CN112904303B - Radar multi-target detection method combining clutter suppression and gridding FRFT processing - Google Patents

Radar multi-target detection method combining clutter suppression and gridding FRFT processing Download PDF

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CN112904303B
CN112904303B CN202110436856.3A CN202110436856A CN112904303B CN 112904303 B CN112904303 B CN 112904303B CN 202110436856 A CN202110436856 A CN 202110436856A CN 112904303 B CN112904303 B CN 112904303B
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黄勇
关键
陈小龙
薛永华
刘宁波
丁昊
王国庆
张�林
何友
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Naval Aeronautical University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
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Abstract

The invention relates to a radar multi-target detection method combining clutter suppression and gridding FRFT processing, and belongs to the technical field of radar signal processing and target detection. The method firstly solves the problem that the search range and the search step length of two independent variable parameters are difficult to quantify in the conventional FRFT domain target detection, and establishes a Doppler-acceleration two-dimensional search grid; then, aiming at each grid point in the Doppler-acceleration two-dimensional search grid, an adaptive matched filter technology is utilized to form an FRFT spectrum after clutter suppression, and the signal-to-noise-and-noise ratio is improved; and finally, the problem of multi-target detection is solved by utilizing a subspace projection technology. Compared with the conventional FRFT domain target detection method, the radar multi-target detection method based on FRFT processing has the advantages of clear parameter search grid, good signal-to-noise-ratio improvement performance, strong multi-target detection capability and suitability for engineering realization.

Description

Radar multi-target detection method combining clutter suppression and gridding FRFT processing
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
The wide-transmission narrow-reception radar (such as a Multiple-Input Multiple-Output (MIMO) radar for short) can obtain long-time observation on a target, and further can perform long-time coherent accumulation processing on target echo energy, improve the signal-to-noise-and-noise ratio, and improve the radar target detection performance. However, in the long-time coherent accumulation process, due to the instability of the motion of the target itself, the change of the radar observation angle and the improvement of the doppler resolution caused by long-time accumulation, the doppler of the target is easy to diffuse, and thus the target energy is not well accumulated. Fractional Fourier Transform (FRFT) processing can effectively solve this problem, especially for the case of doppler linear dispersion over time.
The target doppler changes linearly with time, that is, the target makes uniform acceleration motion, and at this time, the target echo pulse train forms a Linear Frequency Modulation (LFM) signal. The FRFT process achieves the purpose of accumulating LFM signal energy by compensating for a phase difference between adjacent pulses due to such a linear time shift speed. However, in the field of radar target detection, the FRFT-based radar target detection method has not been well solved for three problems:
1) The search range and the search step length of two independent variable parameters in FRFT processing.
In literature, FRFT processing typically employs a set of parameters (p, u) to represent the argument parameters of its two dimensions, where p represents the transformation order, 0 < p ≦ 1, and u is analogous to the argument ω in the frequency domain. However, for radar target detection, two parameters p and u lack clear physical meanings, so that there are large subjective factors (except the search range of p) in determining the search range and the search step size of p and u, which results in lack of basis for determining the FRFT domain search grid, and at the same time, the FRFT domain search grid may be too thin to increase the amount of computation unnecessarily, and may also be too thick to lose the target.
2) Signal to noise ratio improvement in the FRFT domain.
In general, FRFT processing performs dot product (also called inner product) processing on an observed echo burst and an LFM steering vector formed by each (p, u) parameter set, thereby accumulating target energy and attempting to separate target and clutter in the FRFT domain for the purpose of improving signal-to-noise-and-noise ratio. This is done similarly to the MTD method in doppler domain processing. In practice, the target and the clutter may occupy partially the same (p, u) parameter unit, and the clutter energy is also accumulated while the target energy is accumulated, so the signal-to-noise ratio improvement is not optimal.
3) Multi-target detection problem in the FRFT domain.
