CN111999714B - Self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance - Google Patents

Self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance Download PDF

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CN111999714B
CN111999714B CN202010911685.0A CN202010911685A CN111999714B CN 111999714 B CN111999714 B CN 111999714B CN 202010911685 A CN202010911685 A CN 202010911685A CN 111999714 B CN111999714 B CN 111999714B
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CN111999714A (en
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简涛
何佳
王海鹏
何友
杨予昊
侯颖妮
李辉
高永婵
张健
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Naval Aeronautical University
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Abstract

The invention discloses a self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance, and belongs to the field of radar signal processing. Heterogeneous clutter prior distribution easy to process mathematically is built, arithmetic mean fusion is carried out on clutter prior information and heterogeneous clutter information contained in auxiliary data, whether different scattering points of a distance extension target are independent or not is designed, a high-precision distance extension target scattering point complex amplitude approximate maximum likelihood comprehensive estimation method is designed, distance extension target self-adaptive fusion detectors based on multi-scattering point estimation and clutter knowledge assistance are built respectively, expressions in a closed form are provided, optimization balance is kept between algorithm operation amount and detection performance, algorithm practicability is improved, adaptability of a broadband radar to a heterogeneous clutter environment is improved, target detection performance under heterogeneous clutter is improved, detection capacity of the broadband radar to weak and small targets under a complex electromagnetic environment is improved, and popularization and application values are achieved.

Description

Self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance
Technical Field
The invention belongs to the field of broadband radar signal processing, and particularly relates to a self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance.
Background
Unlike low-resolution narrow-band radar, the broadband radar has large bandwidth and high range resolution, has obvious advantages in the aspects of anti-interference, anti-reconnaissance, accurate detection and imaging, high-precision tracking, target identification and the like, has attracted wide attention in the fields of modern military and civil use, and has become an important direction for the development of modern radars. The distance resolution unit of the narrow-band radar is generally far larger than the geometric size of a common target, a target echo signal only occupies one distance resolution unit, and the actual target is often treated as a 'point target'. And echo signals of the broadband radar target not only occupy one distance resolution unit, but also are distributed in different radial distance resolution units to be presented as a one-dimensional range image to form a range extension target. With the wide application of broadband radars, the problem of detecting extended-range targets is receiving more and more attention, and becomes one of the hot spots and difficult problems in the radar signal processing field in recent years.
On one hand, target echoes observed by the broadband radar are distributed on a plurality of radial distance units, if a point target detection method of the narrow-band radar is still adopted, target detection is carried out on echo signals by aiming at a single distance unit, and sampling of adjacent distance units is utilized to carry out background clutter statistical characteristic estimation, energy of a strong scattering point of a distance extension target leaks into the adjacent distance units to form a signal pollution phenomenon, and further a shielding effect is formed on the target signals of the single distance unit to be detected, so that the target cannot be detected. On the other hand, the adaptive detection of the broadband radar target is influenced by various factors such as the complexity and the variability of the natural environment of the target, electromagnetic interference and the like, and the clutter statistical characteristic no longer meets the assumption condition of the independent and identically distributed uniform environment, which is particularly prominent in the detection scene of the broadband radar target, so that the existing distance extension target detection method is difficult to obtain an ideal detection effect. The existing clutter models mainly comprise a uniform environment, a partial uniform environment, a non-Gaussian clutter environment, a heterogeneous clutter environment and the like. In a distance extended target detection model under a partial uniform environment, clutter components in data (also called main data) of a plurality of distance units to be detected and reference distance unit data (also called auxiliary data) only containing pure clutter are assumed to have the same covariance matrix structure but different power levels; in a non-gaussian clutter environment modeled by a complex gaussian distribution, clutter components in the main data and the auxiliary data are assumed to have the same covariance matrix structure but different clutter power levels between different distance units. In the two clutter models, clutter components are assumed to have the same covariance matrix structure, but in the actual environments of complex sea clutter, ground clutter and the like faced by broadband radar detection, the uniformity of the clutter covariance matrix structure among different distance units can be further destroyed due to the existence of various interference factors, and the assumed conditions that the clutter components in the uniform, partially uniform and non-Gaussian clutter models have the same covariance matrix structure cannot be met. At the moment, a heterogeneous clutter model is needed to carry out environment modeling, clutter covariance matrix structures among different distance units in the heterogeneous clutter environment are similar but different, and the clutter covariance matrix structures in the auxiliary data and the main data need to be linked through reasonable prior distribution.
Under a detection scene of a broadband radar, aiming at the characteristic that a clutter covariance matrix structure between different distance units in an actual heterogeneous clutter environment is non-uniform, how to utilize reasonable prior distribution, accurately estimating clutter statistical information in a plurality of distance units in main data based on heterogeneous clutter information contained in auxiliary data, and establishing a high-precision distance extension target multi-scattering point amplitude estimation method while considering the estimation accuracy of the main data clutter covariance matrix structure, so as to establish distance extension target detection statistics in a closed form.
