An efficient long-term coherent integration detection method for maneuvering targets using airborne bistatic radar

Abstract

The invention discloses an efficient long-term coherent accumulation detection method for maneuvering targets of airborne bistatic radar, and belongs to the field of radar target detection. The radial motion parameter variables of the maneuvering target are decomposed in two scales; the range migration is compensated by GIFT transformation in the target coarse-scale motion parameter variable search space; the fine-scale motion parameters of the maneuvering target are estimated by CCPF transformation in the target fine-scale motion parameter variable search space; the GFT transformation is performed based on the estimated fine-scale motion parameters of the target to obtain a temporary range-Doppler spectrum; according to the two-scale decomposition model of the maneuvering target motion parameter variables, the complete motion parameters of the high-speed and high-maneuvering target are estimated; based on the estimation of the maneuvering target motion parameter, the range migration and Doppler migration are compensated in sequence and independently to obtain the maneuvering target range-Doppler spectrum. The method has the characteristics of low computational complexity, high detection probability, and high degree of freedom in algorithm implementation.

Description

Technical Field

[0001] The invention relates to the field of radar target detection, and in particular to an efficient coherent accumulation detection method for maneuvering targets of an airborne bistatic radar. Background Art

[0002] The airborne bistatic radar cooperative detection system has the advantages of wide detection range and no restriction on the curvature of the earth, and has received extensive attention in recent years. The long-term coherent accumulation technology can effectively improve the target signal-to-noise ratio and detection performance by accumulating target energy through long-term observation. However, the relative motion between the target and the radar can cause the peak of its echo envelope to appear in different distance units, which is called the range migration effect; the relative acceleration between the target and the radar will cause the echo phase to change, causing the Doppler frequency to cross multiple Doppler units, which is called the Doppler migration effect. Both range migration and Doppler migration will cause the target energy to defocus and affect the detection performance. Therefore, it is necessary to estimate and compensate for motion parameters such as range, velocity and acceleration. When the airborne bistatic radar system detects the target, the transmitting station, the receiving station and the target are all in motion, and the three present a complex relative motion relationship. Therefore, the slant range needs to be described by the third-order Taylor expansion model. Therefore, the range migration (RM) and Doppler frequency migration (DFM) caused by high-order parameters are difficult to ignore.

[0003] The existing long-term coherent accumulation detection technology solutions for high-order RM and DFM are divided into the following three categories: 1) Keystone transform (KT) type methods: KT corrects the range migration of the target through a scale transformation on the range frequency-slow time plane. Among them, the second-order KT (SoKT) can correct the second-order range migration of the target through a second-order scale transformation, but the KT type methods cannot correct the Doppler migration caused by high-order motion parameters; 2) Radon transform type methods: Through multi-dimensional search in the motion parameter space, the energy of the target echo signal in different distance units is effectively accumulated, and RM and DFM are corrected at the same time, the echo signal-to-noise ratio is improved, thereby improving the target detection performance, but this type of method has high computational complexity; 3) Non-parametric search processing type methods: Using the coupling relationship between echo signals, the signal energy accumulation is completed in the correlation function domain, and the target detection statistics related to the accumulated signal characteristics are constructed. The computational complexity is lower than that of the parameter search type method, but the detection probability is reduced.

[0004] In general, for high-order parameter estimation and compensation, the existing high-performance long-term coherent accumulation method involves high-dimensional optimization problems, has high computational complexity, and is difficult to meet the actual needs of airborne dual-base cooperative detection. The essential reason is that it does not consider the differences in the formation mechanism of range migration and Doppler migration, and directly uses a single fine granularity to estimate the optimal compensation functions such as speed and acceleration based on the minimum granularity criterion, resulting in a coupled estimation problem between the range and Doppler of the target motion parameters. Therefore, there is a large amount of computational redundancy and the algorithm has low degrees of freedom. Although the computational complexity of another large category of non-parametric search processing methods is greatly reduced, the performance also drops sharply. Therefore, for the detection needs of airborne dual-base radars, it is urgent to study an efficient long-term coherent accumulation detection method for maneuvering targets. On the one hand, its computational complexity can meet the actual system requirements, and on the other hand, its detection performance loss is small. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a long-term coherent integration detection method for maneuvering targets of an airborne bistatic radar with low computational complexity and high degree of implementation freedom.