A plurality of targets with different Doppler and acceleration may exist in the FRFT domain of one range-azimuth unit, and the phenomenon that the FRFT side lobe of a strong target covers the main lobe of a weak target easily occurs in consideration of the characteristics of the FRFT. The existing literature generally uses a Clean technology for processing, and firstly estimates the echo of a strong target, and then subtracts the echo of the strong target from the total echo, and further detects a weak target. However, this method is only suitable for the case where the strong target is very strong, otherwise the random initial phase of the strong target cannot be accurately estimated; meanwhile, higher requirements are also provided for the search step size of the two-dimensional parameters in the FRFT domain so as to ensure the estimation precision of the (p, u) parameter group.
In order to solve the three problems, the invention provides a radar multi-target detection method combining clutter suppression and gridding FRFT processing.
Disclosure of Invention
The invention aims to provide a radar multi-target detection method combining clutter suppression and gridding FRFT (fractional Fourier transform) processing aiming at the problem of long-time accumulation detection of a wide-transmitting narrow-receiving radar target, wherein the technical problems to be solved comprise:
(1) designing the search ranges and the search step lengths of two independent variable parameters in FRFT processing;
(2) Signal to noise ratio improvement problems in the FRFT domain;
(3) multi-target detection problem in the FRFT domain.
The invention relates to a radar multi-target detection method combining clutter suppression and gridding FRFT processing, which is characterized by comprising the following technical measures:
the method comprises the following steps: according to the expression form of the Doppler effect of the uniformly accelerated moving target in the radar echo pulse train, Doppler and acceleration are directly adopted as independent variable parameters of two dimensions of an FRFT domain, and then a search grid about the two parameters is established on the basis of determining the search range and the search step length of the two parameters.
On one hand, Doppler and acceleration are inherent characteristics of a uniformly accelerated moving target and have definite physical meanings; on the other hand, in a radar coherent pulse train observation mode, the search range and the search step length of the doppler and the acceleration in the FRFT processing can be theoretically determined, and the basic principle is as follows: the Doppler search range is determined by the pulse repetition frequency, and is generally between positive and negative half pulse repetition frequencies; the Doppler search step length is determined by the ratio of the pulse repetition frequency to the number of pulses; the acceleration is corresponding to the change of Doppler, the search range of the acceleration depends on the type of the target and the observation duration, the maximum and minimum speed and the bearable overload capacity of the target can be determined according to the type of the target, and then the search range of the acceleration of the target can be determined by combining the observation duration; the search step for the acceleration is then determined by the principle that, within the observation period, the change in acceleration within the search step cannot cause a change in doppler more than one doppler search step. The observation period in the above description refers to the product of the pulse repetition period and the number of pulses. In this way, the respective search ranges are divided into individual search grid points, referred to as doppler grid points and acceleration grid points, respectively, using the search step sizes of the two parameters doppler and acceleration, respectively, which in combination form a two-dimensional search grid for doppler and acceleration.
Step two: for each doppler-acceleration grid point, FRFT spectral values are calculated at the doppler-acceleration grid point using an adaptive matched filter technique.
The basic principle of the step is as follows: in the field of radar target adaptive detection, an adaptive matched filter estimates a background covariance matrix of a current detection range unit (called a detection unit) by using observation data of a plurality of range units (called reference units), then the purpose of suppressing clutter of the detection unit is achieved through the inversion operation of the background covariance matrix, and the purpose of accumulating echo energy on a given Doppler-acceleration grid point is achieved through coherent processing. Therefore, based on the search grid obtained in step one, for each doppler-acceleration grid point, the output of the adaptive matched filter is taken as the FRFT spectrum value on the doppler-acceleration grid point.
Step three: and searching the FRFT spectrum for the maximum spectrum value, and performing threshold judgment.
The basic principle is as follows: searching the FRFT spectrum for the maximum spectrum value, comparing the maximum spectrum value with a detection threshold, if the maximum spectrum value does not exceed the detection threshold, determining that the maximum spectrum value is not a target, and terminating the algorithm; if the detection threshold is exceeded, the target is considered, and the algorithm enters the step four. The detection threshold in the above description is calculated by combining a given false alarm probability on the basis of deriving a false alarm probability expression of the complex gaussian clutter adaptive matched filter under a non-target condition.