Disclosure of Invention
In a broadband radar detection scene, aiming at the characteristic of inhomogeneous structure of a clutter covariance matrix among different distance units in an actual heterogeneous clutter environment, how to construct reasonable prior distribution easy for mathematical processing, reasonably fusing clutter prior information and heterogeneous clutter information contained in auxiliary data, constructing a simple and effective covariance matrix structure estimation method, providing a solid foundation for designing a CFAR (computational fluid dynamics) of a broadband radar distance extension target detector while considering both estimation accuracy and algorithm operation amount of a main data clutter covariance matrix structure, further designing a high-precision distance extension target multi-scattering point amplitude estimation method, constructing a distance extension target knowledge self-adaptive fusion detector with a closed form, keeping optimized balance between algorithm operation amount and detection performance, improving algorithm practicability, and considering the specific condition whether statistics is independent among different scattering points of a distance extension target, the adaptability of the broadband radar to heterogeneous clutter environments is further improved, the target detection performance of the broadband radar under the heterogeneous clutter is improved, and the detection capability of the broadband radar to weak and small targets under the complex electromagnetic environment is improved.
The self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance comprises the following technical measures:
step 1, acquiring main data X from P distance units to be detected, acquiring K auxiliary data from K pure clutter distance units adjacent to the distance units to be detected, performing prior distribution modeling on a clutter covariance matrix structure M by utilizing inverse Wishart distribution, respectively constructing K posterior probability density functions of M based on the K auxiliary data, and respectively constructing the K posterior probability density functionsPerforming arithmetic mean on numbers to construct an arithmetic mean estimation method of the heterogeneous clutter covariance matrix structure probability density function, and obtaining the arithmetic mean estimation of the heterogeneous clutter covariance matrix structure probability density function
Figure BDA0002663534760000021
The method comprises the following specific steps:
p data vectors x to be detected are formed by echo complex amplitudes of P distance units to be detectedm(m is 1,2, …, P), and then, the main data X of dimension N × P is constructed [ X ═ X1,x2,...,xP]I.e. xmRepresenting the mth main data component, P being a natural number greater than 1; taking P distance units to be detected as the center, continuously taking a certain number of distance unit echo complex amplitudes which do not contain the target before and after the P distance units to be detected respectively to form K auxiliary data y only containing pure clutterk(K ═ 1,2,. K), where x ismAnd ykThe complex vectors are N multiplied by 1 dimension complex vectors, and N represents the product of the number of radar receiving array elements and the number of coherent processing pulses;
clutter components in the main data and the auxiliary data obey conditional complex Gaussian distribution, but a clutter covariance matrix structure M is a random matrix, the obeying degree of freedom is L, and the mean value is LR0Wherein M and R are0Hermitian complex matrices, each N × N dimensional, the prior distribution Probability Density Function (PDF) of M can be expressed as
Figure BDA0002663534760000022
Wherein exp [. C]Representing exponential functions, the functions tr (-) and det (-) representing the tracing and determinant of the matrix, respectively, the functions I [ L ] (LR)0)-1]Is defined as:
Figure BDA0002663534760000031
in the above formula, Γ (·) represents a Gamma function.
Let the complex matrix R of NxN dimensionsk(k＝1,2,... K) is represented by
Figure BDA0002663534760000032
Wherein the superscript "H" denotes the conjugate transpose.
Given the kth auxiliary data ykConditional PDF of the time matrix M (i.e. with auxiliary data y)kThe posterior PDF of conditional M) can be expressed as:
Figure BDA0002663534760000033
wherein, f (y)k| M) represents the auxiliary data y when M is knownkConditional complex Gaussian distribution PDF, f (y)k) Denotes ykPDF of (A), can be calculated by the following formula
Figure BDA0002663534760000034
Combining clutter prior distribution information and K auxiliary data yk(K ═ 1, 2.. K) clutter covariance matrix structure estimation is performed, intuitively and succinctly by arithmetic averaging, based on K conditional PDF f (M | y)k) (K ═ 1, 2.. K), the PDF of matrix M is estimated using arithmetic mean as:
Figure BDA0002663534760000035
the above expression is a PDF arithmetic mean estimation expression of M, and the expression has a closed form and concise operation, thereby laying a foundation for the subsequent design of the distance extension target self-adaptive fusion detection statistics of the closed form.
