A high-efficiency motorized long-time coherent accumulation detection method for weak target

Abstract

The application discloses a kind of high-efficiency motorized weak target long-time coherent accumulation detection methods, belong to radar target detection field;Including the three-scale decomposition of motorized target radial motion parameter variable;In target blur scale motion parameter variable search space eliminates Doppler frequency blur effect;Through Keystone conversion correction first-order range migration caused by motorized target baseband speed;In target coarse scale baseband motion parameter variable search space, estimate motorized target coarse scale baseband motion parameter by GIFT conversion;In target fine scale baseband motion parameter variable search space, estimate motorized target fine scale baseband motion parameter, obtain temporary range-doppler spectrum;According to motorized target motion parameter variable three-scale decomposition model, estimate the complete motion parameter of motorized target;Based on motorized target motion parameter estimation, obtain motorized target range-doppler spectrum.The method has the characteristics of low computational complexity, Doppler blur effective compensation, high detection performance.

Description

Technical Field

[0001] This invention relates to the field of radar target detection, and in particular to a highly efficient long-term coherent accumulation detection method for maneuvering weak targets. Background Technology

[0002] With the continuous development of maneuvering targets and the increasing maturity of stealth technology, efficient detection of moving targets by radar remains a challenge in the field of radar signal processing. For the detection of weak maneuvering targets, long-term coherent accumulation technology can effectively improve detection performance by accumulating target energy through long-term observation. However, the relative motion between the target and the radar can cause the peak value of its echo envelope to appear in different range cells, known as the range migration effect; the relative acceleration between the target and the radar will cause echo phase changes, resulting in the Doppler frequency spanning multiple Doppler cells, known as the Doppler migration effect. Both range migration and Doppler migration can cause target energy defocusing, affecting detection performance. Therefore, it is necessary to estimate and compensate for motion parameters such as range, velocity, and acceleration.

[0003] Existing long-term coherent accumulation detection techniques for high-order RM and DFM can be categorized into three types: 1) Keystone Transform (KT) methods: KT corrects the linear range migration of the target through a scale transformation on the range-frequency-slow-time plane. Second-order KT (SoKT) can correct the second-order range migration of the target through a second-order scale transformation. However, KT methods may exhibit trajectory segmentation effects during moving target detection, leading to decreased detection performance. 2) Radon Transform methods: These methods accumulate target echo signal energy within different range cells through multi-dimensional search in the motion parameter space, simultaneously correcting RM and DFM, improving the echo signal-to-noise ratio, and thus enhancing target detection performance. However, these methods have high computational complexity. 3) Non-parametric search processing methods: These methods utilize the coupling relationship between echo signals to complete signal energy accumulation in the correlation function domain and construct target detection statistics related to the characteristics of the accumulated signal. The computational complexity is lower than that of parametric search methods, but the detection probability decreases.

[0004] In general, existing long-term coherent accumulation methods for estimating and compensating motion parameters of maneuvering targets involve high-dimensional optimization problems and have high computational complexity. The first type of method does not consider the fundamental reason for the piecewise effect of KT trajectory in maneuvering target detection scenarios. It directly performs KT under conditions where the target's Doppler frequency is ambiguous. Since the Doppler frequency exceeds the slow-time sampling frequency, KT detection performance deteriorates, and the estimation error of target motion parameters becomes large. The second type of method does not consider the differences in the formation mechanisms of range migration and Doppler migration. It directly uses the minimum granularity criterion and a single fine-granularity estimation of the optimal velocity, acceleration, and other compensation functions, leading to a coupling estimation problem between range and Doppler motion parameters. Therefore, there is a large amount of computational redundancy, and the algorithm implementation has low degrees of freedom. While the third type of non-parametric search processing method significantly reduces computational complexity, its performance also drops sharply. Therefore, for the requirement of maneuvering weak target detection, there is an urgent need to study efficient long-term coherent accumulation detection methods for maneuvering weak targets. These methods should have computational complexity that meets the needs of practical systems and detection probabilities comparable to the performance of optimal detectors. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides an efficient long-term coherent accumulation detection method for maneuvering weak targets.

