CN113341380A - Target detection method based on subspace clutter cancellation in complex Gaussian clutter - Google Patents
Target detection method based on subspace clutter cancellation in complex Gaussian clutter Download PDFInfo
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- CN113341380A CN113341380A CN202110608323.9A CN202110608323A CN113341380A CN 113341380 A CN113341380 A CN 113341380A CN 202110608323 A CN202110608323 A CN 202110608323A CN 113341380 A CN113341380 A CN 113341380A
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- 238000001514 detection method Methods 0.000 title claims abstract description 38
- 230000003044 adaptive effect Effects 0.000 claims abstract description 21
- 238000007906 compression Methods 0.000 claims abstract description 21
- 230000006835 compression Effects 0.000 claims abstract description 20
- 239000011159 matrix material Substances 0.000 claims abstract description 19
- 238000001914 filtration Methods 0.000 claims abstract description 17
- 239000013598 vector Substances 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims abstract description 15
- 230000001427 coherent effect Effects 0.000 claims abstract description 14
- 238000012360 testing method Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000005070 sampling Methods 0.000 claims description 9
- 230000017105 transposition Effects 0.000 claims description 2
- 230000001629 suppression Effects 0.000 abstract description 8
- 238000007796 conventional method Methods 0.000 abstract description 3
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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
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
- G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
- G01S7/2927—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods by deriving and controlling a threshold value
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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
- G01S7/414—Discriminating targets with respect to background clutter
Abstract
The invention relates to a target detection method based on subspace clutter cancellation in complex Gaussian clutter, and belongs to the technical field of radar signal processing and target detection. The method comprises the following steps: 1) acquiring echo data which is not subjected to matched filtering processing; 2) for each distance unit, reorganizing the data matrix; 3) calculating a covariance matrix corresponding to each distance unit; 4) calculating a non-iterative adaptive pulse compression filter weight vector corresponding to each distance unit; 5) calculating a non-iterative adaptive pulse compression filtering output result corresponding to each distance unit; 6) calculating the test statistic corresponding to each distance unit; 7) and calculating the CFAR detection threshold corresponding to the false alarm probability. Compared with the conventional method of adaptive clutter suppression and target detection after matching and filtering, the detection method provided by the invention solves the problem of strong target high-distance side lobes which are easy to appear in the conventional pulse compression, realizes clutter suppression through coherent source elimination, and has CFAR capability.
Description
Technical Field
The invention belongs to the technical field of radar signal processing and target detection, and particularly relates to a target detection technology in radar target detection.
Background
Under the conditions of complex Gaussian clutter and transmitted coherent pulse trains, the fully adaptive radar clutter suppression and target detection technology usually needs a large amount of independent same-distribution distance unit echo data as training samples, and particularly under the observation condition of a digital array radar, the number of the required training samples is huge. Meanwhile, the performance of the conventional method of adaptive clutter suppression and target detection after matching and filtering is obviously reduced by a large side lobe formed during strong target echo pulse compression.
Aiming at the first problem, under the condition of limited number of training samples, a diagonal loading technology is often adopted to improve the detection performance of the radar. However, the diagonal loading level is a quantity which is difficult to determine, too small loading will not work, and too large loading will affect the suppression of clutter; meanwhile, the diagonal loading technology also needs a certain number of training samples, otherwise the performance of the technology is greatly reduced.
For the second problem, an iterative Adaptive Pulse Compression (APC) technique is usually used to suppress the range side lobe, but the iterative APC technique usually destroys the phase coherence between pulses and the statistically independent co-distribution property between training samples, so that the problem becomes more severe. This is also the reason that the detection performance obtained based on the iterative APC technique is not even as good as that of the non-adaptive matched filtering technique in the dense and strong clutter environment.
Aiming at the two problems, the invention provides a target detection method based on subspace clutter cancellation processing, which is suitable for a complex Gaussian clutter background.
Disclosure of Invention
The invention aims to provide a target detection method based on subspace clutter cancellation in complex Gaussian clutter, aiming at the problems of high training sample requirement and high strong target distance side lobe faced by clutter suppression and target detection in complex Gaussian clutter background, wherein the technical problems to be solved comprise:
(1) reconstructing echo data which is not subjected to matched filtering processing in a radar coherent pulse train observation mode into a data matrix form required by non-iterative adaptive pulse compression;
(2) in the adaptive pulse compression process, the energy of scattering points from other distance units is minimized while the energy of the scattering points from the current distance unit is kept unchanged;
(3) and outputting the designed CFAR problem of the test statistic based on the non-iterative adaptive pulse compression filtering.
