CN107229040B - high-frequency radar target detection method based on sparse recovery space-time spectrum estimation - Google Patents
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- 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
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Abstract
The invention provides a novel high-frequency ground wave radar target detection method. Aiming at the insufficient utilization of space-time two-dimensional information by a space-time cascade scheme, a space-time joint estimation theory is introduced to optimize a detection process. Aiming at the problem of insufficient snapshot number in space-time joint estimation, a multi-snapshot sparse recovery theory is introduced. The invention establishes a set of method for detecting the target of the wide-beam high-frequency ground wave radar based on sparse recovery space-time spectrum estimation, solves the technical problem of detecting the target in the sea clutter area of the single-station high-frequency ground wave radar, and simultaneously optimizes the multi-target detection performance under the dense ship background.
Description
Technical Field
The invention belongs to the field of radars, particularly relates to a high-frequency radar first-order sea clutter region target detection and multi-target detection enhancement technology, and particularly relates to a high-frequency radar target detection method based on sparse recovery space-time spectrum estimation.
Background
As a new ocean monitoring technology, the high-frequency ground wave radar has the advantages of over-the-horizon, large range, all weather, low cost and the like, and is considered to be a high-tech means capable of effectively monitoring exclusive economic areas of all countries. Research and development investment is carried out in various developed countries, and comparison verification and application demonstration are carried out for years. Marine surveillance is not only the protection of ownership, but also search and rescue (horse navigation events), traffic management, and the like. Conventional monitoring approaches suffer from a number of physical limitations. For example, a shore-based microwave radar can only propagate along a line of sight, and the detection distance is greatly limited. The time resolution of satellite sensors (such as synthetic aperture radar) is low and all-weather observation cannot be achieved. The high-frequency ground wave radar has obvious advantages in marine supervision compared with the traditional means.
However, the detection of small and medium-sized ships is directly influenced by the serious sea clutter interference of the high-frequency radar, and even the target echo is completely covered. Especially for a small array wide-beam radar, complex sea clutter signals cannot be effectively suppressed through a simple method, so that clutter region targets are difficult to effectively detect. In order to enhance the detection capability of the radar for the targets in the clutter region and reduce the condition of a large number of missed detections of the targets, there are generally two processing methods: 1) multi-station mode, j.hostmann and roarty.h et al, observe in different directions through multiple sets of radar systems,
Detecting a target by means of Doppler frequency shift change caused by a viewing angle, which is detailed in an article [1 ];
2) in a multi-frequency mode, d.m. fernandez et al, a single set of radar multiband works simultaneously, and a target is detected by relying on the difference in the relationship between doppler shift, first-order sea clutter shift and wavelength, as described in article [2 ].
however, the above methods all require improvement of the system, and enhance the detection performance by increasing the number of station points or frequency points. To reduce costs, it is desirable to explore ways to optimize probing performance without increasing complexity. For target detection in a sea clutter region, a detection method based on time-frequency modulation characteristic difference of a target and clutter is provided by the key [3] and the like in China and Thayaparan [4] and the like in Canada, and a certain effect is achieved.
[1]S.Maresca,P.Braca,J.Horstmann et al.,“Maritime Surveillance Using Multiple High-Frequency Surface-Wave Radars,”IEEE Trans.Geosci.Remote.,vol.52,no.8,pp.5056-5071,Aug,2014.
[2]D.M.Fernandez,J.F.Vesecky,D.E.Barrick et al.,“Detection of Ships with Multi-Frequency and CODAR SeaSonde HF Radar Systems,”Can J Remote Sens,vol.27,no.4,pp.277-290,2001.
[3]GUAN J,CHEN X L,HUANG Y,et al.Adaptive fractional Fourier transform-based detection algorithm for moving target in heavy sea clutter[J].IET Radar Sonar Nav.,2012,6(5):389-401.