[0006] To achieve the above objectives, the technical solution provided by this invention is: a highly efficient long-term coherent accumulation detection method for maneuvering weak targets, comprising the following steps:

[0007] Step 1: Construct the slant range variable search space between the radar and the moving target based on radar parameters and the motion characteristics of the moving target. and the search space of motion parameter variables in: Let c represent the search space for the radial distance variable, the radial velocity variable, and the radial acceleration variable, respectively; 0,min and c 0,max These represent the minimum and maximum values ​​of the target radial distance variable, respectively; c 1,min and c 1,max These represent the minimum and maximum values ​​of the target radial velocity variable, respectively; c 2,min and c 2,max These represent the minimum and maximum values ​​of the target's radial acceleration variable, respectively.

[0008] Step 2: Search the target motion parameter variables and By performing a three-scale decomposition, the search space of the target fuzzy motion parameter variables required for Doppler frequency defuzzification is obtained. The search space for target coarse-scale baseband motion parameter variables is determined by the minimum scale of the motion parameters required for target range migration correction. The search space for the target fine-scale motion parameter variables determined by the minimum scale of motion parameters required by Doppler migration correction. in: These represent the search space for the target fuzzy velocity variable and the search space for the fuzzy acceleration variable, respectively. These represent the search space for the coarse-scale baseband radial velocity variable and the search space for the coarse-scale baseband radial acceleration variable, respectively. These represent the search space for the fine-scale baseband radial velocity variable and the search space for the fine-scale baseband radial acceleration variable, respectively.

[0009] 2.1 Three-scale decomposition formula for target motion parameter variables:

[0010]

[0011] in: These are the target's p-order motion parameter variables; These are the target p-order fuzzy motion parameter variables; These are the target p-order baseband motion parameter variables, and the values ​​after Doppler frequency deblurring. The remaining part; It is the target coarse-scale p-order baseband motion parameter variable, which is determined by the minimum scale of motion parameters required for range migration correction; These are the target's fine-scale p-order baseband motion parameter variables, determined by the minimum scale of motion parameters required for Doppler migration correction, and are the values ​​after range migration correction. The remaining part.

[0012] Specifically, based on radar parameters, to avoid the Keystone Transform (KT) trajectory segmentation effect caused by Doppler ambiguity, the target p-order ambiguity motion parameter variable is defined as follows:

[0013]

[0014] in: c represents the p-th order motion parameter ambiguity of the target; p c represents the true value of the target's p-th order motion parameters; p,min This represents the minimum value of the search space for the p-th order motion parameter variables of the target; round(·) represents rounding; Δc p,a This ensures that the Doppler frequencies are not ambiguous, that is, the Doppler frequencies do not exceed the minimum p-order motion parameter interval of [-PRF / 2, PRF / 2].

[0015]

[0016] Where: 0≤β p ≤1, p=1,2 satisfies PRF is the radar pulse repetition frequency; λ is the radar transmitted signal wavelength; T is the radar coherent accumulation time.

[0017] Depend on The range of values ​​for the baseband motion parameters must satisfy the following conditions:

[0018]

[0019] Therefore, to avoid range migration caused by baseband motion parameters, the target coarse-scale p-order baseband motion parameter variable is defined as follows:

[0020]

[0021] Where: the search interval for the p-order motion parameters of the target coarse-scale Δ RM,p It is the minimum interval on the distance unit that guarantees no p-order distance migration:

[0022]

[0023] Where: 0≤α p ≤1, p=1,2 satisfies c is the speed of light; f s It is a fast sampling frequency.

[0024] To avoid Doppler migration, the target fine-scale p-order baseband motion parameter variable is defined as follows:

[0025]

[0026] Wherein: the search interval for the p-order baseband motion parameters at the target fine scale Δ DFM,p It is the minimum interval on the Doppler unit that guarantees no p-order Doppler migration:

[0027]

[0028] Where: f c It is the carrier frequency.

[0029] 2.2 Based on the three-scale decomposition formula of target motion parameters, the search space of target motion parameter variables is decomposed into three scales to obtain the target fuzzy scale motion parameter search space, the target coarse scale baseband motion parameter search space, and the target fine scale baseband motion parameter search space.

[0030] The search space for the p-th order motion parameter variables of the target fuzzy scale is defined as follows:

[0031]

[0032] The search space for the target coarse-scale p-order baseband motion parameter variables is defined as follows:

[0033]

[0034] The search space for the target fine-scale p-order baseband motion parameter variables is defined as follows:

[0035]

[0036] Step 3: For an array of fuzzy scale motion parameter variables Construct a fuzzy motion parameter compensation function in the target fuzzy scale motion parameter search space:

[0037]

[0038] Where: f k =kΔf, k=1,…,K represents the fast time domain frequency; Δf is the frequency interval; K is the number of frequency intervals; t m =mPRT, where m = 1, ..., M represents the slow time, PRT is the pulse repetition time, and M is the number of pulses.