Step four: obtaining Doppler and acceleration parameters corresponding to the maximum spectral value searched in the third step, and constructing a projection operator; then, through projection operation, the LFM signal corresponding to the maximum spectrum value is removed from the observation data of the detection unit, and new observation data of the detection unit is formed; and repeating the second step to the fourth step to realize target-by-target detection.
Compared with the traditional radar target detection method based on FRFT processing, the radar multi-target detection method combining clutter suppression and gridding FRFT processing has the advantages that:
(1) the method solves the design problems of Doppler and acceleration search range and search step length in FRFT processing, so that the FRFT domain search grid cannot excessively increase calculated amount due to thinness, and cannot lose targets due to oversize.
(2) In the FRFT processing process, the output of the adaptive matched filter of each Doppler-acceleration grid point is used as the FRFT spectrum value, wherein the adaptive matched filter introduces background covariance matrix inversion operation, so clutter suppression is realized, and the signal-to-noise-and-noise ratio of the FRFT spectrum is effectively improved.
(3) The method utilizes projection operation to carry out target-by-target detection and elimination in the FRFT spectrum, and effectively solves the problem that FRFT side lobes of a large target cover a main lobe of a small target in the FRFT spectrum. The technology does not need to estimate the initial phase of a large target, and meanwhile, the estimation precision of Doppler and acceleration parameters is not high, so that the technology is convenient for engineering use.
Drawings
FIG. 1 is a flow chart of the radar multi-target detection method combining clutter suppression and gridding FRFT processing.
Detailed Description
The invention is described in further detail below with reference to the drawings. Referring to the attached FIG. 1 of the specification, the embodiment of the present invention comprises the following steps:
(1) establishing a Doppler-acceleration two-dimensional search grid;
(2) preparing observation data of a detection unit and a reference unit, and estimating a background covariance matrix;
(3) aiming at each grid point in a Doppler-acceleration two-dimensional search grid, forming an FRFT spectrum after clutter suppression by using a self-adaptive matched filter technology;
(4) searching the maximum spectral value in the FRFT spectrum, and performing threshold judgment; if the target does not exceed the detection threshold, the target does not exist, and the algorithm is terminated; if the detection threshold is exceeded, the target is considered to exist, and the algorithm enters the step (5);
(5) under the condition that the target exists in the step (4), adopting projection operation to remove the LFM signal corresponding to the maximum spectral value from the observation data of the detection unit to form new observation data of the detection unit; and (4) replacing the original detection unit observation data in the step (3) with new detection unit observation data, and executing the step (3) again until the algorithm termination condition in the step (4) is met.
The above steps are described in detail below:
(1) and establishing a Doppler-acceleration two-dimensional search grid.
The radar transmits coherent pulse train, the Doppler effect of the echo pulse train of the uniformly accelerated moving target is embodied in the radar echo pulse train after pulse compression and sampling
Figure BDA0003033383570000031
Wherein f isdtRepresenting the instantaneous doppler, corresponding to the instantaneous velocity of the target; f. ofdThe initial Doppler of the target in the current pulse train is shown, which is referred to as Doppler in the invention for short, and the initial speed of the corresponding target in the current pulse train is shown; a represents a target acceleration; λ represents a wavelength; t represents a pulse repetition period; l denotes a pulse number, L is 0, …, and L-1, where L denotes a burst length, that is, the number of pulses.
Doppler and acceleration are inherent characteristics of target motionThe symbols have definite physical meanings. The invention employs Doppler fdAnd two parameters of the acceleration a are used for constructing a two-dimensional parameter search space in FRFT processing, namely a Doppler-acceleration two-dimensional parameter space.