The invention relates to a target detection method based on subspace clutter cancellation in complex Gaussian clutter, which is characterized by comprising the following technical measures:
(1) obtaining echo data which is not subjected to matched filtering processing in a radar coherent pulse train observation mode to obtain a K multiplied by L dimensional data matrix D;
(2) recombining and forming a corresponding N multiplied by K dimension data matrix Y (l) aiming at each distance unit;
(3) calculating a corresponding N multiplied by N dimensional covariance matrix C (l) for each distance unit;
(4) calculating a corresponding non-iterative adaptive pulse compression filter weight vector w (l) for each distance unit;
(5) aiming at each distance unit, calculating a corresponding non-iterative adaptive pulse compression filtering output result z (l);
(6) calculating a corresponding test statistic T (l) for each distance unit;
(7) for the test statistic T (l), the false alarm probability P is calculatedfaA corresponding CFAR detection threshold η;
(8) and comparing T (l) with eta to give a detection judgment result of each distance unit.
Compared with the conventional method of adaptive clutter suppression and target detection after matching and filtering, the target detection method based on subspace clutter cancellation in the complex Gaussian clutter has the advantages that:
(1) the method solves the problem of strong target high-distance side lobes which easily occur in conventional pulse compression by analogy of the problem of self-adaptive pulse compression to the problem of self-adaptive beam forming and referring to the concept of Capon, and overcomes the problem of insufficient detection capability of a radar on a small target caused by the fact that the side lobes of the strong target shield the small target;
(2) the method utilizes the Capon idea to construct a non-iterative self-adaptive pulse compression filter which has coherent source cancellation capability, namely utilizes the characteristic that clutter observation vectors in adjacent distance units form coherent sources due to the fact that the clutter observation vectors have the same subspace, and achieves the purpose of clutter cancellation, namely clutter suppression;
(3) the target detection method based on subspace clutter cancellation in the complex Gaussian clutter designed by the method has CFAR capability.
Drawings
FIG. 1 is a flow chart of an implementation of a target detection method based on subspace clutter cancellation in complex Gaussian clutter.
Detailed Description
The invention is described in further detail below with reference to the drawings. Referring to the attached figure 1 of the specification, the specific implementation mode of the invention comprises the following steps:
(1) acquiring echo data which is not subjected to matched filtering processing in a radar coherent pulse train observation mode;
the radar emits coherent pulse trains with pulse repetition frequency frThe number of pulses is K, the waveform of each transmitted pulse is the same, and the column vector s ═ s is used0,…,sN-1]TRepresenting, wherein N is the number of waveform sampling points, each sampling point corresponds to a distance resolution unit, and superscript T represents transposition; n and K satisfy the relation that N is less than K;
sampling the distance echo of each pulse according to the Nyquist sampling theorem to obtain a distance dimension sampling sequence with the length of L; thereby obtaining echo data under a radar coherent pulse train observation mode, using a K multiplied by L dimensional matrix D to represent the echo data,
(2) traversing all L in L-N +1 with the L being more than or equal to 1, recombining the echo data matrix Y (L) corresponding to the L-th distance unit in the following way to obtain each echo data matrix Y (L) corresponding to L, which is composed of N distance units adjacent to L and echo data of K pulses,
(3) traversing all L in L-N +1 with the L being more than or equal to 1, calculating an NxN dimensional covariance matrix C (L) corresponding to the L-th distance unit in the following way, obtaining a covariance matrix C (L) corresponding to each L, wherein the superscript H represents a conjugate transpose,
C(l)＝Y(l)YH(l)/K；
(4) traversing all L in L-N +1 which is more than or equal to 1, calculating the weight vector w (L) of the non-iterative adaptive pulse compression filter corresponding to the L-th distance unit in the following way to obtain the weight vector w (L) corresponding to each L,
the above formula uses the Capon idea in adaptive beam forming for reference, and for the adaptive pulse compression problem, the idea specifically means that the scattering point energy from other range units is minimized while the scattering point energy from the current range unit is kept unchanged; meanwhile, because the construction of the weight vector w (l) is based on the Capon idea, w (l) has the capability of coherent source cancellation; under the condition that the clutter in each distance unit meets the condition of statistical independence and same distribution, the clutter observation vector in each distance unit is in the same subspace, so that the clutter observation vector becomes a coherent source, and therefore, the weight vector w (l) of the non-iterative self-adaptive pulse compression filter has cancellation capacity on the subspace clutter in the distance unit;
(5) traversing all L in L-N +1 which is more than or equal to 1, calculating the output result z (L) of the non-iterative adaptive pulse compression filtering corresponding to the L-th distance unit in the following way to obtain the filtering output result z (L) corresponding to each L,
(6) traversing all L in L-N +1 which is more than or equal to 1 and less than or equal to L, calculating the test statistic T (L) corresponding to the L-th distance unit in the following way to obtain the test statistic T (L) corresponding to each L,
wherein, ctA doppler vector representing the object to be detected,
fdtis the Doppler value of the object to be detected, which is set at the grid point
Selecting one by one to obtain;
(7) for the test statistic t (l), the false alarm probability P is calculated as followsfaThe corresponding CFAR detection threshold η.