[0039] The echo signal is deblurred using a fuzzy scale motion parameter compensation function, as shown below:

[0040]

[0041] Among them: echo signal A represents the complex amplitude of the target in the frequency domain; c0, c1, and c2 represent the target's radial distance, radial velocity, and radial acceleration at the initial moment, respectively; c 1,b and c 2,b These represent the target radial baseband velocity and radial baseband acceleration, respectively.

[0042] Step 4: Compensate for the Doppler ambiguity frequency in the signal y1(f) k ,t m Perform the Keystone transformation, that is, Eliminate target linear distance migration:

[0043]

[0044] Where: A2=A1exp[-j4πc0 / λ];t m′ ≈m′PRT is the slow-time variable after the Keystone transform, where m′=0,…,M-1; λ=c / f c It is the wavelength of the radar transmitted signal.

[0045] Step 5: For an array of coarse-scale baseband motion parameter variables The echo signal y2(f) is searched in the coarse-scale baseband motion parameter search space of the target. k ,t m′ The modified generalized inverse Fourier transform (mGIFT) is performed as follows:

[0046]

[0047] Where: τ n =n / f s (n = 1, ..., K) represents fast time; f s For fast time-domain sampling frequency; For the target coarse-scale baseband motion parameter distance migration compensation function; A3(τ) is the pre-compensation function for the Doppler migration of the target coarse-scale baseband motion parameters. n )=A2Kexp[jπf s (τ n [-2c0 / c)] represents the complex amplitude of the signal after mGIFT transformation; scaling factor.

[0048] Step 6: For an array of fine-scale baseband motion parameter variables with p≥2 p is the order of the motion parameters, in For y3(τ) n ,t m′ Performing a Generalized Fourier Transform (GFT) yields the temporary range-Doppler (RD) spectrum:

[0049]

[0050] in: A4(τ) represents an array consisting of fine-scale baseband motion parameter variables multiplied by the scale factor κ; n ,v d )=A3(τ n )Mexp[-j2πT(v d +c 1,b ) / λ]; The target baseband velocity variable; Based on The Doppler migration compensation function; This represents the fine-scale baseband acceleration variable after transformation by the scale factor κ.

[0051] Step 7: Using the temporary RD spectral plane obtained in Step 6, determine whether the target exists using the Likelihood Ratio Test (LRT) method:

[0052]

[0053] Wherein: γ LRT H0 is the threshold for the likelihood ratio test; H0 is the null hypothesis of LRT; H1 is the alternative hypothesis of LRT. If LRT holds, then all radial motion parameters of the target are estimated: the target radial distance estimate. τ is obtained from the likelihood ratio test n Estimated value; target radial velocity estimate Target radial acceleration estimate c is obtained from the likelihood ratio test 1,b The estimated value, and its relationship with the estimated target radial velocity, are as follows: This is the estimated radial baseband velocity of the target.

[0054] Step 8: Given the target fuzzy scale motion parameters and coarse-scale baseband motion parameters in Steps 3 and 5, if the target fine-scale baseband motion parameter search space in Step 6 has not been traversed... Then return to step 6; if the target fine-scale baseband motion parameter search space has been traversed. Then proceed to the next step.

[0055] Step 9: If the target coarse-scale baseband motion parameter variable search space in Step 5 has not been traversed Then return to step 5; if the search space of the target coarse-scale baseband motion parameter variables has been traversed. Then proceed to the next step.

[0056] Step 10: If the target fuzzy scale motion parameter variable search space in Step 3 has not been traversed Then return to step 3; if the target fuzzy scale motion parameter variable search space has been traversed. Then proceed to the next step.

[0057] Step 11: Based on all target fuzzy scale motion parameters estimated in Steps 1 to 10 Target baseband motion speed Target coarse-scale baseband motion parameters Target fine-scale baseband motion parameters and target radial distance J represents the number of targets, and the estimated radial distances and all motion parameters of all targets are given. in: and Let represent the radial distance estimate, fuzzy-scale radial velocity estimate, and fuzzy-scale radial acceleration estimate of the j-th target, respectively. and Let represent the baseband radial velocity estimate, coarse-scale baseband radial acceleration estimate, and fine-scale baseband radial acceleration estimate for the j-th target, respectively. Let represent the radial velocity estimate and radial acceleration estimate of the j-th target, respectively.