According to the radar signal theory, in the radar coherent pulse train observation mode, Doppler fdHas a value range of
Figure BDA0003033383570000041
Wherein
Figure BDA0003033383570000042
Represents the pulse repetition frequency; doppler fdStep size of search
Figure BDA0003033383570000043
The acceleration is corresponding to the change of the speed, the range of the acceleration depends on the type of the target and the observation time length, the maximum and minimum speed and the bearable overload capacity of the target can be determined according to the type of the target, and then the search range of the acceleration of the target can be determined by combining the observation time length, wherein the observation time length refers to the product of the pulse repetition period and the number of pulses. Recording the maximum speed change of the target within the observation time length as | V maxIf the acceleration is within a predetermined range, the acceleration is determined to be within a predetermined range
Figure BDA0003033383570000044
If the range is larger, the search range can be further reduced according to the maximum bearable overload capacity of the ship. The acceleration search step is determined based on the following principle: the change in acceleration within the search step cannot cause a change in doppler more than one doppler search step over the observation period, and thus,
Figure BDA0003033383570000045
thereby, the following doppler-acceleration two-dimensional search grid is formed:
doppler dimension: searching for a starting point
Figure BDA0003033383570000046
Search step size
Figure BDA0003033383570000047
Grid point
Figure BDA0003033383570000048
Grid number n, n is 0, …, L-1;
and (3) acceleration dimension: searching for a starting point
Figure BDA0003033383570000049
Search step size
Figure BDA00030333835700000410
Grid point
Figure BDA00030333835700000411
Grid serial number
Figure BDA00030333835700000412
Wherein the content of the first and second substances,
Figure BDA00030333835700000413
indicating rounding up.
(2) The observation data of the detection unit and the reference unit are prepared, and a background covariance matrix is estimated.
The specific operation of this step is as follows:
preparing observation data
Since adaptive clutter suppression is to be performed, it is necessary to estimate the background covariance matrix of the currently detected range bin (referred to as the detection bin) using the observed data of neighboring range bins (referred to as reference bins). By x0The method comprises the steps of representing L multiplied by 1 dimensional observation data obtained by pulse pressure and sampling of L pulse echoes in a detection unit; by x kK is 1, …, and K represents L × 1-dimensional observation data in K reference units. For the target echo, the phase relationship between elements in the L × 1 dimensional observation data is as follows,
Figure BDA0003033383570000051
estimate the background covariance matrix
The background covariance matrix estimate of the detection cell is denoted as M, and the formula is as follows,
Figure BDA0003033383570000052
wherein the superscript H denotes the conjugate transpose.
(3) And aiming at each grid point in the Doppler-acceleration two-dimensional search grid, forming an FRFT spectrum after clutter suppression by using the self-adaptive matched filter technology.
Searching the (n, m) th grid point in the grid by using x for the Doppler acceleration0And M, calculating FRFT spectral values Γ (n, M) at the grid points as follows,
Figure BDA0003033383570000053
wherein s isn,mThe guide vector corresponding to the (n, m) th grid point is expressed by the calculation formula
Figure BDA0003033383570000054
Am＝diag([am,0…am,l…am,L-1]) Represents an acceleration compensation matrix, wherein
Figure BDA0003033383570000055
fd,n＝[fdn,0…fdn,l…fdn,L-1]TRepresents a Doppler vector, wherein
Figure BDA0003033383570000056
Superscript T denotes transpose; m-1Means that the inverse operation is performed on the background covariance matrix estimated value M, according to the radar signal processing theory, M-1Has the function of inhibiting clutter, particularly in M-1M contained in-1/2(i.e., M)-1Cholesky decomposition) of (c).
And traversing all grid points in the Doppler-acceleration two-dimensional search grid to form an FRFT spectrum after clutter suppression.
(4) Searching the maximum spectral value in the FRFT spectrum, and performing threshold judgment; if the target does not exceed the detection threshold, the target does not exist, and the algorithm is terminated; if the detection threshold is exceeded, the target is considered to exist, and the algorithm enters the step (5).
The specific operation of this step is as follows:
calculating the given false alarm probability P by using the following formulafaThe detection threshold η under the condition.