[0006] In order to achieve the above object, the technical solution provided by the present invention is: an efficient long-term coherent integration detection method for maneuvering targets by airborne bistatic radar, comprising the following steps:

[0007] Step 1: Based on the airborne bistatic radar parameters and the motion characteristics of the maneuvering target, construct the slant range variable search space between the radar and the maneuvering target and motion parameter variable search space in: They represent the target radial distance variable search space, radial velocity variable search space, radial acceleration variable search space and radial acceleration rate variable search space respectively; c 0,min and c 0,max Respectively represent the minimum and maximum values ​​of the target radial distance variable; c 1,min and c 1,max Respectively represent the minimum and maximum values ​​of the target radial velocity variable; c 2,min and c 2,max Respectively represent the minimum and maximum values ​​of the target radial acceleration variable; c 3,min and c 3,max Respectively represent the minimum and maximum values ​​of the target radial acceleration variable;

[0008] Step 2: Variable search space of target motion parameters and Perform dual-scale decomposition to obtain the target coarse-scale motion parameter variable search space determined by the minimum scale of motion parameters required by range migration correction The search space of target fine-scale motion parameter variables is determined by the minimum motion parameter scale required by Doppler motion correction. in: They represent the target coarse-scale radial velocity variable search space, the coarse-scale radial acceleration variable search space, and the coarse-scale radial acceleration rate variable search space respectively; They represent the target fine-scale radial velocity variable search space, the fine-scale radial acceleration variable search space and the fine-scale radial acceleration rate variable search space respectively.

[0009] 2.1. Double-scale decomposition formula of target motion parameter variables:

[0010]

[0011] in: is the target p-order motion parameter variable; is the target coarse-scale p-order motion parameter variable, which is determined by the minimum scale of motion parameters required by range migration correction; is the target fine-scale p-order motion parameter variable, which is determined by the minimum scale of motion parameters required by Doppler motion correction and is the value after range motion correction. The remainder.

[0012] Specifically, to avoid range migration, the target coarse-scale p-order motion parameter variable is defined as:

[0013]

[0014] Among them: round(·) means rounding operation; c p represents the true value of the target p-order motion parameter; c p,min represents the minimum value of the target p-order motion parameter variable search space; the target coarse-scale p-order motion parameter search interval Δc p,c =Δ RM,p ; Δ RM,p is the minimum interval on the distance unit to ensure that no p-order distance migration occurs:

[0015]

[0016] Where: 0≤α p ≤1, p=1,2,3 satisfies c is the speed of light; f s is the fast-time sampling frequency.

[0017] To avoid Doppler migration, the target fine-scale p-order motion parameter variable is defined as:

[0018]

[0019] Where: The target fine-scale p-order motion parameter search interval Δc p,f =Δ DFM,p , Δ DFM,p is the minimum interval on the Doppler unit to ensure that no p-order Doppler migration occurs:

[0020]

[0021] Where: f c is the carrier frequency.

[0022] 2.2. Based on the double-scale decomposition formula of target motion parameters, the target motion parameter variable search space is double-scaled to obtain the target coarse-scale motion parameter search space and the target fine-scale motion parameter search space. The target coarse-scale p-order motion parameter variable search space is defined as follows:

[0023]

[0024] Where: c p,min and c p,max The meanings when p=1, 2, and 3 are as shown in step 1.

[0025] The target fine-scale p-order motion parameter variable search space is defined as follows:

[0026]

[0027] Step 3: For an array of coarse-scale motion parameter variables In the target coarse-scale motion parameter search space, the echo signal y(f k ,t m ) to perform modified Generalized Inverse Fourier Transform (mGIFT):

[0028]

[0029] 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, m=1,…,M represents slow time, PRT is pulse repetition time; M is the number of pulses; τ n =n / f s (n=1,…,K) represents fast time; f s is the fast time domain sampling frequency; echo signal A represents the complex amplitude of the target in the frequency domain; c0, c1, c2 and c3 represent the radial distance, radial velocity, radial acceleration and radial acceleration rate of the target at the initial moment respectively; is the target coarse-scale motion parameter distance migration compensation function; is the Doppler migration pre-compensation function of the target coarse-scale motion parameter; λ is the carrier wavelength; the complex amplitude of the signal after mGIFT transformation c′ p,f =κc p,f is the target fine-scale p-order motion parameter transformed by the scale factor κ, and the scale factor

[0030] Step 4: Radial distance The corresponding slow time domain signal The conditional cubic phase function (CCPF) is constructed in the target fine-scale motion parameter variable search space, and the target fine-scale motion parameter estimation is obtained through a series of calculations. in: represents the target radial distance variable; c represents the speed of light; is the target radial distance variable The corresponding fast time variable; are the estimated target fine-scale radial acceleration and fine-scale radial acceleration rate, respectively.