[0058] Using radial range and motion parameter estimates for all targets, the target's fuzzy Doppler frequencies are compensated, a Keystone transform is performed, and the remaining range migration and Doppler migration are corrected to estimate the target's RD spectrum.

[0059]

[0060] in: This represents the complex amplitude of the j-th target after processing by the TS-KT-MFP algorithm; It is the estimated Doppler frequency of the j-th target, i.e. The asinc function is the discrete form of the sinc function, asinc(Bτ) n )=sin(πBτ n ) / [K valid sin(πΔfτ n )];B=K valid Δf is the signal bandwidth; Δf is the frequency unit spacing; K valid It represents the number of effective frequency units; PRF = 1 / PRT is the pulse repetition frequency; It is the radial distance of the j-th target.

[0061] target motion parameters The RD spectrum is used for subsequent radar signal processing.

[0062] The advantages of this invention over the prior art are:

[0063] 1) Improve the degree of freedom of the algorithm by decomposing the motion parameter variables into three scales;

[0064] 2) In the search space of motion parameter variables at the target fuzzy scale, the fuzziness effect of the echo signal Doppler frequency is eliminated by the compensation function, avoiding the trajectory segmentation effect in KT, thereby improving the accumulation performance of the algorithm;

[0065] 3) The distance migration is corrected by GIFT transformation in the search space of the target coarse-scale motion parameter variables, so as to achieve complete distance migration compensation without ignoring the distance migration caused by residual motion parameters;

[0066] 4) Use the LRT criterion to perform target detection on the RD spectrum. By limiting the threshold, the cross terms generated between multiple targets at the same distance are suppressed, thereby reducing the detection of false targets. Attached Figure Description

[0067] Figure 1 This is a flowchart of an efficient long-term coherent accumulation detection method for maneuvering weak targets provided by an embodiment of the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Reference Figure 1 This embodiment provides an efficient long-term coherent accumulation detection method for maneuvering weak targets, including the following steps:

[0070] Step 1: Construct the slant range variable search space between the radar and the moving target based on radar parameters and the motion characteristics of the moving target. and the search space of motion parameter variables in: Let c represent the search space for the radial distance variable, the radial velocity variable, and the radial acceleration variable, respectively; 0,min and c 0,max These represent the minimum and maximum values ​​of the target radial distance variable, respectively; c 1,min and c 1,max These represent the minimum and maximum values ​​of the target radial velocity variable, respectively; c 2,min and c 2,max These represent the minimum and maximum values ​​of the target's radial acceleration variable, respectively.

[0071] Step 2: Search the target motion parameter variables and By performing a three-scale decomposition, the search space of the target fuzzy motion parameter variables required for Doppler frequency defuzzification is obtained. The search space for target coarse-scale baseband motion parameter variables is determined by the minimum scale of the motion parameters required for target range migration correction. The search space for the target fine-scale motion parameter variables determined by the minimum scale of motion parameters required by Doppler migration correction. in: These represent the search space for the target fuzzy velocity variable and the search space for the fuzzy acceleration variable, respectively. These represent the search space for the coarse-scale baseband radial velocity variable and the search space for the coarse-scale baseband radial acceleration variable, respectively. These represent the search space for the fine-scale baseband radial velocity variable and the search space for the fine-scale baseband radial acceleration variable, respectively.

[0072] 2.1 Three-scale decomposition formula for target motion parameter variables:

[0073]

[0074] in: These are the target's p-order motion parameter variables; These are the target p-order fuzzy motion parameter variables; These are the target p-order baseband motion parameter variables, and the values ​​after Doppler frequency deblurring. The remaining part; It is the target coarse-scale p-order baseband motion parameter variable, which is determined by the minimum scale of motion parameters required for range migration correction; These are the target's fine-scale p-order baseband motion parameter variables, determined by the minimum scale of motion parameters required for Doppler migration correction, and are the values ​​after range migration correction. The remaining part.

[0075] Specifically, based on radar parameters, to avoid the KT trajectory segmentation effect caused by Doppler ambiguity, the target p-order ambiguity motion parameter variable is defined as follows:

[0076]

[0077] in: c represents the p-th order motion parameter ambiguity of the target; p c represents the true value of the target's p-th order motion parameters; p,min This represents the minimum value of the search space for the p-th order motion parameter variables of the target; round(·) represents rounding; Δc p,a This ensures that the Doppler frequencies are not ambiguous, that is, the Doppler frequencies do not exceed the minimum p-order motion parameter interval of [-PRF / 2, PRF / 2].