Figure BDA0003033383570000057
Wherein, bqIs the qth independent sample of the random variable b, Q is 1, …, Q is the total number of independent samples of the random variable b; the random variable b obeys a Beta distribution with parameters of (K-L +2, L-1); probability of false alarm PfaIs given by the radar system and generally takes the value 10-6。
② extracting the maximum spectral value in FRFT spectrum, marking as gammamax(n ', m '), n ' represents the number of Doppler dimensions corresponding to the maximum spectral value, and the grid points of the corresponding Doppler dimensions are expressed as
Figure BDA0003033383570000061
m' represents the serial number of the acceleration dimension corresponding to the maximum spectrum value, and the grid point of the corresponding acceleration dimension is recorded as
Figure BDA0003033383570000062
③ comparing gammamax(n ', m') and a detection threshold η, if ΓmaxIf (n ', m') < eta, judging that no target exists at the maximum spectrum value, and terminating the algorithm; if gamma ismaxAnd (n ', m') is larger than or equal to eta, judging that the target exists at the maximum spectral value, and entering the algorithm into the step (5).
(5) Under the condition that the target exists in the step (4), adopting projection operation to remove the LFM signal corresponding to the maximum spectral value from the observation data of the detection unit to form new observation data of the detection unit; and (4) replacing the original detection unit observation data in the step (3) with new detection unit observation data, and executing the step (3) again until the algorithm termination condition in the step (4) is met.
The specific operation of the step is as follows:
using Doppler dimension grid points obtained in step (4)
Figure BDA0003033383570000063
And acceleration dimension grid points
Figure BDA0003033383570000064
Constructing a steering vector as follows
Figure BDA0003033383570000065
Figure BDA0003033383570000066
Calculating projection operator
Figure BDA0003033383570000067
Figure BDA0003033383570000068
Wherein, I represents an L × L dimensional unit matrix.
Computing formula by projection
Figure BDA0003033383570000069
I.e. data x can be observed from the detection unit0Middle rejection grid point
Figure BDA00030333835700000610
Corresponding LFM signal to obtain new observation data of detection unit, and recording the data
Figure BDA00030333835700000611
Fourthly, use
Figure BDA00030333835700000612
Replacement of x0And (5) executing the step (3) and the step (5) again until the algorithm termination condition of the third step in the step (4) is met.

Claims (2)

1. The radar multi-target detection method combining clutter suppression and gridding FRFT processing is characterized by comprising the following steps of:
s1, establishing a Doppler-acceleration two-dimensional search grid;
the specific steps for establishing the Doppler-acceleration two-dimensional search grid are as follows:
s11, representation form in radar echo pulse train of uniform acceleration moving target according to Doppler effect
Figure FDA0003620740050000011
Selecting Doppler fdTwo parameters of acceleration a are used for constructing a two-dimensional parameter search space in FRFT processing, wherein fdtRepresenting instantaneous Doppler, fdThe method comprises the steps of representing target starting Doppler in a current pulse train, wherein Doppler is used for short in the invention, corresponding to the starting speed of a target in the current pulse train, a represents target acceleration, and lambda represents wavelength; t represents a pulse repetition period; l represents a pulse number, L is 0, …, L-1, L represents a burst length, i.e., the number of pulses;
S12 Doppler f according to targetdValue range of
Figure FDA0003620740050000012
And search step size
Figure FDA0003620740050000013
The grid points forming the doppler dimension are as follows: the starting point of the search is
Figure FDA0003620740050000014
The search step size is
Figure FDA0003620740050000015
Doppler dimensional grid points of
Figure FDA0003620740050000016
The doppler dimension grid number is n, n is 0, …, L-1,
Figure FDA0003620740050000017
is the pulse repetition frequency;
s13, forming a search grid point of the acceleration dimension, specifically: firstly, according to the maximum speed variation | V that the target can reach in the observation time lengthmaxDetermining the variation range of the acceleration as
Figure FDA0003620740050000018