[4]ZHANG Y,QIAN S,THAYAPARAN T.Detection of a manoeuvring air target in strong sea clutter via joint time-frequency representation[J].IET Signal Process.,2008,2(3):216-22.
however, the method still has defects, and for the high-frequency ground wave radar, the space-time two-dimensional distribution of the sea clutter which causes serious interference to target detection is different from that of the targets except for the long-time modulation characteristics, so that the method has an important effect of optimizing the detection of the targets in the sea clutter area.
disclosure of Invention
Aiming at the defects of the prior art, the invention provides a high-frequency radar target detection method based on sparse recovery space-time spectrum estimation, fills the technical blank of detecting the targets in the sea clutter region under the conditions of not increasing the system complexity and not relying on prior information, and solves the technical problem of multi-target detection in the dense ship scene.
the invention is realized by adopting the following technical scheme:
Step 1, determining the resolution scale of a space domain and a Doppler dimension, dividing a space-time two-dimensional plane into P x Q grids, calculating a space-time steering vector on each grid, and generating a space-time steering dictionary phi, wherein the space-time steering vectorAnd the space domain steering vector a (theta)n) And a Doppler steering vector aτ(ωn) Is in the structural relationship of is kronecker product;
Step 2, carrying out first FFT on the high-frequency radar echo data, and selecting continuous M sampling points of a distance unit to be detected; by forming a frame every N points, spreading and straightening according to channelsA space-time snapshot matrix Y of the spatial snapshots,Represents rounding down;
And 3, carrying out sparse recovery on the multi-fast-beat data in the step 2 by using an SPGL1 algorithm toolkit, and solving an optimization problem:
Wherein, Representing D x K dimensional matrix SxThe ith row of (1) | · | | non-conducting phosphor2To representNorm, | · | luminance2,1To representThe mixed norm, epsilon, is the noise variance;
step 4, obtaining the joint distribution SxAnd then reconstructing a space-time covariance matrix, wherein the calculation method comprises the following steps:
Wherein phiirepresenting correspondences in space-time-oriented dictionariesH is a conjugate transposed symbol;
Step 5, singular value decomposition is carried out on the reconstructed covariance matrix,
Wherein U is left singular vector, S characteristic value distribution, VHSorting S for the distribution of the right eigenvalue, and then eliminating the signal subspace corresponding to the significant eigenvalue through the estimation of the number of information sourcesTo obtain a noise subspace estimatenamely have
Step 6, obtaining an azimuth Doppler spectrum P based on a two-dimensional MUSIC algorithm through space-time two-dimensional scanningMU(θ,ω):
wherein, aθωIs a space-time guide vector; finally, the target is detected through a constant false alarm technology, namely, the noise level sigma is estimated by searching the lowest point on two-dimensional space-time distribution2Setting false alarm rate to 10-8And setting the noise distribution as Rayleigh distribution, so as to adaptively obtain a decision threshold, and simultaneously obtain corresponding Doppler and azimuth estimation values by detecting the target.
In step 2, in order to ensure the signal stability in a single space-time snapshot, the value of N is 32 or 64, and the number of samples of M is 256 or 512.
In step 3, for the estimation of the noise level epsilon, firstly, the total energy E is obtained for the time domain data, and then the noise level is calculated according to epsilon as max (E)/20; in coarse computation of the sparse distribution, the iteration stop threshold is chosen to be 10-5when fine search is required, the stop threshold may be set to 10-6。
In step 5, the method for rapidly determining the number of the information sources comprises the following steps: first the peak value p is determinedmaxthen 1/20 is taken for its magnitude, and if the second characteristic value is greater than that value, the iteration continues from the second characteristic valueand continuously judging the next characteristic value until the condition is not met, wherein the number of all the characteristic values meeting the condition is the number of the information sources.
Compared with the prior art, the invention has the following advantages and effects:
the invention fully excavates the space-time two-dimensional characteristic difference of the target signal and the clutter signal under the condition of not increasing the number of radar systems or the complexity of equipment, and realizes the target detection and multi-target distinguishing in the sea clutter area. The method has great military value and civil navigation management value, and can directly improve the application capability of high-frequency radar systems which are arranged in large numbers at the coast in the aspects of coast early warning and ship management. When the target detection method in the scheme is not adopted, the conventional method cannot detect the targets in the sea clutter area, so that a quite large detection blind area is formed, the target detection probability is low, the problems of accumulation time and resolution are solved, and the small array high-frequency radar cannot effectively work in a dense multi-target scene. The method integrates the advantages of space-time two-dimensional estimation and sparse recovery, realizes effective estimation on the space-time spectrum under the condition of a small sample, enhances the target detection capability of the high-frequency radar, has great application potential, and has great application value in the aspects of military affairs, sea prison, fishery management and the like.
drawings
FIG. 1 is a simulated echo signal data, time Doppler spectrum, of an embodiment of the present invention.
fig. 2 shows the result of classical spatio-temporal super-resolution estimation of simulated echo according to an embodiment of the present invention.
