CN112906476B - Airborne radar training sample selection method based on signal-to-noise-ratio loss - Google Patents
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Abstract
The invention belongs to the field of airborne radar signal processing, and particularly provides a method for selecting an airborne radar training sample based on signal-to-noise ratio loss, which is used for eliminating the training sample inconsistent with the target distance ring clutter characteristic and ensuring the uniformity of the training sample used for clutter covariance matrix estimation in airborne radar space-time adaptive processing. Compared with the traditional method, the method has the advantages that the clutter characteristics of the target distance ring and the training sample are directly represented by the aid of the sub-aperture covariance matrix, and the representation of the clutter characteristics is not influenced by other samples. In order to eliminate the influence of a target signal, the target distance ring possibly containing target signal components is eliminated in a Capon spectrum integral reconstruction-based mode, and compared with an orthogonal projection mode, clutter characteristics can be better reserved. The method adopts the output signal-to-noise ratio loss as the test statistic, and directly represents the approximation degree of the current sample and the CUT clutter characteristic.
Description
Technical Field
The invention belongs to the field of airborne radar signal processing, and particularly relates to an airborne radar training sample selection method which is used for eliminating training samples which are inconsistent with the characteristics of target distance ring clutter and ensuring the uniformity of training samples used for clutter covariance matrix estimation in airborne radar space-time adaptive processing.
Background
The space-time adaptive processing technology is an effective means for detecting a slow and small ground target under a strong clutter background by an airborne radar, and the key of the space-time adaptive processing lies in the estimation of a clutter covariance matrix of a target distance ring. However, in an actual application scenario, due to the influence of various non-ideal factors, such as a discrete strong scattering point, an interference target signal, intra-clutter motion, terrain variation, weather influence, and the like, the clutter characteristic of the training sample may be inconsistent with the target distance ring, and the estimation accuracy of the clutter covariance matrix of the target distance ring by using the training sample is reduced, so that the space-time adaptive processing performance is reduced, and the detection performance of the airborne radar on the ground target is deteriorated. Therefore, the original training samples are screened, the training samples which are inconsistent with the target distance ring clutter characteristic are removed, and the method has important significance for improving the detection performance of the airborne radar to the ground moving target.
At present, the existing airborne radar training sample selection method is mainly based on a generalized inner product, waveform similarity, terrain data and a sub-aperture covariance matrix. For example, in the document "Tang B, tang J, pen y.detection of coherent samples based on loaded generated inner product method [ J ]. Digital Signal Processing,2012,22 (4): 605-613", an airborne radar training sample selection method based on generalized inner product is disclosed, wherein the original training sample is used to calculate sample covariance matrix to characterize clutter characteristics of a target distance ring, and then the generalized inner product of each training sample is calculated as test statistic to perform sample screening; the technique uses a sample covariance matrix calculated from the original training samples, but does not take into account clutter characteristics of the target range rings, when the clutter characteristics of most samples are not consistent with the target range rings, the selected samples will not have uniformity with the target range rings.
For example, in the document "Yifeng W, tong W, jianxin W, et al, robust tracking sampling selection al, high basis on spectral similarity for space-time adaptive processing in noise in terms of noise similarity [ J ]. Radio Source & Navigation Iet,2015,9 (7): 778-782", an airborne Radar training sample selection method based on waveform similarity is disclosed, and a sample screening process is completed by comparing the waveform similarity of a sample signal and a target distance ring signal in a time domain or a frequency domain and removing training samples with larger differences; the fundamental purpose of the Training sample Selection is to select the Training sample with the same covariance matrix as the target range ring, and theoretical analysis in the document "Li H, bao W, hu J, et al.a Training Samples Selection Method Based on System Identification for STAP [ J ]. Signal Processing,2017,142 (jan.): 119-124" proves that even the Training Samples with completely different waveforms can have the same covariance matrix, so the Method can seriously reduce the utilization rate of the Training Samples, resulting in the loss of effective Training Samples.
For example, in documents "Capraro C, capraro G, bradic I, et al, imaging digital terrain data I n knowledge-aided space-time adaptive processing [ J ]. IEEE Transactions on Aerospace & electronic Systems,2006,42 (3): 1080-1099", an airborne radar training sample selection method based on terrain data is disclosed, which assumes that when a training sample and a target distance ring have similar terrain, an echo signal of the training sample and the target distance ring have a consistent covariance matrix, so that the terrain data is used to screen the training sample having similar terrain to the target distance, and the whole sample screening process is completed; the method requires accurate matching of terrain data and radar echo signals, which is difficult to meet in practical application scenarios, and radar echo signals are affected by the irradiation angle under the same terrain, which is not considered at all in a method based on terrain data, so that the accuracy of screened samples can be seriously reduced.