[0031] 4.1 CCPF Transformation

[0032] Set the slow time position ρ1 = M / 2, ρ2 = M / 2 + floor (0.11M), and let M′ = M-ρ2, then the slow time and Department, CCPF Transform and The definition is as follows:

[0033]

[0034] Where: i = 1, 2; t ι = ι·PRT; Ω i Indicates slow time The corresponding instantaneous frequency change rate variable; (·) * represents complex conjugate; floor(·) represents floor operation.

[0035] Therefore, the peak position of the CCPF spectrum is located at Place.

[0036] 4.2 Estimation of instantaneous frequency change rate

[0037] First, the instantaneous frequency change rate variable search space Defined as:

[0038]

[0039] Where: The instantaneous frequency change rate variable search interval is defined as:

[0040]

[0041] Secondly, let The maximum likelihood estimation criterion (MLE) is used to search the instantaneous frequency change rate variable space for and Estimate the instantaneous frequency change rate and get and Right now:

[0042]

[0043] 4.3. Detailed Parameter Estimation

[0044] From 4.1, we know that Therefore, through and The intersection of the two straight lines can estimate the fine-scale motion parameters of the target and The matrix representation is as follows:

[0045]

[0046] in: is the target fine-scale radial acceleration estimate transformed by the scale factor κ and fine-scale radial acceleration rate estimates The array composed of is the target fine-scale radial acceleration estimate and fine-scale radial acceleration rate estimates The array formed;

[0047] Step 5: Initialize the target fine-scale motion parameter array based on the target fine-scale radial acceleration and fine-scale radial acceleration rate estimated in step 4 right Perform a generalized Fourier transform (GFT) to obtain the temporary range-Doppler (RD) spectrum:

[0048]

[0049] in: represents the target fine-scale radial velocity variable after transformation by the scale factor κ; the scale factor κ is the coefficient in the solution process of step 4; is the target fine-scale radial velocity variable; Is and The array formed; λ is the carrier wavelength; is the Doppler migration compensation function of the target fine-scale motion parameter; p represents the order of the target radial motion parameter; when p = 2 and p = 3, Equal to and

[0050] Step 6: Based on the temporary RD spectrum plane obtained in step 5, determine whether there is a target using the likelihood ratio test (LRT) method:

[0051]

[0052] Where: γ LRT is the threshold of the likelihood ratio test; H0 is the null hypothesis of LRT; H1 is the alternative hypothesis of LRT. If LRT is established, all radial motion parameters of the target are estimated: target radial distance estimate Target radial velocity estimate Target radial acceleration estimate Target radial acceleration rate estimate c′ is obtained by the likelihood ratio test 1,f The estimated value, and its relationship with the estimated value of the target radial velocity is: is the estimated radial velocity of the target.

[0053] Step 7: Under the target coarse-scale motion parameters in step 3, if the target radial distance variable search space is not traversed Then return to step 4; if the target radial distance variable search space has been traversed Then proceed to the next step.

[0054] Step 8: If the target coarse-scale motion parameter variable search space in step 3 is not traversed Then return to step 3; if the target coarse-scale motion parameter variable search space has been traversed Then proceed to the next step.

[0055] Step 9: Based on the estimated coarse-scale motion parameters of all targets from Steps 1 to 8 Target fine-scale motion parameters And target radial distance j=1,…,J, J represents the number of targets, and estimates the radial distances and all motion parameters of all targets. in: and They represent the radial distance estimate, coarse-scale radial velocity estimate, coarse-scale radial acceleration estimate and coarse-scale radial acceleration rate estimate of the j-th target respectively; and They represent the fine-scale radial velocity estimate, fine-scale radial acceleration estimate and fine-scale radial acceleration rate estimate of the j-th target respectively; j=1,…,J represent the radial velocity estimate, radial acceleration estimate and radial acceleration rate estimate of the jth target respectively.

[0056] Using all target radial distances and all motion parameter estimates, the target range migration and Doppler migration are compensated to estimate the target RD spectrum:

[0057]

[0058] in: represents the complex amplitude after GIFT and GFT of the jth target; is the estimated Doppler frequency of the jth target, that is The asinc function is the discrete form of the sinc function. 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 is the number of effective frequency units; PRF = 1 / PRT is the pulse repetition frequency; is the radial distance of the jth target.

