze and classify the characteristics of an interference source, and a specific interference pattern is identified so as to provide prior information for the optimized selection of anti-interference measures. The radar interference pattern recognition means that the radar processes received interference signals when suffering interference, extracts characteristic information of the interference signals so as to judge which interference is received, and is convenient for follow-up adoption of corresponding anti-interference means so as to ensure normal work of own radar.
At present, the radar has relatively perfect anti-interference function and strategy, but cannot automatically sense the interference type in real time, the radar relies on manual judgment and setting on interference identification and selection of anti-interference measures, and the radar has strong ambiguity and subjectivity, and the failure of the radar to automatically and effectively detect and identify the interference becomes one of important factors restricting the development of the radar anti-interference technology. Therefore, the development of the radar interference signal identification technology has great practical significance for improving the survival capability and the fighting efficiency of the radar in the complex electromagnetic environment. The scheme provides a radar broadband interference identification method based on time-frequency power spectrum projection, and the method has the advantages of small calculated amount, high identification speed, high accuracy, simple flow, convenience in engineering implementation and the like.
Disclosure of Invention
In order to overcome the defects that the traditional radar is dependent on manual judgment and setting in interference identification and selection of anti-interference measures and has strong fuzziness and subjectivity, the invention provides a radar broadband interference identification method based on time-frequency power spectrum projection by constructing a three-dimensional time-frequency power spectrum and typical characteristics based on the time-frequency power spectrum.
In order to realize the purpose of the invention, the technical scheme of the invention is as follows:
s1: constructing a three-dimensional time-frequency power spectrum of the broadband echo signal;
s2: and (3) time-frequency power spectrum binary segmentation: solving the time-frequency power spectrum energy segmentation threshold value, wherein the formula is as follows:
according to the threshold value TrAnd performing binary segmentation on the time-frequency power spectrum to form a binary time-frequency power spectrum, wherein the formula is as follows:
wherein: the point which is larger than the threshold value is called an object point, and the point which is smaller than the threshold value is called a background point;
s3: object point frequency domain projection: projecting all object points along a frequency domain, and realizing energy accumulation of frequency domain projection, wherein the formula is as follows:
counting non-zero numbers of energy accumulation S (N) on all frequency points, and when the number is more than N/2, performing step S4; otherwise, performing step S5;
s4: calculating the second-order waveform moment of the projection energy: the degree of waviness of the waveform can be determined by its second-order moment, which defines the projection energy s (n) as:
when the second-order waveform moment Mo is larger than 3, the broadband suppression interference is realized; otherwise, the interference is the frequency sweep interference;
s5: calculating the number of points after the condensation of projection energy: the 3/5 criterion is used to realize the connection of the splitting points of the projection energy on the frequency domain, and the formula is as follows:
merging and condensing the communicated data Z (n), namely counting the number of data segments continuously being 1, removing the data segments of which the number is less than 4, wherein the number of the residual condensing points is C, and when C is more than 3, the comb spectrum interference is generated; otherwise, the signal is the frequency-aiming interference.
Further, in S1: performing Fourier transform on intermediate frequency data of every N points of a broadband echo signal to obtain frequency spectrum information of the broadband echo signal, then sequentially performing M times of interval sliding windows, and constructing a three-dimensional time-frequency power spectrum F (M, N) of time, frequency and power, wherein: m is the time dimension, n is the frequency dimension, and F (m, n) is the energy at that time and frequency point.
The invention has the beneficial effects that: by adopting the radar broadband interference identification method based on time-frequency power spectrum projection, the accurate classification of broadband aiming frequency interference, frequency sweep interference, broadband suppression interference and comb spectrum interference is realized, the automatic selection capability of anti-interference measures is effectively enhanced, and the survival capability and the operational efficiency of the radar in a complex electromagnetic environment are improved.
Drawings
Fig. 1 is a flow chart of a radar broadband interference identification method based on time-frequency power spectrum projection. Wherein: the S1-S5 in the figure correspond to the S1-S5 processes, respectively, as described in the summary of the invention.
Fig. 2 is a specific implementation method of each step in fig. 1. F (m, n) represents a three-dimensional time-frequency power spectrum of time, frequency and power, G (m, n) represents a binary time-frequency power spectrum, S (n) represents energy accumulation of frequency domain projection, Mo represents a second-order waveform moment, and C represents the number of residual condensation points.
Detailed Description
The implementation process of the present invention is shown in fig. 2, and is specifically described as the following process:
s1: constructing a three-dimensional time-frequency power spectrum: the time-frequency power spectrum can represent frequency domain and time slice information of all signals in the current channel bandwidth and can be used as an important basis for interference comprehensive classification identification and parameter extraction. According to the overall requirement of broadband identification data rate, intercepting MN-point-shared broadband echo signals at intervals, then performing Fourier transform on intermediate frequency data at every N points to obtain frequency spectrum information, performing M times of sequential sliding window sharing, arranging frequency spectrums according to time in sequence, and constructing three-dimensional time-frequency power spectrums F (M, N) of time, frequency and power, wherein: m is a time dimension and has a value range of not less than 1 and not more than M; n is a frequency dimension and is greater than or equal to 1 and less than or equal to N; f (m, n) is the energy at that point in time and frequency.