FIG. 3 shows the result of space-time super-resolution estimation with echo sparse recovery simulation according to an embodiment of the present invention.
FIG. 4 is a comparison of simulated echo covariance matrix eigenvalue distributions at different fast beat numbers and sparse recovery according to an embodiment of the present invention.
FIG. 5 is a time Doppler spectrum of measured echo data with aliasing of the target and sea clutter according to an embodiment of the invention.
FIG. 6 shows a classical space-time super-resolution estimation result of measured echo data according to an embodiment of the present invention.
FIG. 7 shows the result of sparse recovery space-time super-resolution estimation of measured echo data according to an embodiment of the present invention.
Fig. 8 is a detection tracking comparison result of the measured echo data processed by the classical cascade scheme and the sparse processing according to the embodiment of the present invention.
FIG. 9 is a time Doppler spectrum of measured data from a multi-target detection radar in accordance with an embodiment of the present invention.
FIG. 10 shows a classical space-time two-dimensional estimation result of multi-target radar measured data according to an embodiment of the present invention.
FIG. 11 is a space-time two-dimensional estimation result of sparse recovery of multi-target radar measured data according to an embodiment of the present invention.
FIG. 12 is an overall flow chart of the method of the present invention.
Detailed Description
The invention provides a target detection method based on space-time two-dimensional joint estimation, mainly based on a space-time two-dimensional spectrum estimation and multi-snapshot sparse recovery theory, considering the difference of space-time two-dimensional distribution of a target signal and a sea clutter signal in Doppler radar echo, and integrates the multi-snapshot sparse recovery theory and designs an actual operation system aiming at the problem that the snapshot number is small due to target maneuvering characteristics in practical application. The method fully considers the space-time characteristics of the target signal, enhances the effectiveness of space-time spectrum estimation through sparse recovery, and fills up the technical blank of detecting the target in the first-order spectral region of the high-frequency radar under the condition of not increasing the complexity of the system. The system is also beneficial to monitoring and management of dense multi-target scenes such as port and channel.
The method provided by the invention can realize the flow by using the computer software technology as shown in figure 12.
Example 1:
Taking target detection in a first-order sea clutter area of a high-frequency radar as an example, the advantage of sparse recovery-based space-time spectrum estimation on target detection is explained, a simulated target signal is shown in fig. 1, the target signal is difficult to detect in a time Doppler spectrum, when a classical two-dimensional MUSIC algorithm is used for carrying out two-dimensional space-time spectrum estimation, the result is shown in fig. 2, due to the fact that fast beat numbers are insufficient, covariance matrix statistics is insufficient, and the target signal is difficult to form an effective aggregation area. To solve this problem, the subsequent processing is as follows:
Step 1, carrying out grid division on a spatial dimension according to 1 degree, wherein the scanning range is-60 degrees, and the Doppler dimension is divided according to 0.01Hz, the scanning range is the whole Doppler sampling frequency band, and a space-time guide vector dictionary is formed through the step 1 in the technical scheme;
step 2, selecting 256 points of time domain sampling of the distance unit to be detected, wherein each 32 points form a space-time snapshot, each space-time snapshot is 256-dimensional due to the fact that the number of array channels is 8, and a 256 x 8 space-time snapshot matrix is formed in total;
step 3, setting the iterative optimization stop condition as 10-5The noise level is 1/20 of the peak value, and then the sparse recovery optimization problem is solved to obtain a sparse solution;
Step 4, reconstructing a space-time covariance matrix by using sparse solution;
Step 5, estimating the number of information sources through singular value distribution characteristics, and constructing a noise subspace;
And 6, estimating the space-time spectrum according to the step 6 of the technical scheme, wherein the result is shown in fig. 3, the target signal can be seen to form an obvious aggregation area, the signal-to-noise ratio is greatly increased, and the target detection is favorably carried out.
fig. 4 is a comparison of the eigenvalue distribution of the sparse recovery covariance matrix under 8 snapshots and the distribution under 8 snapshots and 32 snapshots, and it can be seen that after sparse recovery, the eigenvalue distribution under 8 snapshots is equivalent to the 32 snapshot estimation result, and direct use of 8 snapshot estimation has an obvious rank lacking problem, and cannot effectively estimate the noise subspace.
