CN114895289A - Joint detection method based on suppression type interference and target multi-dimensional difference characteristics - Google Patents
Joint detection method based on suppression type interference and target multi-dimensional difference characteristics Download PDFInfo
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- CN114895289A CN114895289A CN202210559190.5A CN202210559190A CN114895289A CN 114895289 A CN114895289 A CN 114895289A CN 202210559190 A CN202210559190 A CN 202210559190A CN 114895289 A CN114895289 A CN 114895289A
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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
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/52001—Auxiliary means for detecting or identifying sonar signals or the like, e.g. sonar jamming signals
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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
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/537—Counter-measures or counter-counter-measures, e.g. jamming, anti-jamming
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- 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 relates to a combined detection method based on suppression type interference and target multi-dimensional difference characteristics, for a passive sonar target, the method constructs a frequency domain focusing matrix to perform weighting processing on frequency domain matrix data to obtain a guiding power spectral density matrix; constructing an optimal weight coefficient for self-adaptive beam forming based on a guide minimum variance criterion to obtain self-adaptively weighted beam domain frequency domain data; and traversing all the beams, extracting multi-dimensional difference characteristics, and obtaining a joint detection result based on the multi-dimensional difference characteristics. The invention improves the anti-interference processing gain of the weak target under the suppression type interference, effectively reduces the blind area range of the suppression type interference, and greatly improves the detection efficiency of the passive sonar target under the high pressure type underwater sound anti-interference environment; the method is verified through simulation and sea test data, the weak target detection capability of the method under the suppression interference is obviously superior to that of the traditional energy detection method, and the detection blind area range under the suppression interference is effectively reduced.
Description
Technical Field
The invention relates to the technical field, belongs to the field of sonar signal processing, and particularly relates to a combined detection method based on suppression type interference and target multi-dimensional difference characteristics, which is suitable for weak target detection under underwater acoustic anti-interference.
Background
The suppression type underwater acoustic interference mainly suppresses and reduces the detection performance of sonar and torpedo on targets, is mainly provided with an underwater acoustic interference device and the like, has a difference between a high-frequency acoustic interference device and a low-frequency acoustic interference device according to the working frequency range, suppresses the normal work of the acoustic self-guidance of the sonar and the torpedo of the other party by transmitting strong power noise into water, and aims to weaken or destroy the acoustic detection performance and improve the survival capability of the naval vessel platform of the own party.
Meanwhile, the target detection technology under the underwater sound anti-interference is a processing method adopted for inhibiting/eliminating the influence of the underwater sound on the anti-interference. The development of the current underwater sound anti-interference technology can enable the detection performance of the existing sonar to be sharply reduced, and under the condition that the suppression type interference radiation noise energy is strong, background noise can be increased, a detection blind area with a large sector can be formed, the sonar action distance and the effective detection range can be greatly reduced, and the target position and distance isoparametric estimation precision is reduced.
In the prior art, a passive target detection method generally takes energy detection as a main part and is easily influenced by pressing type strong interference, and although the existing high-resolution self-adaptive detection method can inhibit the side lobe leakage problem of the strong interference, the problems of strong main lobe energy, wide wave beam and the like still exist, and the detection range is still limited.
Disclosure of Invention
The invention solves the problems in the prior art, provides an optimized joint detection method based on the suppression type interference and target multi-dimensional difference characteristics, and provides a detection method based on the suppression type interference device characteristics, wherein the detection method is provided by jointly utilizing the difference characteristics such as high-low frequency energy ratio characteristics, narrow-band line spectrum characteristics and spatial energy distribution based on the characteristics that the suppression type interference frequency band range is limited, the frequency spectrum is uniform and white, and low-frequency information cannot be covered.
The technical scheme adopted by the invention is that a combined detection method based on suppression type interference and target multi-dimensional difference characteristics is adopted, and for a passive sonar target, a frequency domain focusing matrix is constructed by the method to perform weighting processing on frequency domain matrix data to obtain a guiding power spectral density matrix; constructing an optimal weight coefficient for self-adaptive beam forming based on a guide minimum variance criterion to obtain self-adaptively weighted beam domain frequency domain data; and traversing all the beams, extracting multi-dimensional difference characteristics, and obtaining a joint detection result based on the multi-dimensional difference characteristics.