S2: and (3) time-frequency power spectrum binary segmentation: the time-frequency power spectrum mainly comprises interference information with higher energy and noise bottom information with lower energy, the time-frequency power spectrum energy segmentation threshold is solved, the extraction of the interference information is realized, and the formula is as follows:
according to the threshold value TrAnd performing binary segmentation on the time-frequency power spectrum to form a binary time-frequency power spectrum, wherein the formula is as follows:
wherein: those greater than the threshold are called object points, i.e., interference information; those smaller than the threshold are called background points, i.e., background information.
S3: object point frequency domain projection: different types of interference have obvious differentiability on the projection of a power spectrum frequency domain, sweep frequency interference and broadband suppression interference have frequency domain full coverage, and aiming frequency interference and comb spectrum interference only implement interference on limited frequency points. Therefore, all the object points are projected along the frequency domain, and the energy accumulation of the frequency domain projection is realized, and the formula is as follows:
counting non-zero numbers of energy accumulation S (N) on all frequency points, and performing step S4 for frequency sweep interference or broadband suppression interference when the number is greater than N/2; otherwise, the step S5 is performed for the frequency-aiming interference and the comb spectrum interference.
S4: calculating the second-order waveform moment of the projection energy: the statistical characteristic of the broadband suppression interference after frequency domain projection is still random noise, and the frequency domain projection of the sweep frequency interference is relatively stable. Therefore, the degree of undulation of the waveform can be classified, i.e. the second order moment, which defines the projection energy s (n), is defined as:
when the second-order waveform moment Mo is larger than 3, the broadband suppression interference is realized; otherwise, the interference is the frequency sweep interference.
S5: calculating the number of points after the condensation of projection energy: comb spectral interference is more energy blocks projected in the time-frequency power spectrum frequency domain than aiming frequency interference. The 3/5 criterion is used to realize the connection of the splitting points of the projection energy on the frequency domain, and the formula is as follows:
merging and condensing the communicated data Z (n), namely counting the number of data segments continuously being 1, removing the data segments of which the number is less than 4, wherein the number of the residual condensing points is C, and when C is more than 3, the comb spectrum interference is generated; otherwise, the signal is the frequency-aiming interference.
Claims (2)
1. A radar broadband interference identification method based on time-frequency power spectrum projection is characterized in that:
s1: constructing a three-dimensional time-frequency power spectrum F of the broadband echo signal;
s2: and (3) time-frequency power spectrum binary segmentation: solving the time-frequency power spectrum energy segmentation threshold value, wherein the formula is as follows:
wherein: m is the time domain sliding window times, N is the number of Fourier transform points, M is the time dimension, N is the frequency dimension, and F (M, N) is the energy on the time and frequency points; according to the threshold value TrAnd performing binary segmentation on the time-frequency power spectrum to form a binary time-frequency power spectrum, wherein the formula is as follows:
wherein: the point which is larger than the threshold value is called an object point, and the point which is smaller than the threshold value is called a background point;
s3: object point frequency domain projection: projecting all object points along a frequency domain, and realizing energy accumulation of frequency domain projection, wherein the formula is as follows:
counting non-zero numbers of energy accumulation S (N) on all frequency points, and when the number is more than N/2, performing step S4; otherwise, performing step S5;
s4: calculating the second-order waveform moment of the projection energy: the degree of waviness of the waveform can be determined by its second-order moment, which defines the projection energy s (n) as:
when the second-order waveform moment Mo is larger than 3, the broadband suppression interference is realized; otherwise, the interference is the frequency sweep interference;
s5: calculating the number of points after the condensation of projection energy: the 3/5 criterion is used to realize the connection of the splitting points of the projection energy on the frequency domain, and the formula is as follows:
merging and condensing the communicated data Z (n), namely counting the number of data segments continuously being 1, removing the data segments of which the number is less than 4, wherein the number of the residual condensing points is C, and when C is more than 3, the comb spectrum interference is generated; otherwise, the signal is the frequency-aiming interference.
2. The radar broadband interference identification method based on time-frequency power spectrum projection according to claim 1, is characterized in that: in said S1: performing Fourier transform on intermediate frequency data of every N points of the broadband echo signal to obtain frequency spectrum information of the broadband echo signal, and then sequentially performing sliding window interval for M times to construct a three-dimensional time-frequency power spectrum F of time, frequency and power.
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| CN111693944A (en) * | 2020-06-18 | 2020-09-22 | 上海志良电子科技有限公司 | Radar active interference signal parameter extraction and interference pattern identification method and device |
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| CN106330385B (en) * | 2016-08-29 | 2018-11-09 | 电子科技大学 | A kind of interference type recognition methods |
| CN109085614B (en) * | 2018-07-05 | 2020-10-20 | 南京航空航天大学 | GNSS interference feature identification method and system based on time-frequency spectrum value smoothing and segmentation |
| CN110412552B (en) * | 2019-07-20 | 2023-02-17 | 中国船舶重工集团公司第七二四研究所 | Interference sample self-adaptive selection side lobe cancellation method |
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| CN111693944A (en) * | 2020-06-18 | 2020-09-22 | 上海志良电子科技有限公司 | Radar active interference signal parameter extraction and interference pattern identification method and device |
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