For example, in the document "Wu Y, wang T, wu J, et al. Conveying Sample Selection for Space-Time A significant Processing in Heterogeneous environment [ J ]. IEEE Geoscience and Remote sensing letters,2014,12 (4): 691-695", a method for selecting a training Sample for an airborne radar based on a subaperture covariance matrix is disclosed, which is realized by comparing the difference between a target distance ring and the subaperture covariance matrix of the training Sample to eliminate the non-uniform training Sample, and the specific method is to calculate the nearest norm through the subtraction of two matrices; according to the method, in order to remove target components possibly existing in a target distance ring, orthogonal projection is carried out on target distance ring signals in an orthogonal subspace of a target guide vector, so that the target components are removed, the target guide vector cannot be orthogonal to a clutter subspace, clutter signal characteristics in the target distance ring are seriously damaged by adopting the orthogonal projection mode, so that the clutter characteristics of a screened training sample and the actual clutter characteristics of the target distance ring have larger deviation, and when the difference between the target distance ring and a training sample sub-aperture covariance matrix is measured by adopting a Frobenius norm, the Frobenius norm cannot accurately represent the approximation degree of the two covariance matrices, so that the screening efficiency is low.
Disclosure of Invention
The invention aims to provide a method for selecting training samples of an airborne radar based on signal-to-noise-ratio loss, aiming at the problems of the existing methods for selecting the training samples of the airborne radar; compared with the traditional method, the clutter suppression method has the advantages that the reconstructed sub-aperture covariance matrix is used for directly representing the clutter characteristic of the target distance ring, the accuracy is higher, then the space-time adaptive filter designed by the sub-aperture covariance matrix of the sample distance ring is used for processing the target distance ring signal, the loss of the output signal-to-noise ratio is used as the test statistic, the clutter suppression capability of the selected sample on the target distance ring during space-time adaptive processing is directly measured, and the corresponding training sample is removed when the value of the test statistic is lower than the screening threshold.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for selecting training samples of an airborne radar based on signal-to-noise ratio loss is characterized by comprising the following steps:
setting the number of array elements of the airborne radar as N and the number of transmitted pulses in the coherent processing interval as M, and obtaining a received signal X of a target distance ring CUT ：
Mixing X CUT The number of array elements divided into a series is N 1 The number of the transmitted pulses in the coherent processing interval is M 1 Sub-aperture signal of
Wherein,
represents X CUT The nth row and the mth column of elements;
estimating a target range ring sub-aperture covariance matrix using a sub-aperture smoothing technique
defining a Capon integration region Ω according to the distribution of the clutter ridges in the space-time plane:
Ω＝{f:||f-f′|| 2 ≤ε,f′∈Π}，
wherein pi represents a set of points on the clutter ridge, and f' epsilon is any point on the clutter ridge; epsilon is a preset constant:
then, the target range ring sub-aperture covariance matrix
Capon spectrum of (a) is expressed as:
wherein f is s Representing normalized spatial frequency, f d Represents a normalized doppler frequency;
calculating a target distance ring sub-aperture clutter covariance matrix R 0 ：
Step 3, estimating a sub-aperture covariance matrix of the training sample;
dividing data X (l) of the ith training sample by adopting a sub-aperture division mode which is the same as that of the target distance ring to obtain:
wherein,
the n-th row and m-th column elements representing X (l);
estimating a sub-aperture covariance matrix R (l) of the ith training sample by using a sub-aperture smoothing technique;
step 4, calculating a weight vector of the space-time adaptive filter;
selecting a sub-aperture space-time guide vector outside any integral area as a target guide vector a, and calculating a weight vector w (l) of the space-time adaptive filter by using a sub-aperture covariance matrix of the ith training sample according to a minimum variance distortionless response criterion:
and 5, calculating the test statistic eta (l) of the ith training sample:
step 6, screening samples according to the test statistic eta (l);
and setting a screening threshold value mu, and rejecting the first training sample when the test statistic eta (l) of the first training sample is smaller than the screening threshold value mu.