Step 2, arithmetic mean estimation based on heterogeneous clutter covariance matrix structure probability density function
Figure BDA0002663534760000036
Under the assumption of both target and non-targetSolving a probability density function of main data X, fusing clutter covariance matrix structure prior distribution information and clutter information in all auxiliary data according to a correction generalized likelihood ratio test criterion, solving approximate maximum likelihood comprehensive estimation of unknown complex amplitude of scattering points of a distance extension target on the basis of a clutter covariance matrix structure prior distribution mean value and a correction sampling covariance matrix formed by all K auxiliary data, and respectively constructing detection statistic lambda based on clutter prior information self-adaptive fusion detection in a heterogeneous clutter environment according to two conditions of whether statistics between different scattering points of the distance extension target is independent or not; the method comprises the following specific steps:
case 1: the independence among different scattering points of the distance extension target is not considered, namely the non-statistical independence among the different scattering points of the distance extension target is considered;
in the case of no target H0Under the assumption, the arithmetic mean estimation of the PDF based on the matrix M
Figure BDA0002663534760000041
The PDF of the main data X can be expressed as
Figure BDA0002663534760000042
In targeted H1Under the assumption, the arithmetic mean estimation of the PDF based on the matrix M
Figure BDA0002663534760000043
The PDF of the main data X can be expressed as
Figure BDA0002663534760000044
Wherein, in the above two formulae, fX(X|M,H0) And fX(X|M,H1) Each represents H0And H1Two complex Gaussian distribution conditions PDFs of the main data X under the assumption that M is known; a complex amplitude vector a with dimension of 1 XP represents the unknown complex amplitude of the distance extension target, and specifically comprises P scattering pointsUnknown complex amplitude of (d); p represents a known space-time guide vector, is a unit vector of Nx 1 dimension and is determined according to the working parameters of the radar system;
according to the generalized likelihood ratio test criterion, the distance extended target detection statistic can be preliminarily expressed as
Figure BDA0002663534760000045
Wherein, INAn identity matrix representing dimensions NxN;
as can be seen from the equation (9), due to the iterative summation operation, the maximum likelihood estimation of the unknown complex amplitude vector a is difficult to obtain, so the generalized likelihood ratio test criterion needs to be modified, and the suboptimal estimation expression of a is solved; note that only the kth auxiliary data y in the kth summation term of the right-hand numerator of equation (9)kCorresponding matrix RkRegarding a, if the estimation accuracy of a needs to be improved, the matrix R corresponding to all auxiliary data should be considered at the same timekAnd (K ═ 1, 2.. K), fusing clutter covariance matrix structure prior distribution information and clutter information in all auxiliary data, and based on a modified sampling covariance matrix formed by clutter covariance matrix structure prior distribution mean and all K auxiliary data, namely all K matrices RkBased on the sum of (K ═ 1, 2.. K), the approximate maximum likelihood integrated estimate (CE) of a can be obtained as:
Figure BDA0002663534760000051
wherein, the complex matrix D of N × N dimension represents a modified sampling covariance matrix, and specifically:
Figure BDA0002663534760000052
of the formula (10)
Figure BDA0002663534760000053
Substitution of the summation terms in equation (9)And a is given to Λ in formula (9)1The expression of (X) is subjected to equivalent mathematical transformation, and the distance extension target self-adaptive fusion detection statistic lambda based on multi-scattering point estimation and clutter knowledge assistance can be obtained for heterogeneous clutter environments1I.e. by
Figure BDA0002663534760000054
Without considering the independence between different scattering points of the range-extended object, it is possible to let
λ＝λ1 (13)
Case 2: aiming at the condition that statistics among different scattering points of a distance extension target are independent;
in the case of no target H0Under the assumption, the arithmetic mean estimation of the PDF based on the matrix M
Figure BDA0002663534760000055
Mth component X of main data XmCan be expressed as
Figure BDA0002663534760000056
In targeted H1Under the assumption, the arithmetic mean estimation of the PDF based on the matrix M
Figure BDA0002663534760000057
Mth component X of main data XmCan be expressed as
Figure BDA0002663534760000058
Wherein, in the two formulas, the first and the second,
Figure BDA0002663534760000059
and
Figure BDA00026635347600000510
individual watchShow H0And H1Two assumptionsmComplex gaussian distribution condition PDF when M is known; a complex magnitude vector a of dimension 1 × P may be expressed as a ═ a1,a2,...,aP]I.e. containing the unknown complex amplitude a of the P scattering pointsm(m-1, 2, …, P), where the complex scalar amRepresenting the unknown complex amplitude of the mth scattering point of the range expansion target;
according to the generalized likelihood ratio test criterion, the distance extended target detection statistic can be expressed as
Figure BDA0002663534760000061
As can be seen from equation (16), a is difficult to obtain due to the iterative summation operationm( m 1,2, …, P), therefore, the generalized likelihood ratio criterion needs to be modified to solve for amA suboptimal estimated expression of ( m 1,2, …, P); note that only the kth auxiliary data y in the kth summation term of the right-hand numerator of equation (16)kCorresponding matrix RkAnd am(m is 1,2, …, P), if a is increasedm( m 1,2, …, P) should be considered simultaneously with the matrix R for all assistance datakAnd (K ═ 1, 2.. K), fusing clutter covariance matrix structure prior distribution information and clutter information in all auxiliary data, and based on a modified sampling covariance matrix formed by clutter covariance matrix structure prior distribution mean and all K auxiliary data, namely all K matrices RkBased on the sum of (K ═ 1,2,. K), one can obtain amThe approximate maximum likelihood comprehensive estimate of (m ═ 1,2, …, P) is:
Figure BDA0002663534760000062
of the formula (17)
Figure BDA0002663534760000063
Substitution of the unknowns a in the summation terms in equation (16)mAnd in the formula (16)2The expression of (X) is subjected to equivalent mathematical transformation, and the self-adaptive fusion detection statistic lambda of the distance extension target based on multi-scattering point estimation and clutter knowledge assistance when the scattering points are independent can be obtained for heterogeneous clutter environments2I.e. by
Figure BDA0002663534760000064
In the above formula, | · | represents a modulus of a complex number.