Under the condition that the clutter in each distance unit is complex Gaussian clutter which is statistically independent and distributed, the numerator of T (l) is subjected to central chi-square distribution with the degree of freedom of 2, the denominator is subjected to central chi-square distribution with the degree of freedom of 2(N-1), and then T (l) is subjected to central F distribution with the degrees of freedom of 2 and 2(N-1), so T (l) is CFAR, and the false alarm probability P isfaThe relationship with the CFAR detection threshold η is as follows,
given false alarm probability PfaThen the corresponding CFAR detection threshold eta is obtained by the above formula calculation, and the detection threshold eta is related to the distanceThe units are irrelevant, i.e. all L in L-N +1 are traversed, L is more than or equal to 1 and less than or equal to L, and the CFAR detection threshold corresponding to each distance unit is eta.
(8) Traversing all L in L-N +1 with L being more than or equal to 1, and giving a detection judgment result of each distance unit by comparing T (L) with eta; if T (l) is larger than or equal to eta, judging that the target exists in the distance unit, otherwise, judging that the target does not exist.
Claims (8)
1. The target detection method based on subspace clutter cancellation in complex Gaussian clutter is characterized by comprising the following steps:
s1, obtaining echo data which are not subjected to matched filtering processing in a radar coherent pulse train observation mode to obtain a K multiplied by L dimensional data matrix D;
s2, recombining each distance unit to form a corresponding N multiplied by K dimension data matrix Y (l);
s3, calculating a corresponding N multiplied by N dimensional covariance matrix C (l) for each distance unit;
s4, calculating a corresponding non-iterative adaptive pulse compression filter weight vector w (l) for each distance unit;
s5, calculating the corresponding non-iterative adaptive pulse compression filtering output result z (l) of each distance unit;
s6, calculating corresponding test statistic T (l) for each distance unit;
s7, aiming at the test statistic T (l), calculating the false alarm probability PfaA corresponding CFAR detection threshold η;
and S8, comparing T (l) with eta to give a detection judgment result of each distance unit.
2. The method for detecting a target based on subspace clutter cancellation in complex gaussian clutter according to claim 1, wherein the step S1 specifically comprises:
the radar emits coherent pulse trains with pulse repetition frequency frThe number of pulses is K, the waveform of each transmitted pulse is the same, and the column vector s ═ s is used0,…,sN-1]TExpress, N is the number of waveform samples, eachThe sampling point corresponds to a distance resolution unit, and the superscript T represents transposition; n and K satisfy the relation that N is less than K;
sampling the distance echo of each pulse according to the Nyquist sampling theorem to obtain a distance dimension sampling sequence with the length of L; thereby obtaining echo data under a radar coherent pulse train observation mode, using a K multiplied by L dimensional matrix D to represent the echo data,
3. the method for detecting a target based on subspace clutter cancellation in complex gaussian clutter according to claim 1, wherein the step S2 specifically comprises:
traversing all L in L-N +1 with the L being more than or equal to 1, recombining the echo data matrix Y (L) corresponding to the L-th distance unit in the following way to obtain each echo data matrix Y (L) corresponding to L, which is composed of N distance units adjacent to L and echo data of K pulses,
4. the method for detecting a target based on subspace clutter cancellation in complex gaussian clutter according to claim 1, wherein the step S3 specifically comprises:
traversing all L in L-N +1 with the L being more than or equal to 1, calculating an NxN dimensional covariance matrix C (L) corresponding to the L-th distance unit in the following way, obtaining a covariance matrix C (L) corresponding to each L, wherein the superscript H represents a conjugate transpose,
C(l)＝Y(l)YH(l)/K。
5. the method for detecting a target based on subspace clutter cancellation in complex gaussian clutter according to claim 1, wherein the step S4 specifically comprises:
traversing all L in L-N +1 which is more than or equal to 1, calculating the weight vector w (L) of the non-iterative adaptive pulse compression filter corresponding to the L-th distance unit in the following way to obtain the weight vector w (L) corresponding to each L,
6. the method for detecting a target based on subspace clutter cancellation in complex gaussian clutter according to claim 1, wherein the step S5 specifically comprises:
traversing all L in L-N +1 which is more than or equal to 1, calculating the output result z (L) of the non-iterative adaptive pulse compression filtering corresponding to the L-th distance unit in the following way to obtain the filtering output result z (L) corresponding to each L,
7. the method for detecting a target based on subspace clutter cancellation in complex gaussian clutter according to claim 1, wherein the step S6 specifically comprises:
traversing all the L-N +1 with the value of 1-L, calculating the test statistic T (L) corresponding to the L-th distance unit in the following way to obtain the test statistic T (L) corresponding to each L,
wherein, ctA doppler vector representing the object to be detected,
fdtis the Doppler value of the object to be detected, which is set at the grid point
And selecting one by one to obtain.
8. The method for detecting a target based on subspace clutter cancellation in complex gaussian clutter according to claim 1, wherein the step S7 specifically comprises:
according to false alarm probability PfaA relation with the CFAR detection threshold η,
when the false alarm probability P is givenfaAnd then, calculating to obtain a corresponding CFAR detection threshold eta, wherein the detection threshold eta is irrelevant to the distance units, namely, traversing all L in L-N +1, wherein L is more than or equal to 1, and the CFAR detection threshold corresponding to each distance unit is eta.
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