Further, in the step 2, the distribution of the clutter ridges in the space-time plane is as follows:
according to the flight parameters of the aircraft: obtaining the normalized Doppler frequency f by the flight speed v, the yaw angle psi of the airborne radar and the flight direction of the airborne radar, the pitch angle theta of a specific counter point relative to the airborne platform and the observation direction beta of the airborne radar d Expressed as:
obtaining a normalized spatial frequency f s Expressed as:
wherein, λ is the wavelength of the airborne radar, d is the array element spacing of the airborne radar, f r The pulse repetition frequency of the airborne radar;
by normalizing the spatial frequency f s And normalized Doppler frequency f d The distribution of the clutter ridges in the spatio-temporal plane is determined.
Further, in step 6, the screening threshold μ is set to:
wherein k is a constant: k belongs to [0.1,0.01], L is the total number of training samples.
The invention has the beneficial effects that:
the invention provides a method for selecting an airborne radar training sample based on signal-to-noise-ratio loss, which has the following advantages:
1) The clutter characteristic of the target distance ring is represented by the sub-aperture covariance matrix of the target distance ring, and compared with a mode that the clutter characteristic of the target distance ring is represented by a sample covariance matrix based on a generalized inner product method, the method can avoid the influence of the heterogeneity of a training sample on the representation of the clutter characteristic of the target distance ring, and the representation result is only related to the target distance ring;
2) In order to eliminate target components possibly existing in the target distance ring sub-aperture covariance matrix, the invention reconstructs the target distance ring sub-aperture covariance matrix along a clutter ridge region in a space-time plane by using a Capon spectrum, and compared with an orthogonal projection mode adopted based on the sub-aperture covariance matrix, the Capon spectrum integral reconstruction mode adopted by the invention can better keep the clutter characteristic; according to the method, the distribution condition of the clutter ridges in the space-time plane is determined through the flight state parameters of the aircraft platform, and compared with a method based on terrain data, the method provided by the invention does not need to consider the problem of data matching, and is more easily suitable for actual working scenes;
3) The invention adopts signal-to-noise-ratio loss as test statistic to represent the strength of the space-time filter designed based on training sample data on the clutter suppression capability of a target distance ring, and the clutter suppression performance is better when the target distance ring is closer to the clutter characteristics of a training sample, namely the test statistic can represent the approximation degree of a target distance ring sub-aperture clutter covariance matrix and a training sample sub-aperture covariance matrix.
Drawings
Fig. 1 is a schematic flow chart of a method for selecting training samples of an airborne radar based on signal-to-noise-ratio loss in an embodiment of the present invention.
Fig. 2 is a schematic diagram of sub-aperture division of target range ring data according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a geometric model of an airborne platform of an airborne radar system in an embodiment of the invention.
FIG. 4 is a schematic diagram of the targets and clutter in the space-time plane according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides a method for selecting an airborne radar training sample, which adopts output signal-to-noise ratio loss as test statistic; in order to represent the clutter characteristic of the target distance ring, the method comprises the steps of firstly calculating a sub-aperture covariance matrix of the target distance ring, and performing Capon spectrum reconstruction in a space-time plane according to clutter ridge slope information (which can be estimated through flight state information) because target signal components may exist in the target distance ring to obtain the sub-aperture clutter covariance matrix of the target distance ring; then calculating a sub-aperture covariance matrix of a training sample, selecting a space-time guide vector outside an integral area in a space-time plane as a target guide vector, and designing a space-time adaptive filter according to a minimum variance distortionless response criterion; the filter designed by the training sample is used for processing the sub-aperture signal of the target distance ring, and the loss of the output signal-to-noise ratio is estimated to be used as the test statistic, so that the strength of the suppression capability of the selected sample on the target distance ring clutter when the selected sample is used for designing the space-time filter is directly represented, the output signal-to-noise ratio of the finally designed space-time filter is directly influenced, and the method is more direct and accurate.