Aiming at the condition that the statistics among different scattering points of the distance extension target are independent, the method can be used for
λ＝λ2 (19)
Step 3, setting a detection threshold T according to a preset false alarm probability in order to keep the CFAR characteristic of the detection method; comparing the detection statistic lambda corresponding to the main data with a detection threshold T, if lambda is larger than or equal to T, judging that a distance expansion target exists in the current distance unit to be detected, and using the main data not as auxiliary data of other subsequent distance units to be detected; and otherwise, if the lambda is less than T, judging that the distance expansion target does not exist in the current distance unit to be detected, and using the main data as auxiliary data of other subsequent distance units to be detected.
Compared with the background art, the invention has the beneficial effects that: 1) aiming at the characteristic of non-uniformity of a clutter covariance matrix structure among different distance units in an actual heterogeneous clutter environment, reasonable prior distribution of the clutter covariance matrix structure easy to mathematically process is constructed; 2) reasonable arithmetic mean fusion is carried out on clutter prior information and heterogeneous clutter information contained in auxiliary data, a simple and effective covariance matrix structure estimation method is constructed, the calculation complexity of an estimation algorithm is reduced under the condition of meeting estimation accuracy, and a solid foundation is laid for the CFAR design of a distance extension target detector; 3) aiming at whether different scattering points of a distance extension target are independent or not, a high-precision distance extension target scattering point complex amplitude approximate maximum likelihood comprehensive estimation method is designed through decoupling operation before summation, distance extension target self-adaptive fusion detectors based on multi-scattering point estimation and clutter knowledge assistance are respectively constructed, the distance extension target self-adaptive fusion detectors all have closed expressions, optimization balance is kept between algorithm operation amount and detection performance, algorithm practicability is improved, the adaptability of a broadband radar to a heterogeneous clutter environment is further improved, the target detection performance under the heterogeneous clutter is improved, and the detection capability of the broadband radar to a weak target and a small target under a complex electromagnetic environment is improved; 4) the method is suitable for some non-broadband radar detection situations, for example, large targets are detected by using low/medium resolution radars or space adjacent point target groups moving at the same speed (ship formation, airplane formation, vehicle formation and the like), and the method has a good application prospect.
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FIG. 1 is a functional block diagram of an adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance according to the present invention. In the figure 1, 1 is an intermediate matrix calculation module, 2 is an arithmetic mean estimation module of a clutter covariance matrix structure probability density function, 3 is a distance extension target self-adaptive fusion detector construction module based on multi-scattering point estimation and clutter knowledge assistance, and 4 is a detection judgment module.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The present embodiments are to be considered as illustrative and not restrictive, and all changes and modifications that come within the spirit of the invention and the scope of the appended claims are desired to be protected.
In order to verify the effectiveness of the method of the present invention, the present embodiment provides two embodiments, the first embodiment is directed to a wideband radar ground detection environment, and the second embodiment is directed to a wideband radar sea detection environment.