Preferably, constructing the frequency domain focus matrix comprises the steps of:
step 1.1: computing array steering vector a n (f k ,θ)；
Step 1.2: carrying out phase compensation on the frequency domain array data of each channel by using the array guide vector to obtain a compensated array output matrix Y (f) k ,θ)。
Preferably, in said step 1.1,
wherein, a n (f k Theta) is the processing frequency f of the nth array element k And a direction vector at the scanning azimuth theta, wherein N is 1,2, …, N and N are the total array element number of the linear arrays, d is the array element interval, c is the sound velocity, f is the sound velocity k ∈[fL,fH]fL and fH are the upper and lower limits of the processing frequency band, and j is an imaginary number;
in said step 1.2, Y (f) k ,θ)＝T Η (f k ,θ)X(f k )，
Wherein the content of the first and second substances,
to correspond to f k Diagonal array formed by array popular vectors corresponding to frequency points and theta directions, X (f) k ) Is frequency domain array data, (.) Η Is a complex matrix conjugate transpose.
Preferably, the steering power spectral density matrix is
Wherein R (f) k ) Is f k The covariance matrix of the frequency points is,
m is the fast beat number of the frequency domain array data, M is a positive integer from 1 to M, and K is the number of the total frequency points to be processed.
Preferably, the adaptive beamforming optimal weight coefficients are constructed based on a steering minimum variance criterion
Wherein the content of the first and second substances,
i is the inverse of the steering power spectral density matrix and is the unit matrix.
Preferably, the beam output is Y (f) k ,θ)＝w Η (θ)X(f k )。
Preferably, the multidimensional difference features include high and low frequency energy difference features, normalized narrow-band line spectrum feature vectors and spatial energy distribution features, the multidimensional feature joint detection result is a product of the multidimensional difference features, B F (θ)＝α eLH (θ)•α Fline (θ)•α Beam (θ)。
Preferably, the frequency domain signal Y (f) is based on the beam domain k Theta) of the high-frequency and low-frequency energy difference characteristics, alpha, by traversing all the beams eLH (θ)＝E L (θ)/E H (theta), wherein, low frequency energy
High frequency energy
f o Is the dividing line dividing the low frequency from the high frequency.
Preferably, the frequency domain signal Y (f) is based on the beam domain k Theta), traversing all wave beams, extracting narrow-band line spectrums to obtain the number of the narrow-band line spectrums of each wave beam, and obtaining the narrow-band line spectrums by using an efficacy coefficient methodNormalized narrowband line spectral feature vector alpha Fline (θ)＝c 1 +(N Fline (θ)-min(N Fline ))·d 1 /(max(N Fline )-min(N Fline ) Wherein c) is 1 ＝25、d 1 75 is the efficiency coefficient method parameter, N Fline And (theta) is a vector of the number of narrow-band line spectra, and max (-) and min (-) are functions of the maximum value and the minimum value respectively.
Preferably, the frequency domain signal Y (f) is based on the beam domain k θ), traverse all beams, obtaining spatial energy distribution features
The invention relates to an optimized joint detection method based on suppression type interference and target multi-dimensional difference characteristics, and for a passive sonar target, the method constructs a frequency domain focusing matrix to perform weighting processing on frequency domain matrix data to obtain a guiding power spectral density matrix; constructing an optimal weight coefficient for self-adaptive beam forming based on a guide minimum variance criterion to obtain self-adaptively weighted beam domain frequency domain data; and traversing all the beams, extracting multi-dimensional difference characteristics, and obtaining a joint detection result based on the multi-dimensional difference characteristics.
Compared with the traditional energy detection method, the method improves the anti-interference processing gain of the weak target under the suppression type interference, effectively reduces the blind area range of the suppression type interference, and greatly improves the detection efficiency of the passive sonar target under the high-pressure type underwater sound anti-interference environment; the method is verified through simulation and sea test data, the weak target detection capability of the method under the suppression interference is obviously superior to that of the traditional energy detection method, and the detection blind area range under the suppression interference is effectively reduced.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a multi-dimensional feature joint detection result of a simulation 64-element uniform linear array sonar with a spacing of 1.5m under suppression type interference;
fig. 3 is a data processing result of a passive target detection sea test under suppressed interference.
Detailed Description
The present invention is described in further detail with reference to the following examples, but the scope of the present invention is not limited thereto.
The invention relates to a joint detection method based on suppression type interference and target multi-dimensional difference characteristics, for a passive sonar target, the method constructs a frequency domain focusing matrix to carry out weighting processing on frequency domain matrix data to obtain a guiding power spectral density matrix; constructing an optimal weight coefficient for self-adaptive beam forming based on a guide minimum variance criterion to obtain self-adaptively weighted beam domain frequency domain data; and traversing all the beams, extracting multi-dimensional difference characteristics, and obtaining a joint detection result based on the multi-dimensional difference characteristics.
(1) Calculating an array steering vector:
a n (f k theta) is the processing frequency f of the nth array element k And a direction vector at the scanning azimuth theta, wherein N is 1,2, …, N and N are the total array element number of the linear arrays, d is the array element interval, c is the sound velocity, f is the sound velocity k ∈[fL,fH]fL and fH are the upper and lower limits of the processing band, j is an imaginary number, -j2 pi f k And ndcos theta/c is the phase information of the nth array element.