In this embodiment, the following technical scheme is specifically adopted:
a method for selecting airborne radar training samples based on signal-to-noise-ratio loss is disclosed, the flow of which is shown in FIG. 1, and the method specifically comprises the following steps:
assuming that the number of array elements of the airborne radar is N uniform linear arrays, the wavelength is lambda, the array element spacing is d, the number of transmitted pulses in coherent processing intervals is M, and the pulse repetition frequency is f r The number of training samples to be screened is L;
then, the received signal of the target range ring is
Dividing the array into a series of array elements with the number of N 1 The number of the transmitted pulses in the coherent processing interval is M 1 Sub-aperture signal of
Wherein,
represents X CUT The nth row and the mth column of elements; as shown in fig. 2;
estimating a target range ring sub-aperture covariance matrix using a sub-aperture smoothing technique
Comprises the following steps:
since the target range ring may contain the target signal, if it is used directly
The sub-aperture clutter covariance matrix representing the target distance ring possibly has deviation, so the sub-aperture clutter covariance matrix of the target distance ring is estimated by adopting a Capon spectrum reconstruction mode, and the specific method is to calculate the sub-aperture clutter covariance matrix of the target distance ring firstly
In a Capon spectrum in a space-time plane, determining the distribution of clutter ridges according to flight state parameters of the airplane, and finally performing integral reconstruction in a region near the clutter ridges to obtain a sub-aperture clutter covariance matrix of a target distance ring; the specific process is as follows:
the schematic diagram of the geometric model of the airborne platform of the airborne radar system is shown in FIG. 3, wherein the airplane flies along the direction of an x axis, the flying speed is v, the height of the airborne platform is H, and the array is parallel to an XOY plane; in the figure, P represents an anti-point on the ground, theta and
respectively representing the pitch angle and the azimuth angle of the array relative to the plane of the aircraft, and psi representing the yaw angle of the array relative to the flight direction of the aircraft; defining β as the radar observation direction, we can obtain:
then, the normalized doppler frequency at point P can be expressed as:
the normalized spatial frequency at point P may be represented as:
for any point on the ground, it can be represented by β as a variable, according to f s And f d The clutter in the space-time plane can be determined by the expressionThe distribution of ridges;
when psi =0 ° In the time, the airborne radar adopts a front side view working mode, so that the distribution of clutter ridges in the space-time plane is a straight line, the target and clutter distribution is shown in fig. 4, and the slope of the clutter ridges can be expressed as:
and the set of points on the ridge of the clutter is represented as pi, then any point on the ridge of the clutter is represented as f' ∈ pi, and the area of Capon integral reconstruction is defined as Ω:
Ω＝{f:||f-f′|| 2 ≤ε,f′∈Π}，
wherein epsilon is a predetermined constant, and is used for determining the range of integration, and is taken
f represents any point within the integration region Ω;
then the user can use the device to make a visual display,
the Capon spectrum of (a) can be expressed as:
wherein,
representing space-time steering vectors, f s Representing normalized spatial frequency, f d Represents a normalized doppler frequency; a -1 Represents a matrix inversion operation, · H Which represents the conjugate transpose of the image,
represents the Kronecker product;
the target range ring sub-aperture clutter covariance matrix based on Capon spectral reconstruction can be expressed as:
for calculation, the integration area can be divided into grid points, and then the integration area is uniformly divided into Q points by using summation instead of integration, and Q > N 1 M 1 Generally, Q =100N is selected 1 M 1 Then the reconstructed target range ring sub-aperture clutter covariance matrix can be expressed as:
wherein, s (f) si ,f di ) E omega, i =1,2, \8230, Q represents a space-time guiding vector corresponding to the discretization grid point selected in the integral area;
step 3, estimating a sub-aperture covariance matrix of the training sample;
l, L =1, 2.. For L training sample data
Dividing training sample data X (l) by adopting a sub-aperture division mode which is the same as that of the target distance ring to obtain:
wherein,
the n-th row and m-th column elements representing X (l);
then the sub-aperture covariance matrix for the ith training sample is estimated using a sub-aperture smoothing technique as:
step 4, calculating a weight vector of the space-time adaptive filter;
selecting a sub-aperture space-time guide vector outside an integral area as a target guide vector a, and calculating a weight vector w (l) of a space-time self-adaptive filter by using a sub-aperture covariance matrix of the ith training sample according to a minimum variance distortionless response criterion;
then, according to the minimum variance undistorted response criterion, the solution expression of calculating the weight vector w (l) of the space-time adaptive filter by using the sub-aperture covariance matrix of the ith training sample is:
the following can be obtained:
step 5, calculating the test statistic eta (l) of the ith training sample;
processing the target range ring subaperture signal by using a filter with a weight vector w (l), estimating the loss of the output signal-to-noise ratio, and taking the loss as a test statistic eta (l) corresponding to the ith training sample, wherein the covariance matrix of the target range ring clutter signal is represented by the covariance matrix of the target range ring subaperture clutter estimated in the step 2, namely
Step 6, screening samples according to the test statistic eta (l);
the test statistic of the invention is the signal-to-noise-ratio loss of the filter with weight vector w (l) to the target distance ring data processing output, directly representing the strength of the first training sample used for the space-time adaptive filter design to the target distance ring clutter suppression capability, and the greater the eta (l) value, the stronger the target distance ring clutter suppression capability; therefore, when the training samples are screened, after a threshold value is set, the training samples lower than the threshold value are removed; one method that can be used for setting the threshold value in this embodiment is:
wherein k is [0.1,0.01].