Example 1:
referring to the attached fig. 1 of the specification, the embodiment of example 1 is divided into the following steps:
step A1 is to irradiate the radar in the non-target range around the region to be detected by using the ground detection broadband radar to obtain the range unit echo complex amplitude values which are adjacent to the range unit to be detected and do not contain the target, and K auxiliary data y only containing pure ground clutter is formedk(k＝1,2,...K)，Sending the auxiliary data to an intermediate matrix calculation module (1); in the intermediate matrix calculation module (1), a matrix R is calculated according to equation (3)k(K ═ 1, 2.. K), matrix D is calculated according to equation (11), and matrix R is calculatedk(K ═ 1,2,. K) to an arithmetic mean estimation module (2) of the probability density function of the clutter covariance matrix structure, and the matrix D is sent to an adaptive fusion detector construction module (3) based on the comprehensive estimation of the target amplitude; in an arithmetic mean estimation module (2) for the probability density function of a clutter covariance matrix structure M, an arithmetic mean estimate of the probability density function of the clutter covariance matrix structure M is calculated according to equation (6)
Figure BDA0002663534760000071
And will be
Figure BDA0002663534760000072
Sending the data to a distance expansion target self-adaptive fusion detector building module (3) based on multi-scattering point estimation and clutter knowledge assistance;
it is worth noting that in the step A1, the ground clutter covariance matrix structure is subjected to prior distribution modeling by utilizing inverse Wishart distribution, heterogeneous clutter conditions caused by different clutter environments such as actual buildings, forest lands, grasslands and the like are fully considered, and the constructed ground clutter covariance matrix structure prior distribution is easy to mathematically process aiming at the characteristic of heterogeneous clutter matrix structures among different distance units under the heterogeneous clutter; in addition, the clutter prior information and heterogeneous clutter information contained in auxiliary data are subjected to simple and effective arithmetic mean fusion, the obtained covariance matrix structure estimation expression has a closed form, the estimation accuracy and the arithmetic operation amount of the ground clutter covariance matrix structure are considered, subsequent mathematical processing is facilitated, and a solid foundation is laid for the design of the ground detection broadband radar distance extension target detector CFAR.
A2, in a distance expansion target self-adaptive fusion detector building module (3) based on multi-scattering point estimation and clutter knowledge assistance, calculating a detection statistic lambda based on multi-scattering point estimation and clutter knowledge-assisted distance expansion target self-adaptive fusion detection under heterogeneous clutter according to an equation (12) and an equation (13), and sending the lambda to a detection decision module (4);
it should be noted that, for the case that the correlation between different scattering points of the ground target is strong, in this embodiment, the non-independence between different scattering points of the distance extension target is considered, and since the problem that the maximum likelihood estimation is obtained due to the fact that the unknown complex amplitude vector a is difficult to decouple in the summation operation is solved, the generalized likelihood ratio test criterion is modified, and the matrix R corresponding to all auxiliary data is fully consideredkThe method has the advantages that (K is 1, 2.. K) potential influence on the estimation a, an approximate maximum likelihood comprehensive estimation expression of a is designed by integrating all auxiliary data information, the estimation precision of unknown complex amplitude is improved, a closed form self-adaptive fusion detector is constructed, and the detection performance under a specific scene is improved; in addition, in the step a2, the constructed distance-extended target adaptive fusion detector based on multi-scattering point estimation and clutter knowledge assistance maintains optimized balance between algorithm computation and detection performance, improves algorithm practicability, reduces detection algorithm computation while maintaining CFAR characteristics, further improves adaptability of the broadband to the ground radar in heterogeneous ground clutter environments, improves distance-extended target detection performance in complex ground environments under heterogeneous clutter, and improves detection capability of the broadband radar to weak and small ground targets in complex electromagnetic environments. The method is also suitable for detecting the space adjacent vehicle point target group moving at the same speed by using the low/medium resolution radar, and has good application prospect.
Step a3 sets a detection threshold T according to a preset false alarm probability: specifically, the false alarm probability is set to PfaAccording to the Monte Carlo method, according to the previously accumulated 100/PfaAnd calculating a detection threshold T by the measured ground clutter data. Further, the detection statistic lambda is compared with a detection threshold T, if lambda is larger than or equal to T, it is judged that a distance expansion target exists in the current distance unit to be detected, and the main data X is not used as auxiliary data of other subsequent distance units to be detected; and otherwise, if the lambda is less than T, judging that the distance expansion target does not exist in the current distance unit to be detected, and using the main data X as auxiliary data of other subsequent distance units to be detected.