[0078]

[0079] Where: 0≤β p ≤1, p=1,2 satisfies PRF is the radar pulse repetition frequency; λ is the radar transmitted signal wavelength; T is the radar coherent accumulation time.

[0080] Depend on The range of values ​​for the baseband motion parameters must satisfy the following conditions:

[0081]

[0082] Therefore, to avoid range migration caused by baseband motion parameters, the target coarse-scale p-order baseband motion parameter variable is defined as follows:

[0083]

[0084] Where: the search interval for the p-order motion parameters of the target coarse-scale Δ RM,p It is the minimum interval on the distance unit that guarantees no p-order distance migration:

[0085]

[0086] Where: 0≤α p ≤1, p=1,2 satisfies c is the speed of light; f s It is a fast sampling frequency.

[0087] To avoid Doppler migration, the target fine-scale p-order baseband motion parameter variable is defined as follows:

[0088]

[0089] Wherein: the search interval for the p-order baseband motion parameters at the target fine scale Δ DFM,p It is the minimum interval on the Doppler unit that guarantees no p-order Doppler migration:

[0090]

[0091] Where: f c It is the carrier frequency.

[0092] 2.2 Based on the three-scale decomposition formula of target motion parameters, the search space of target motion parameter variables is decomposed into three scales to obtain the target fuzzy scale motion parameter search space, the target coarse scale baseband motion parameter search space, and the target fine scale baseband motion parameter search space.

[0093] The search space for the p-th order motion parameter variables of the target fuzzy scale is defined as follows:

[0094]

[0095] The search space for the target coarse-scale p-order baseband motion parameter variables is defined as follows:

[0096]

[0097] The search space for the target fine-scale p-order baseband motion parameter variables is defined as follows:

[0098]

[0099] Step 3: For an array of fuzzy scale motion parameter variables Construct a fuzzy motion parameter compensation function in the target fuzzy scale motion parameter search space:

[0100]

[0101] Where: f k =kΔf, k=1,…,K represents the fast time domain frequency; Δf is the frequency interval; K is the number of frequency intervals; t m =mPRT, where m = 1, ..., M represents the slow time, PRT is the pulse repetition time, and M is the number of pulses.

[0102] The echo signal is deblurred using a fuzzy scale motion parameter compensation function, as shown below:

[0103]

[0104] Among them: echo signal A represents the complex amplitude of the target in the frequency domain; c0, c1, and c2 represent the target's radial distance, radial velocity, and radial acceleration at the initial moment, respectively; c 1,b and c 2,b These represent the target radial baseband velocity and radial baseband acceleration, respectively.

[0105] Step 4: Compensate for the Doppler ambiguity frequency in the signal y1(f) k ,t m Perform the Keystone transformation, that is, Eliminate target linear distance migration:

[0106]

[0107] Where: A2=A1exp[-j4πc0 / λ];t m′ ≈m′PRT is the slow-time variable after the Keystone transform, where m′=0,…,M-1; λ=c / f c It is the wavelength of the radar transmitted signal.

[0108] Step 5: For an array of coarse-scale baseband motion parameter variables The echo signal y2(f) is searched in the coarse-scale baseband motion parameter search space of the target.k ,t m′ The modified generalized inverse Fourier transform (mGIFT) is performed as follows:

[0109]

[0110] Where: τ n =n / f s (n = 1, ..., K) represents fast time; f s For fast time-domain sampling frequency; For the target coarse-scale baseband motion parameter distance migration compensation function; A3(τ) is the pre-compensation function for the Doppler migration of the target coarse-scale baseband motion parameters. n )=A2Kexp[jπf s (τ n [-2c0 / c)] represents the complex amplitude of the signal after mGIFT transformation; scaling factor.

[0111] Step 6: For an array of fine-scale baseband motion parameter variables with p≥2 p is the order of the motion parameters, in For y3(τ) n ,t m′ Performing a Generalized Fourier Transform (GFT) yields the temporary range-Doppler (RD) spectrum:

[0112]

[0113] in: A4(τ) represents an array consisting of fine-scale baseband motion parameter variables multiplied by the scale factor κ; n ,v d )=A3(τ n )Mexp[-j2πT(v d +c 1,b ) / λ]; The target baseband velocity variable; Based on The Doppler migration compensation function; This represents the fine-scale baseband acceleration variable after transformation by the scale factor κ.