Then, the search step of the acceleration is determined as follows, according to the principle that the change of the acceleration in the search step cannot make the change of the Doppler more than one Doppler search step in the observation time length
Figure FDA0003620740050000019
The grid points forming the acceleration dimension are then as follows: the starting point of the search is
Figure FDA00036207400500000110
The search step size is
Figure FDA00036207400500000111
Acceleration dimension grid points are
Figure FDA00036207400500000112
The serial number of the acceleration dimension grid is m,
Figure FDA00036207400500000113
wherein the content of the first and second substances,
Figure FDA00036207400500000114
represents rounding up;
s14, forming a Doppler-acceleration two-dimensional search grid by combining the Doppler search grid point formed by the S12 and the acceleration search grid point formed by the S13;
s2, preparing observation data of the detection unit and the reference unit, and estimating a background covariance matrix;
s3, aiming at each grid point in the Doppler-acceleration two-dimensional search grid, forming an FRFT spectrum after clutter suppression by using a self-adaptive matched filter technology;
The specific steps for forming the clutter suppressed FRFT spectrum are as follows:
with respect to the (n, m) -th grid point in the doppler-acceleration two-dimensional search grid, the detection cell observation data x prepared in step S2 is used0And an estimated background covariance matrix M, calculating FRFT spectral values Γ (n, M) at the grid points as follows,
Figure FDA0003620740050000021
wherein s isn,mThe guide vector corresponding to the (n, m) th grid point is expressed by the calculation formula
Figure FDA0003620740050000022
Am＝diag([am,0…am，l…am,L-1]) Represents an acceleration compensation matrix, wherein
Figure FDA0003620740050000023
fd,n＝[fdn,0…fdn，l…fdn,L-1]TRepresents a Doppler vector, wherein
Figure FDA0003620740050000024
Superscript T denotes transpose; m-1Means to invert M;
traversing all grid points in the Doppler-acceleration two-dimensional search grid to form an FRFT spectrum after clutter suppression;
s4, searching the FRFT spectrum for the maximum spectrum value, and performing threshold judgment; if the detection threshold is not exceeded, the target does not exist, and the algorithm is terminated; if the detection threshold is exceeded, the target is considered to exist, and the algorithm enters the step S5;
s5, under the condition that the target exists in the step S4, the LFM signal corresponding to the maximum spectrum value is removed from the observation data of the detection unit by adopting projection operation to form new observation data of the detection unit; the original detecting unit observing data in step S3 is replaced with new detecting unit observing data, and step S3 is executed again until the algorithm terminating condition in step S4 is satisfied.
2. The radar multi-target detection method combining clutter suppression and gridding FRFT processing according to claim 1, wherein the step S5 specifically comprises:
s21, using the Doppler dimension grid point corresponding to the maximum spectrum value obtained in the step S4
Figure FDA0003620740050000025
And acceleration dimension grid points
Figure FDA0003620740050000026
Constructing a steering vector as follows
Figure FDA0003620740050000027
Figure FDA0003620740050000028
S22, according to the guide vector
Figure FDA0003620740050000029
Computing projection operators
Figure FDA00036207400500000210
Figure FDA00036207400500000211
Wherein, I represents an L multiplied by L dimensional unit array;
s23, using projection arithmetic expression
Figure FDA0003620740050000031
Eliminates the observation data x of the detection unit0Middle grid point
Figure FDA0003620740050000032
Corresponding LFM signal to obtain new observation data of detection unit
Figure FDA0003620740050000033
S24, use
Figure FDA0003620740050000034
Replacement of x0Step S3 and step S5 are executed again until the algorithm termination condition in step S4 is satisfied.
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CN103176178A (en) * 2013-02-04 2013-06-26 中国人民解放军海军航空工程学院 Radar moving target radon-fractional Fourier transform long-time phase-coherent accumulation detection method
CN103344949A (en) * 2013-06-18 2013-10-09 中国人民解放军海军航空工程学院 Radar slightly-moving target detection method based on Radon-linear canonical ambiguity function

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