Example 2:
referring to the attached fig. 1 of the specification, the embodiment of example 2 is divided into the following steps:
step B1, using sea detection broadband radar to perform radar irradiation on the non-target range around the sea area to be detected, obtaining range unit echo complex amplitude values which are adjacent to the range unit to be detected and do not contain targets, and forming K auxiliary data y only containing pure sea clutterk(K ═ 1, 2.. K), the auxiliary data are sent to the intermediate matrix calculation module (1); in the intermediate matrix calculation module (1), a matrix R is calculated according to equation (3)k(K ═ 1, 2.. K), matrix D is calculated according to equation (11), and matrix R is calculatedk(K ═ 1,2,. K) to an arithmetic mean estimation module (2) of the probability density function of the clutter covariance matrix structure, and the matrix D is sent to an adaptive fusion detector construction module (3) based on the comprehensive estimation of the target amplitude; in an arithmetic mean estimation module (2) for the probability density function of a clutter covariance matrix structure M, an arithmetic mean estimate of the probability density function of the clutter covariance matrix structure M is calculated according to equation (6)
Figure BDA0002663534760000081
And will be
Figure BDA0002663534760000082
Sending the data to a distance expansion target self-adaptive fusion detector building module (3) based on multi-scattering point estimation and clutter knowledge assistance;
it is worth noting that in the step B1, the sea clutter covariance matrix structure is subjected to prior distribution modeling by utilizing inverse Wishart distribution, heterogeneous clutter conditions caused by clutter peaks and the like in the marine environment under different sea conditions are fully considered, and the constructed sea clutter covariance matrix structure prior distribution is easy to perform mathematical processing aiming at the characteristic of heterogeneous clutter covariance matrix structures among different distance units under the heterogeneous clutter of the marine environment; in addition, the clutter prior information and heterogeneous clutter information contained in auxiliary data are subjected to simple and effective arithmetic mean fusion, the obtained covariance matrix structure estimation expression has a closed form, the estimation accuracy and the arithmetic operation amount of the sea clutter covariance matrix structure are considered, subsequent mathematical processing is facilitated, and a solid foundation is laid for the design of the CFAR of the sea broadband radar distance extension target detector.
Step B2, in a distance expansion target self-adaptive fusion detector building module (3) based on multi-scattering point estimation and clutter knowledge assistance, calculating a detection statistic lambda based on multi-scattering point estimation and clutter knowledge-assisted distance expansion target self-adaptive fusion detection under heterogeneous sea clutter according to an equation (18) and an equation (19), and sending the lambda to a detection decision module (4);
it should be noted that, for the case that different scattering points of the sea surface target are independent from each other, in this embodiment, the statistical independence between different scattering points of the distance extension target is considered, and due to the iterative summation operation, the unknown quantity amThe maximum likelihood estimation of (m 1, 2., P) cannot obtain a closed analytic expression, the generalized likelihood ratio test criterion needs to be modified, and the matrix R corresponding to all auxiliary data is fully consideredkK) pair estimation am(m-1, 2, …, P), a was designed by integrating all assistance data informationmThe approximate maximum likelihood comprehensive estimation expression of (m is 1,2, …, P) improves the estimation precision of the complex amplitude of the distance expansion target scattering point, constructs a distance expansion self-adaptive fusion detector with a closed form, and improves the detection performance under a specific scene; in addition, in the step B2, the constructed distance-extended target adaptive fusion detector based on multi-scattering point estimation and clutter knowledge assistance maintains the optimized balance between the algorithm computation and detection performance, improves the algorithm practicability, reduces the detection algorithm computation while maintaining the CFAR characteristic, further improves the adaptability of the broadband to the ground radar in the heterogeneous sea clutter environment, improves the distance-extended target detection performance in the complex marine environment under the heterogeneous clutter, and improves the detection capability of the broadband radar to the weak and small sea targets in the complex electromagnetic environment. The method is also suitable for detecting the spatially adjacent ship formation point target group moving at the same speed by using the low/medium resolution radar, and has good application prospect.
Step B3 sets a detection threshold T according to the preset false alarm probability: specifically, the false alarm probability is set to PfaAccording to the Monte Carlo methodAccording to the previously accumulated 100/PfaCalculating a detection threshold T from the measured data; considering that the difficulty of obtaining the sea clutter is high, if the actually obtained pure sea clutter actual measurement data quantity Z is less than 100/PfaThen 100/P is absentfaThe Z clutter data can be obtained by simulation by using a sea clutter simulation model, wherein model parameters are reasonably estimated and set according to the obtained pure sea clutter actual measurement data. Further comparing the detection statistic lambda with a detection threshold T, if lambda is larger than or equal to T, judging that a distance expansion target exists in the current distance unit to be detected, and the main data X is not used as auxiliary data of other subsequent distance units to be detected; and otherwise, if the lambda is less than T, judging that the distance expansion target does not exist in the current distance unit to be detected, and using the main data X as auxiliary data of other subsequent distance units to be detected.