[0114] Step 7: Using the temporary RD spectral plane obtained in Step 6, determine whether the target exists using the Likelihood Ratio Test (LRT) method:

[0115]

[0116] Wherein: γ LRT H0 is the threshold for the likelihood ratio test; H0 is the null hypothesis of LRT; H1 is the alternative hypothesis of LRT. If LRT holds, then all radial motion parameters of the target are estimated: the target radial distance estimate. τ is obtained from the likelihood ratio test n Estimated value; target radial velocity estimate Target radial acceleration estimate c is obtained from the likelihood ratio test 1,b The estimated value, and its relationship with the estimated target radial velocity, are as follows: This is the estimated radial baseband velocity of the target.

[0117] Step 8: Given the target fuzzy scale motion parameters and coarse-scale baseband motion parameters in Steps 3 and 5, if the target fine-scale baseband motion parameter search space in Step 6 has not been traversed... Then return to step 6; if the target fine-scale baseband motion parameter search space has been traversed. Then proceed to the next step.

[0118] Step 9: If the target coarse-scale baseband motion parameter variable search space in Step 5 has not been traversed Then return to step 5; if the search space of the target coarse-scale baseband motion parameter variables has been traversed. Then proceed to the next step.

[0119] Step 10: If the target fuzzy scale motion parameter variable search space in Step 3 has not been traversed Then return to step 3; if the target fuzzy scale motion parameter variable search space has been traversed. Then proceed to the next step.

[0120] Step 11: Based on all target fuzzy scale motion parameters estimated in Steps 1 to 10 Target baseband motion speed Target coarse-scale baseband motion parameters Target fine-scale baseband motion parameters and target radial distance J represents the number of targets, and the estimated radial distances and all motion parameters of all targets are given. in: and Let represent the radial distance estimate, fuzzy-scale radial velocity estimate, and fuzzy-scale radial acceleration estimate of the j-th target, respectively. and Let represent the baseband radial velocity estimate, coarse-scale baseband radial acceleration estimate, and fine-scale baseband radial acceleration estimate for the j-th target, respectively. Let represent the radial velocity estimate and radial acceleration estimate of the j-th target, respectively.

[0121] Using radial range and motion parameter estimates for all targets, the target's fuzzy Doppler frequencies are compensated, a Keystone transform is performed, and the remaining range migration and Doppler migration are corrected to estimate the target's RD spectrum.

[0122]

[0123] in: This represents the complex amplitude of the j-th target after processing by the TS-KT-MFP algorithm; It is the estimated Doppler frequency of the j-th target, i.e. The asinc function is the discrete form of the sinc function, asinc(Bτ) n )=sin(πBτ n ) / [K valid sin(πΔfτ n )];B=K valid Δf is the signal bandwidth; Δf is the frequency unit spacing; K valid It represents the number of effective frequency units; PRF = 1 / PRT is the pulse repetition frequency; It is the radial distance of the j-th target.

[0124] target motion parameters The RD spectrum is used for subsequent radar signal processing.