Where mentioned above are merely embodiments of the invention, any feature disclosed in this specification may, unless stated otherwise, be replaced by alternative features serving equivalent or similar purposes; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.
Claims (3)
1. A method for selecting training samples of an airborne radar based on signal-to-noise-ratio loss is characterized by comprising the following steps:
step 1, estimating a target distance ring sub-aperture covariance matrix;
setting the number of array elements of the airborne radar as N and the number of transmitted pulses in the coherent processing interval as M, and obtaining a received signal X of a target distance ring CUT ：
Mixing X CUT The number of array elements divided into a series is N 1 The number of the transmitted pulses in the coherent processing interval is M 1 Sub-aperture signal of
Wherein,
represents X CUT The nth row and the mth column of elements;
estimating a target range ring sub-aperture covariance matrix using a sub-aperture smoothing technique
Step 2, estimating a target distance ring sub-aperture clutter covariance matrix;
defining a Capon integration region Ω according to the distribution of the clutter ridges in the space-time plane:
Ω＝{f:||f-f′|| 2 ≤ε,f′∈Π}，
wherein f represents any point in the integral region omega, pi represents a set of points on the clutter ridge, and f' epsilon pi represents any point on the clutter ridge; epsilon is a preset constant:
then, the target range ring sub-aperture covariance matrix
Capon spectrum of (a) is expressed as:
wherein f is s Representing normalized spatial frequency, f d Represents a normalized doppler frequency;
calculating a target distance ring sub-aperture clutter covariance matrix R 0 ：
Step 3, estimating a sub-aperture covariance matrix of the training sample;
and dividing the data X (l) of the ith training sample by adopting a sub-aperture division mode which is the same as the target distance ring to obtain:
wherein,
the n-th row and m-th column elements representing X (l);
estimating a sub-aperture covariance matrix R (l) of the ith training sample by using a sub-aperture smoothing technique;
step 4, calculating a weight vector of the space-time adaptive filter;
selecting a sub-aperture space-time guide vector outside any integral area as a target guide vector a, and calculating a weight vector w (l) of the space-time adaptive filter by using a sub-aperture covariance matrix of the ith training sample according to a minimum variance distortionless response criterion:
and 5, calculating the test statistic eta (l) of the ith training sample:
step 6, screening samples according to the test statistic eta (l);
and setting a screening threshold value mu, and rejecting the first training sample when the test statistic eta (l) of the first training sample is smaller than the screening threshold value mu.
2. The method for selecting training samples of airborne radar based on signal-to-noise ratio loss according to claim 1, wherein in the step 2, the distribution of the clutter ridges in the space-time plane is as follows:
according to the flight parameters of the aircraft: obtaining the normalized Doppler frequency f by the flight speed v, the yaw angle psi of the airborne radar and the flight direction of the airborne radar, the pitch angle theta of a specific counter point relative to the airborne platform and the observation direction beta of the airborne radar d Expressed as:
obtaining a normalized spatial frequency f s Expressed as:
wherein, λ is the wavelength of the airborne radar, d is the array element spacing of the airborne radar, f r The pulse repetition frequency of the airborne radar;
by normalizing the spatial frequency f s And normalized Doppler frequency f d The distribution of the clutter ridges in the spatio-temporal plane is determined.
3. The method for selecting training samples of airborne radar based on signal-to-noise-ratio loss according to claim 1, wherein in the step 6, the screening threshold μ is set as:
wherein k is a constant: k belongs to [0.01,0.1], and L is the total number of training samples.
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