(2) Carrying out phase compensation on the frequency domain array data of each channel by using the array guide vector to obtain a compensated array output matrix Y (f) k ,θ)＝T Η (f k ,θ)X(f k )，
Wherein, (.) Η For complex matrix conjugate transpose, X (f) k ) For frequency domain array data, T (f) k θ) is a radical corresponding to f k A diagonal array formed by array popular vectors corresponding to the frequency points and the theta direction is called a focusing matrix and is defined as follows:
(3) focusing the frequency domain array data by using the focusing matrix to obtain a guiding power spectral density matrix,
wherein R (f) k ) Is f k The covariance matrix of the frequency points is,
wherein M is the fast beat number of the frequency domain data, M is a positive integer from 1 to M, and K is the number of the total frequency points.
(4) Computing optimal weight vectors and beam outputs for adaptive waveform recovery
Optimal weight vector
Beam output Y (f) k Theta) is Y (f) k ,θ)＝w Η (θ)X(f k )，
Wherein the content of the first and second substances,
i is the inverse of the steering power spectral density matrix and is the unit matrix.
(5) Based on wave beam domain frequency domain signal Y (f) k Theta) of the high-frequency and low-frequency energy difference characteristics, alpha, by traversing all the beams eLH (θ)＝E L (θ)/E H (θ)，
Wherein the low frequency energy
High frequency energy
f o Is the dividing line between the low frequency and the high frequency.
(6) Based on wave beam domain frequency domain signal Y (f) k Theta) traversing all wave beams, extracting narrow-band line spectrums to obtain the quantity of the narrow-band line spectrums of each wave beam, and obtaining a standardized narrow-band line spectrum characteristic vector alpha by using an efficacy coefficient method Fline (θ)＝c 1 +(N Fline (θ)-min(N Fline ))·d 1 /(max(N Fline )-min(N Fline ))，
Wherein c is 1 ＝25、d 1 75 is the efficiency coefficient method parameter, N Fline And (theta) is a vector of the number of narrow-band line spectra, and max (-) and min (-) are functions of the maximum value and the minimum value respectively.
(7) Based on wave beam domain frequency domain signal Y (f) k Theta), traverse all beams, obtain spatial energy distribution characteristics,
(8) jointly utilizing the difference characteristics of high and low frequency energy, the narrow-band line spectrum characteristics and the spatial energy distribution characteristics to obtain a multi-dimensional characteristic joint detection result, B F (θ)＝α eLH (θ)·α Fline (θ)·α Beam (θ)。
The embodiment of the invention is shown in figures 2 and 3.
As shown in fig. 2, the multi-dimensional feature joint detection result is obtained by simulating a 64-element uniform linear array sonar with a spacing of 1.5m under suppression type interference; the processing frequency is 20-500Hz, scanning is carried out at equal angles, the simulation target position is near 70 degrees, the input signal-to-noise ratio is-5 dB, the simulation pressing type interference position is near 80 degrees, and the input signal-to-noise ratio is 10 dB;
wherein, (a) is a spatial energy distribution characteristic, weak targets are in 70 ° azimuth, and are substantially suppressed by strong interference of 80 ° in spatial energy;
(b) the characteristic vector forms an obvious null in a strong interference direction for the high-low frequency energy ratio characteristic;
(c) is a narrow-band line spectrum characteristic;
(d) spatial spectrum results of the multi-dimensional feature joint detection;
(e) the azimuth process is detected for the traditional energy, wherein a weak target track is suppressed by strong interference side lobes and cannot be detected;
(f) for the multi-dimensional characteristic joint detection of the azimuth process, the combination of a graph (d) shows that strong interference is obviously suppressed, the detection output of a weak target is greatly enhanced, the signal-to-interference ratio is changed from-15 dB of input to 2dB, and the interference rejection ratio can be improved by 17 dB.