[0125] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A highly efficient long-term coherent accumulation detection method for maneuvering weak targets, characterized in that, Includes the following steps: Step 1: Construct the slant range variable search space between the radar and the moving target based on radar parameters and the motion characteristics of the moving target. and the search space of motion parameter variables in: Let c represent the search space for the radial distance variable, the radial velocity variable, and the radial acceleration variable, respectively; 0,min and c 0,max These represent the minimum and maximum values ​​of the target radial distance variable, respectively; c 1,min and c 1,max These represent the minimum and maximum values ​​of the target radial velocity variable, respectively; c 2,min and c 2,max These represent the minimum and maximum values ​​of the target's radial acceleration variable, respectively. Step 2: Search the target motion parameter variables and By performing a three-scale decomposition, the search space of the target fuzzy motion parameter variables required for Doppler frequency defuzzification is obtained. The search space for target coarse-scale baseband motion parameter variables is determined by the minimum scale of the motion parameters required for target range migration correction. The search space for the target fine-scale motion parameter variables determined by the minimum scale of motion parameters required by Doppler migration correction. in: These represent the search space for the target fuzzy velocity variable and the search space for the fuzzy acceleration variable, respectively. These represent the search space for the coarse-scale baseband radial velocity variable and the search space for the coarse-scale baseband radial acceleration variable, respectively. These represent the search space for the fine-scale baseband radial velocity variable and the search space for the fine-scale baseband radial acceleration variable, respectively. Step 3: For an array of fuzzy scale motion parameter variables Construct a fuzzy motion parameter compensation function in the target fuzzy scale motion parameter search space. Where: f k =kΔf, k=1,…,K represents the fast time domain frequency; Δf is the frequency interval; K is the number of frequency intervals; t m =mPRT, where m = 1, ..., M represents the slow time, PRT is the pulse repetition time, and M is the number of pulses; use Doppler frequency deambiguation is performed on the echo signal to obtain the Doppler frequency-unambiguous signal y1(f) k ,t m ); Step 4: Compensate for the Doppler ambiguity frequency in the signal y1(f) k ,t m Perform the Keystone transformation, that is, Eliminate target linear distance migration: Where: A2 = A1exp[-j4πc0 / λ]; A1 is y1(f k ,t m The amplitude of ); t m′ ≈m′PRT is the slow-time variable after the Keystone transform, where m′=0,…,M-1; c is the speed of light; f c It is the carrier frequency; λ = c / f c It is the wavelength of the radar transmitted signal; c0, c 1,b and c 2,b These represent the target's radial distance, radial baseband velocity, and radial baseband acceleration at the initial moment, respectively. Step 5: For an array of coarse-scale baseband motion parameter variables The echo signal y2(f) is searched in the coarse-scale baseband motion parameter search space of the target. k ,t m′ The improved generalized inverse Fourier transform is performed to obtain y3(τ). n ,t m′ ); Step 6: For an array of fine-scale baseband motion parameter variables with p≥2 p represents the order of the motion parameters. For y3(τ) n ,t m′ Performing a generalized Fourier transform yields the temporary distance-Doppler spectrum: in: A4(τ) represents an array consisting of fine-scale baseband motion parameter variables multiplied by the scale factor κ; n ,v d )=A3(τ n )Mexp[-j2πT(v d +c 1,b ) / λ]; The target baseband velocity variable; Based on The Doppler migration compensation function; This represents the fine-scale baseband acceleration variable after transformation by the scale factor κ; Step 7: Using the temporary distance-Doppler spectral plane obtained in Step 6, determine whether a target exists based on the likelihood ratio test. If a target exists, estimate all motion parameters of the target. Step 8: Given the target fuzzy scale motion parameters and coarse-scale baseband motion parameters in Steps 3 and 5, if the target fine-scale baseband motion parameter search space in Step 6 has not been traversed... Then return to step 6; if the target fine-scale baseband motion parameter search space has been traversed. Then proceed to the next step; Step 9: If the target coarse-scale baseband motion parameter variable search space in Step 5 has not been traversed Then return to step 5; if the search space of the target coarse-scale baseband motion parameter variables has been traversed. Then proceed to the next step; Step 10: If the target fuzzy scale motion parameter variable search space in Step 3 has not been traversed Then return to step 3; if the target fuzzy scale motion parameter variable search space has been traversed. Then proceed to the next step; Step 11: Based on all target fuzzy scale motion parameters estimated in steps 1 to 10 Target baseband motion velocity Target coarse-scale baseband motion parameters Target fine-scale baseband motion parameters and target radial distance J represents the number of targets, and the estimated radial distances and all motion parameters of all targets are given. in: and Let represent the radial distance estimate, fuzzy-scale radial velocity estimate, and fuzzy-scale radial acceleration estimate of the j-th target, respectively. and Let represent the baseband radial velocity estimate, coarse-scale baseband radial acceleration estimate, and fine-scale baseband radial acceleration estimate for the j-th target, respectively. Let represent the radial velocity estimate and radial acceleration estimate of the j-th target, respectively; Using radial range and motion parameter estimates for all targets, the target's fuzzy Doppler frequencies are compensated, a Keystone transform is performed, and the remaining range migration and Doppler migration are corrected to estimate the target's RD spectrum. in: This represents the complex amplitude of the j-th target after processing by the TS-KT-MFP algorithm; It is the estimated Doppler frequency of the j-th target, i.e. The asinc function is the discrete form of the sinc function, asinc(Bτ) n )=sin(πBτ n ) / [K valid sin(πΔfτ n )];B=K valid Δf is the signal bandwidth; Δf is the frequency unit spacing; K valid It represents the number of effective frequency units; PRF = 1 / PRT is the pulse repetition frequency; It is the radial distance of the j-th target; Motion parameters of the moving target The range-Doppler spectrum is used for subsequent radar signal processing.