Example 2:
taking target detection in a first-order sea clutter region of a high-frequency radar as an example, the advantage of sparse recovery-based space-time spectrum estimation on target detection is explained, actually-measured echo data is shown in fig. 5, a dotted line is target AIS data, and when a space-time cascade scheme is adopted, a target is difficult to detect only from a time Doppler spectrum because the target is masked by sea clutter. When two-dimensional estimation is performed according to the classical MUSIC algorithm, the result is shown in fig. 6, and the target signal is difficult to form an effective aggregation area. The target is detected by sparse recovery space-time spectrum estimation, and the processing procedure is as follows:
Step 1, selecting 256 points of time domain sampling of a distance unit to be detected, wherein each 32 points form a space-time snapshot, each space-time snapshot is 256-dimensional due to the fact that the number of array channels is 8, and a 256 x 8 space-time snapshot matrix is formed in total;
Step 2, carrying out grid division on the spatial dimension according to 1 degree, wherein the scanning range is-60 degrees, carrying out division on the Doppler dimension according to 0.01Hz, and scanning the whole Doppler sampling frequency band;
Step 3, setting the iterative optimization stop condition as 10-5The noise level is 1/20 of the peak value, and then the sparse recovery optimization problem is solved to obtain a sparse solution;
Step 4, reconstructing a space-time covariance matrix by using sparse solution;
Step 5, estimating the number of information sources through singular value distribution characteristics, and constructing a noise subspace;
and 6, processing and estimating the space-time spectrum according to the MUSIC algorithm estimation process, wherein the result is shown in FIG. 7, the target signal can form an obvious aggregation area, the signal-to-noise ratio is greatly increased and exceeds 20dB, and the target detection is favorably carried out.
the actual measurement echoes are subjected to continuous detection tracking statistical processing, as shown in fig. 8, a solid line of points is a detection result of sparse recovery space-time spectrum estimation, a solid line represents AIS data of a target outside a first-order sea clutter area, and a dotted line represents AIS data of the target in the first-order sea clutter area.
Example 3:
The process of the invention is specifically explained by multi-target detection under a dense scene, the actual measurement data time Doppler spectrum refers to fig. 9, when two-dimensional space-time spectrum estimation is directly carried out on multi-target echo data according to a classical MUSIC algorithm, the result is shown in fig. 10, only two targets can be detected, the signal-to-noise ratio is low and is only about 5dB, the spectrum peak is not sharp enough, and effective detection and distinguishing of multiple targets are difficult to carry out. To optimize detection performance, the following is processed:
Step 1, selecting 256 points of time domain sampling of a distance unit to be detected, wherein each 32 points form a space-time snapshot, each space-time snapshot is 256-dimensional due to the fact that the number of array channels is 8, and a 256 x 8 space-time snapshot matrix is formed in total;
step 2, carrying out grid division on the spatial dimension according to 1 degree, wherein the scanning range is-60 degrees, and the Doppler dimension is divided according to 0.01Hz, and the scanning range is the whole Doppler sampling frequency band;
Step 3, setting iterative optimization stop conditions to 10 in order to enhance the recovery performance under the multi-target scene-6The noise level is 1/25 of the peak value, and then the sparse recovery optimization problem is solved to obtain a sparse solution;
Step 4, reconstructing a space-time covariance matrix by using sparse solution;
Step 5, estimating the number of information sources through singular value distribution characteristics, and constructing a noise subspace;
And step 6, estimating a space-time spectrum according to a classical MUSIC algorithm flow, wherein a new result is shown in fig. 11, so that target signals form obviously different aggregation areas, the signal-to-noise ratio is greatly increased and approaches to 45dB, a low signal-to-noise ratio target is effectively estimated, and the detection performance is improved.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (4)
1. A high-frequency radar target detection method based on sparse recovery space-time spectrum estimation is characterized by comprising the following steps:
step 1, determining the resolution scale of a space domain and a Doppler dimension, dividing a space-time two-dimensional plane into P × Q grids, and calculating the space-time pilot on each gridGenerating a space-time oriented dictionary phi from the vector, wherein the space-time oriented vectorAnd the space domain steering vector a (theta)n) And a Doppler steering vector aτ(ωn) Is in the structural relationship of Is kronecker product;
step 2, carrying out first FFT on the high-frequency radar echo data, and selecting continuous M sampling points of a distance unit to be detected; by forming a frame every N points, spreading and straightening according to channelsa space-time snapshot matrix Y of the spatial snapshots,Represents rounding down;
and 3, carrying out sparse recovery on the multi-fast-beat data in the step 2 by using an SPGL1 algorithm toolkit, and solving an optimization problem:
wherein, Representing D x K dimensional matrix SxThe ith row of (1) | · | | non-conducting phosphor2Is represented by2Norm, | · | luminance2,1Is represented by2,1The mixed norm, epsilon, is the noise variance;
step 4, obtaining the combined scoreCloth Sxand then reconstructing a space-time covariance matrix, wherein the calculation method comprises the following steps:
Wherein phiirepresenting correspondences in space-time-oriented dictionariesh is a conjugate transposed symbol;
Step 5, singular value decomposition is carried out on the reconstructed covariance matrix,
wherein U is left singular vector, S characteristic value distribution, VHSorting S for the distribution of the right eigenvalue, and then eliminating the signal subspace corresponding to the significant eigenvalue through the estimation of the number of information sourcesTo obtain a noise subspace estimateNamely have
step 6, obtaining an azimuth Doppler spectrum P based on a two-dimensional MUSIC algorithm through space-time two-dimensional scanningMU(θ,ω):
wherein, aθωIs a space-time guide vector; finally, the target is detected through a constant false alarm technology, namely, the noise level sigma is estimated by searching the lowest point on two-dimensional space-time distribution2Setting false alarm rate to 10-8And setting the noise distribution as Rayleigh distribution, so as to adaptively obtain a decision threshold, and simultaneously obtain corresponding Doppler and azimuth estimation values by detecting the target.
2. the high-frequency radar target detection method based on sparse recovery space-time spectrum estimation according to claim 1, characterized in that: in the step 2, in order to ensure the signal stationarity in a single space-time snapshot, the value of N is 32 or 64, and the number of samples of M is 256 or 512.
3. The high-frequency radar target detection method based on sparse recovery space-time spectrum estimation according to claim 1, characterized in that: in the step 3, for the estimation of the noise level epsilon, firstly, the total energy E is obtained for the time domain data, and then the noise level is calculated according to epsilon as max (E)/20; in coarse computation of the sparse distribution, the iteration stop threshold is chosen to be 10-5When fine search is required, the stop threshold is set to 10-6。
4. the high-frequency radar target detection method based on sparse recovery space-time spectrum estimation according to claim 1, characterized in that: in step 5, the method for quickly determining the number of the information sources comprises the following steps: first the peak value p is determinedmaxAnd then 1/20 is taken for the amplitude, if the second characteristic value is larger than the value, iteration is continued from the second characteristic value, and the next characteristic value is continuously judged until the condition is not met, and the number of all the characteristic values meeting the condition is the number of the information sources.
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| CN110531311A (en) * | 2019-08-27 | 2019-12-03 | 武汉大学深圳研究院 | A kind of LTE external illuminators-based radar DOA estimation method based on matrix recombination |
| CN111505590A (en) * | 2020-04-07 | 2020-08-07 | 武汉大学 | High-frequency ground wave radar channel calibration method and system |
| CN111796253B (en) * | 2020-09-01 | 2022-12-02 | 西安电子科技大学 | Radar target constant false alarm detection method based on sparse signal processing |
| CN118276014B (en) * | 2022-12-30 | 2024-11-08 | 珠海正和微芯科技有限公司 | Sparse step frequency radar distance spectrum layered virtual estimation method, device and storage medium |
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