Claims (4)

1. The self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance is characterized by comprising the following steps of:
step 1, aiming at X main data and K auxiliary data of P distance units to be detected, performing prior distribution modeling on a clutter covariance matrix structure by utilizing inverse Wishart distribution, further respectively constructing K posterior probability density functions of the clutter covariance matrix structure based on the K auxiliary data, performing arithmetic mean on the K posterior probability density functions of the clutter covariance matrix structure, constructing an arithmetic mean estimation method of a heterogeneous clutter covariance matrix structure probability density function, and obtaining arithmetic mean estimation of the heterogeneous clutter covariance matrix structure probability density function;
step 2, based on arithmetic mean estimation of a heterogeneous clutter covariance matrix structure probability density function, solving the probability density function of main data X under the two assumptions of a target and a non-target, fusing clutter covariance matrix structure prior distribution information and clutter information in all auxiliary data according to a modified generalized likelihood ratio test criterion, solving approximate maximum likelihood comprehensive estimation of unknown complex amplitude of a scattering point of a distance expansion target based on a modified sampling covariance matrix formed by the clutter covariance matrix structure prior distribution mean and all K auxiliary data, and respectively constructing detection statistics of distance expansion target self-adaptive fusion detection based on the prior clutter information under a heterogeneous clutter environment aiming at two conditions of whether statistics are independent among different scattering points of the distance expansion target or not;
step 3, setting a detection threshold T according to a preset false alarm probability; comparing the detection statistic lambda corresponding to the main data X with T, if lambda is larger than or equal to T, judging that a distance expansion target exists in the current distance unit to be detected, and not using X as auxiliary data of other subsequent distance units to be detected; and otherwise, if the lambda is less than T, judging that the distance expansion target does not exist in the current distance unit to be detected, and taking the X as auxiliary data of other subsequent distance units to be detected.
2. The adaptive fusion detection method based on multi-scatter point estimation and clutter knowledge assistance of claim 1, wherein in the step 1:
based on K posterior probability density functions of clutter covariance matrix structure M, estimating the probability density function of M by adopting an arithmetic mean method to obtain the arithmetic mean estimation of the probability density function of heterogeneous clutter covariance matrix structure
Figure FDA0003498007420000011
Is composed of
Figure FDA0003498007420000012
Wherein exp [. C]Expressing exponential function, expressing functions tr (-) and det (-) to trace and determinant matrix respectively, N to product of radar receiving array element number and coherent processing pulse number, and expressing matrix RkK is 1,2,. K, and is specifically represented as
Figure FDA0003498007420000013
The superscript "H" denotes the conjugate transpose, L denotes the degree of freedom of the inverse Wisharp distribution, R0Hermitian complex matrix of dimension NxN, K auxiliary data ykK is 1,2,. K, each being an N × 1-dimensional complex vector;
Figure FDA0003498007420000014
Γ (·) represents a Gamma function.
3. The adaptive fusion detection method based on multi-scatter point estimation and clutter knowledge assistance of claim 1, wherein in the step 2:
aiming at the condition of non-statistical independence between different scattering points of a distance extension target, based on a correction generalized likelihood ratio test criterion and on a correction sampling covariance matrix formed by a clutter covariance matrix structure prior distribution mean value and all K auxiliary data, solving approximate maximum likelihood comprehensive estimation of unknown complex amplitude vectors of the scattering points of the distance extension target, and further aiming at a heterogeneous clutter environment, obtaining a detection statistic lambda of self-adaptive fusion detection based on multi-scattering point estimation and clutter knowledge assistance as
Figure FDA0003498007420000021
Wherein, INAn identity matrix representing dimensions NxN; p represents a known space-time guide vector, is a unit vector of Nx 1 dimension and is determined according to the working parameters of the radar system; the complex matrix D of dimension NxN is expressed as
Figure FDA0003498007420000022
P represents the number of the distance units to be detected; det (-) denotes determinant on matrix; matrix RkK is 1,2,. K, denoted as
Figure FDA0003498007420000023
The superscript "H" denotes the conjugate transpose, L denotes the degree of freedom of the inverse Wisharp distribution, R0Hermitian complex matrix of dimension NxN, K auxiliary data ykK is a complex vector of N × 1 dimensions.