As shown in fig. 3, for the data processing result of the sea test for passive target detection under suppression type interference, the sonar array is a 128-element linear array with half-wavelength arrangement, and cosine scanning is performed at 0-180 degrees;
wherein, (a) is the result of conventional energy detection;
(b) for the traditional self-adaptive energy detection result, the suppression type interference is positioned in the 60-degree direction, the weak target is positioned in the 80-degree direction, and the comparison can be found that in the traditional energy detection result, the track of the weak target is completely covered, and particularly the side lobe influence of the strong interference of the conventional energy detection is more serious;
(c) the result is processed by a multi-dimensional characteristic joint detection method, so that the forced interference of the pressing mode can be obviously inhibited, the influence range of the blind zone is greatly reduced, and the track of a weak target is clear and distinguishable;
(d) for the frequency spectrum characteristics of the suppression type interference and the weak target, the energy of the suppression type interference can be found to be distributed in a medium-high frequency in a centralized manner, the low frequency cannot be covered, and no obvious line spectrum information exists;
(e) and (f) comparing the output energy of the pressing mode interference with the output energy of the weak target for the space spectrum snapshot results at different moments, and finding that the interference rejection ratio of the combined detection method based on the multidimensional characteristics reaches about 20dB, and the weak target detection capability is superior to that of the traditional energy detection method.
Claims (10)
1. A joint detection method based on suppressed interference and target multi-dimensional difference characteristics is characterized by comprising the following steps: for the passive sonar target, the method constructs a frequency domain focusing matrix to perform weighting processing on frequency domain matrix data to obtain a guiding power spectral density matrix; constructing an optimal weight coefficient for self-adaptive beam forming based on a guide minimum variance criterion to obtain self-adaptively weighted beam domain frequency domain data; and traversing all the beams, extracting multi-dimensional difference characteristics, and obtaining a joint detection result based on the multi-dimensional difference characteristics.
2. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 1, wherein: constructing the frequency domain focusing matrix comprises the following steps:
step 1.1: computing array steering vector a n (f k ,θ)；
Step 1.2: carrying out phase compensation on the frequency domain array data of each channel by using the array guide vector to obtain a compensated array output matrix Y (f) k ,θ)。
3. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 2, wherein: in the above-mentioned step 1.1,
wherein, a n (f k Theta) is the processing frequency f of the nth array element k And a direction vector at the scanning azimuth theta, wherein N is 1,2, …, N and N are the total array element number of the linear arrays, d is the array element interval, c is the sound velocity, f is the sound velocity k ∈[fL,fH]fL and fH are the upper and lower limits of the processing frequency band, and j is an imaginary number;
in said step 1.2, Y (f) k ,θ)＝T Η (f k ,θ)X(f k )，
Wherein the content of the first and second substances,
to correspond to f k Diagonal array formed by array popular vectors corresponding to frequency points and theta directions, X (f) k ) For frequency domain array data, (i) Η Is a complex matrix conjugate transpose.
4. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 3, wherein: the steering power spectral density matrix is
Wherein R (f) k ) Is f k The covariance matrix of the frequency points is,
m is the fast beat number of the frequency domain array data, M is a positive integer from 1 to M, and K is the number of the total frequency points to be processed.
5. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 4, wherein: construction of optimal weight coefficient for adaptive beamforming based on guide minimum variance criterion
Wherein the content of the first and second substances,
i is the inverse of the steering power spectral density matrix and is the unit matrix.
6. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 5, wherein: the beam output is Y (f) k ,θ)＝w Η (θ)X(f k )。
7. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 1, wherein: the multi-dimensional difference characteristics comprise high and low frequency energy difference characteristics, standardized narrow-band line spectrum characteristic vectors and space energy distribution characteristics, the multi-dimensional characteristic joint detection result is the product of the multi-dimensional difference characteristics, B F (θ)＝α eLH (θ)•α Fline (θ)•α Beam (θ)。
8. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 7, wherein: based on wave beam domain frequency domain signal Y (f) k Theta) of the high-frequency and low-frequency energy difference characteristics, alpha, by traversing all the beams eLH (θ)＝E L (θ)/E H (theta), wherein, low frequency energy
High frequency energy
f o Is the dividing line dividing the low frequency from the high frequency.
9. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 7, wherein: based on wave beam domain frequency domain signal Y (f) k Theta) traversing all wave beams, extracting narrow-band line spectrums to obtain the quantity of the narrow-band line spectrums of each wave beam, and obtaining a standardized narrow-band line spectrum characteristic vector alpha by using an efficacy coefficient method Fline (θ)＝c 1 +(N Fline (θ)-min(N Fline ))•d 1 /(max(N Fline )-min(N Fline ) Wherein c) is 1 ＝25、d 1 75 is the efficiency coefficient method parameter, N Fline And (theta) is a vector of the number of narrowband lines, and max (. eta.) and min (. eta.) are functions of the maximum and minimum values, respectively.
10. The joint detection method based on the suppressed interference and the target multi-dimensional difference characteristics according to claim 7, wherein: based on wave beam domain frequency domain signal Y (f) k θ), traverse all beams, obtaining spatial energy distribution features
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| CN117607876B (en) * | 2024-01-24 | 2024-04-19 | 汉江国家实验室 | Method and system for detecting passive sonar multi-beam narrowband signals |
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