2. The efficient long-term coherent accumulation detection method for maneuvering weak targets as described in claim 1, characterized in that, In step 2, the three-scale decomposition method for target motion parameters includes: Step 2.1, Three-scale decomposition formula for target motion parameter variables: in: These are the target's p-order motion parameter variables; These are the target p-order fuzzy motion parameter variables; These are the target p-order baseband motion parameter variables, and the values ​​after Doppler frequency deblurring. The remaining part; It is the target coarse-scale p-order baseband motion parameter variable, which is determined by the minimum scale of motion parameters required for range migration correction; These are the target's fine-scale p-order baseband motion parameter variables, determined by the minimum scale of motion parameters required for Doppler migration correction, and are the values ​​after range migration correction. The remaining part; The target p-order fuzzy motion parameter variables are defined as follows: in: c represents the p-th order motion parameter ambiguity of the target; p c represents the true value of the target's p-th order motion parameters; p,min This represents the minimum value of the search space for the p-th order motion parameter variables of the target; round(·) represents rounding; Δc p,a This ensures that the Doppler frequencies are not ambiguous, that is, the Doppler frequencies do not exceed the minimum p-order motion parameter interval of [-PRF / 2, PRF / 2]. Where: 0≤β p ≤1, p=1,2 satisfies PRF is the radar pulse repetition frequency; λ is the wavelength of the radar transmitted signal; T is the radar coherent accumulation time; Depend on The range of values ​​for the baseband motion parameters must satisfy the following conditions: The target coarse-scale p-order baseband motion parameter variables are defined as follows: Where: the search interval for the p-order motion parameters of the target coarse-scale Δ RM,p It is the minimum interval on the distance unit that guarantees no p-order distance migration: Where: 0≤α p ≤1, p=1,2 satisfies c is the speed of light; f s It is a fast sampling frequency; The target fine-scale p-order baseband motion parameter variables are defined as follows: Wherein: the search interval for the p-order baseband motion parameters at the target fine scale Δ DFM,p It is the minimum interval on the Doppler unit that guarantees no p-order Doppler migration: Where: f c It is the carrier frequency; Step 2.2: Based on the three-scale decomposition formula of target motion parameters, the search space of target motion parameter variables is decomposed into three scales to obtain the target fuzzy scale motion parameter search space, the target coarse scale baseband motion parameter search space, and the target fine scale baseband motion parameter search space. The search space for the p-th order motion parameter variables of the target fuzzy scale is defined as follows: The search space for the target coarse-scale p-order baseband motion parameter variables is defined as follows: The search space for the target fine-scale p-order baseband motion parameter variables is defined as follows:

3. The efficient long-term coherent accumulation detection method for maneuvering weak targets as described in claim 1, characterized in that, The fuzzy motion parameter compensation formula in step 3 is: Among them: echo signal A represents the complex amplitude of the target in the frequency domain; c0, c1, and c2 represent the radial distance, radial velocity, and radial acceleration of the target at the initial moment, respectively; fuzzy motion parameter compensation function. f k =kΔf, k=1,…,K represents the fast time domain frequency; Δf is the frequency interval; K is the number of frequency intervals; t m =mPRT, where m = 1, ..., M represents the slow time, PRT is the pulse repetition time, and M is the number of pulses.

4. The efficient long-term coherent accumulation detection method for maneuvering weak targets as described in claim 1, characterized in that, The mGIFT transformation formula in step 5 is: For an array of coarse-scale motion parameter variables In the coarse-scale motion parameter search space of the target, the echo signal y2(f) is searched. k ,t m′ Perform mGIFT transformation: Where: τ n =n / f s (n = 1, ..., K) represents fast time; f s For fast sampling frequency; For the target coarse-scale baseband motion parameter distance migration compensation function; A3(τ) is the pre-compensation function for the Doppler migration of the target coarse-scale baseband motion parameters. n )=A2Kexp[jπf s (τ n [-2c0 / c)] represents the complex amplitude of the signal after mGIFT transformation; scaling factor.

5. The efficient long-term coherent accumulation detection method for maneuvering weak targets as described in claim 1, characterized in that, The likelihood ratio test formula in step 7 is as follows: Wherein: γ LRT H0 is the threshold for the likelihood ratio test; H0 is the null hypothesis of LRT; H1 is the alternative hypothesis of LRT; if LRT holds, then all radial motion parameters of the target are estimated: target radial distance estimate. τ is obtained from the likelihood ratio test n Estimated value; target radial velocity estimate Target radial acceleration estimate c is obtained from the likelihood ratio test 1,b The estimated value, and its relationship with the estimated target radial velocity, are as follows: This is the estimated radial baseband velocity of the target.

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