4. The adaptive fusion detection method based on multi-scatter point estimation and clutter knowledge assistance of claim 1, wherein in the step 2:
aiming at the condition that statistics among different scattering points of the distance extension target are independent, based on a correction generalized likelihood ratio test criterion and on the basis of a correction sampling covariance matrix formed by a clutter covariance matrix structure prior distribution mean value and all K auxiliary data, approximate maximum likelihood comprehensive estimation of unknown complex amplitudes of all P scattering points of the distance extension target is solved, and further aiming at a heterogeneous clutter environment, a detection statistic lambda of self-adaptive fusion detection based on multi-scattering point estimation and clutter knowledge assistance is obtained and is taken as
Figure FDA0003498007420000024
Wherein, the N x 1 dimension complex vector xmM is 1,2, …, P, representing the mth main data component; i isNAn identity matrix representing dimensions NxN; p represents a known space-time guide vector, is a unit vector of Nx 1 dimension and is determined according to the working parameters of the radar system; det (-) denotes determinant on matrix; matrix RkK is 1,2,. K, denoted as
Figure FDA0003498007420000025
The superscript "H" denotes the conjugate transpose, L denotes the degree of freedom of the inverse Wisharp distribution, R0Hermitian complex matrix of dimension NxN, K auxiliary data ykK is 1,2,. K, each being an N × 1-dimensional complex vector; the complex matrix D of dimension NxN is expressed as
Figure FDA0003498007420000026
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CN112799043B (en) * 2021-04-08 2021-07-06 中国人民解放军空军预警学院 Extended target detector and system in the presence of interference in a partially homogeneous environment
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CN115508828B (en) * 2022-10-20 2024-05-14 中国人民解放军海军航空大学 Intelligent fusion detection method for radar target under subspace interference

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7646830B1 (en) * 2005-08-26 2010-01-12 Weill Lawrence R Complex maximum likelihood range estimator in a multipath environment
CN105807267A (en) * 2016-03-11 2016-07-27 中国人民解放军国防科学技术大学 MIMO radar extended target detection method
CN106324576A (en) * 2016-07-29 2017-01-11 西安电子科技大学 Autoregressive model-based radar target adaptive matching and filtering method
CN106872958A (en) * 2017-04-27 2017-06-20 中国人民解放军海军航空工程学院 Radar target self-adapting detecting method based on linear fusion
CN106932766A (en) * 2017-04-27 2017-07-07 中国人民解放军海军航空工程学院 Range extension target self-adapting detecting method based on variable element generalized structure
KR101768587B1 (en) * 2016-05-13 2017-08-17 국방과학연구소 Covariance matrix estimation method for reducing nonstationary clutter and heterogeneity clutter
CN108919224A (en) * 2018-07-26 2018-11-30 中国人民解放军海军航空大学 The adaptive fusion detection method of broadband radar target based on skew symmetry structure

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100984150B1 (en) * 2003-03-21 2010-09-29 록히드 마틴 코포레이션 Target detection improvements using temporal integrations and spatial fusion
US8138963B1 (en) * 2010-09-10 2012-03-20 Mitsubishi Electric Research Laboratories, Inc. Method for detecting targets using space-time adaptive processing and shared knowledge of the environment
US9285469B1 (en) * 2014-10-29 2016-03-15 Src, Inc. Multiplatform GMTI radar with enhanced SNR, monopulse
US10088555B2 (en) * 2014-12-15 2018-10-02 Airbus Singapore Private Limited Automated method for selecting training areas of sea clutter and detecting ship targets in polarimetric synthetic aperture radar imagery
CN108919223B (en) * 2018-07-26 2020-09-08 中国人民解放军海军航空大学 Broadband radar target self-adaptive structured matching filtering method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7646830B1 (en) * 2005-08-26 2010-01-12 Weill Lawrence R Complex maximum likelihood range estimator in a multipath environment
CN105807267A (en) * 2016-03-11 2016-07-27 中国人民解放军国防科学技术大学 MIMO radar extended target detection method
KR101768587B1 (en) * 2016-05-13 2017-08-17 국방과학연구소 Covariance matrix estimation method for reducing nonstationary clutter and heterogeneity clutter
CN106324576A (en) * 2016-07-29 2017-01-11 西安电子科技大学 Autoregressive model-based radar target adaptive matching and filtering method
CN106872958A (en) * 2017-04-27 2017-06-20 中国人民解放军海军航空工程学院 Radar target self-adapting detecting method based on linear fusion
CN106932766A (en) * 2017-04-27 2017-07-07 中国人民解放军海军航空工程学院 Range extension target self-adapting detecting method based on variable element generalized structure
CN108919224A (en) * 2018-07-26 2018-11-30 中国人民解放军海军航空大学 The adaptive fusion detection method of broadband radar target based on skew symmetry structure

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
《Adaptive persymmetric detector of generalised likelihood ratio test in homogeneous environment》;Tao Jian, You He, Guisheng Liao, Jian Guan, Yunlong Dong;《IET Signal Processing》;20160328;91-99 *
《daptive clutter suppression, sea mine detection/classification, and fusion processing string for sonar imagery》;Tom Aridgides, Manuel F. Fernandez, Gerald J. Dobeck;《Detection and Remediation Technologies for Mines and Minelike Targets IV》;19990802;1-8 *
《空间非均匀海杂波的协方差矩阵估计新算法》;时艳玲;《系统工程与电子技术》》;20130806;2239-2243 *
曲付勇 ; 孟祥伟.《基于排序数据变率的最大似然恒虚警检测方法》.《探测与控制学报》.2011, *
简涛 ; 黄晓冬 ; 王捷 ; 何友.《基于修正最大似然估计的距离扩展目标检测器》.《武汉大学学报(信息科学版)》.2016, *

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