[0059] The target motion parameters And RD spectrum is used for subsequent radar signal processing.

[0060] The beneficial effects of the present invention relative to the prior art are:

[0061] 1) Improve the algorithm's degree of freedom through dual-scale decomposition of motion parameter variables;

[0062] 2) In the target coarse-scale motion parameter variable search space, the range migration is corrected by GIFT transformation to achieve complete range migration compensation without ignoring the range migration caused by the motion parameter residue;

[0063] 3) The target's high-order fine-scale motion parameters are estimated through CCPF transformation in the target's fine-scale motion parameter variable search space. Compared with the MPCPF (Modified Product Cubic Phase Function) transformation based on the single-scale motion parameter variable search space, CCPF can suppress the cross-terms generated between multiple targets at the same distance, and has high parameter estimation accuracy. At the same time, it inherits the advantage of the MPCPF algorithm in reducing high-dimensional joint search to multiple one-dimensional searches.

[0064] 4) The LRT criterion is used to detect targets on the RD spectrum, and the cross terms between multiple targets at the same distance are suppressed by threshold limiting, thereby reducing false target detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of an efficient long-term coherent integration detection method for airborne bistatic radar maneuvering targets provided by one embodiment of the present invention;

[0066] Figure 2 It is a flowchart of a CCPF algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Reference Figure 1 and Figure 2 This embodiment provides an efficient long-term coherent integration detection method for airborne bistatic radar maneuvering targets, comprising the following steps:

[0069] Step 1: Based on the airborne bistatic radar parameters and the motion characteristics of the maneuvering target, construct the slant range variable search space between the radar and the maneuvering target and motion parameter variable search space in: They represent the target radial distance variable search space, radial velocity variable search space, radial acceleration variable search space and radial acceleration rate variable search space respectively; c 0,min and c 0,max Respectively represent the minimum and maximum values ​​of the target radial distance variable; c 1,min and c 1,max Respectively represent the minimum and maximum values ​​of the target radial velocity variable; c 2,min and c 2,maxRespectively represent the minimum and maximum values ​​of the target radial acceleration variable; c 3,min and c 3,max Respectively represent the minimum and maximum values ​​of the target radial acceleration variable;

[0070] Step 2: Variable search space of target motion parameters and Perform dual-scale decomposition to obtain the target coarse-scale motion parameter variable search space determined by the minimum scale of motion parameters required by range migration correction The search space of target fine-scale motion parameter variables is determined by the minimum motion parameter scale required by Doppler motion correction. in: They represent the target coarse-scale radial velocity variable search space, the coarse-scale radial acceleration variable search space, and the coarse-scale radial acceleration rate variable search space respectively; They represent the target fine-scale radial velocity variable search space, the fine-scale radial acceleration variable search space and the fine-scale radial acceleration rate variable search space respectively.

[0071] 2.1. Double-scale decomposition formula of target motion parameter variables:

[0072]

[0073] in: is the target p-order motion parameter variable; is the target coarse-scale p-order motion parameter variable, which is determined by the minimum scale of motion parameters required by range migration correction; is the target fine-scale p-order motion parameter variable, which is determined by the minimum scale of motion parameters required by Doppler motion correction and is the value after range motion correction. The remainder.

[0074] Specifically, to avoid range migration, the target coarse-scale p-order motion parameter variable is defined as:

[0075]

[0076] Among them: round(·) means rounding operation; c p represents the true value of the target p-order motion parameter; c p,min represents the minimum value of the target p-order motion parameter variable search space; the target coarse-scale p-order motion parameter search interval Δc p,c =Δ RM,p ; Δ RM,p is the minimum interval on the distance unit to ensure that no p-order distance migration occurs:

[0077]

[0078] Where: 0≤α p ≤1, p=1,2,3 satisfies c is the speed of light; f s is the fast-time sampling frequency.

[0079] To avoid Doppler migration, the target fine-scale p-order motion parameter variable is defined as:

[0080]

[0081] Where: The target fine-scale p-order motion parameter search interval Δc p,f =Δ DFM,p , Δ DFM,p is the minimum interval on the Doppler unit to ensure that no p-order Doppler migration occurs:

[0082]

[0083] Where: f c is the carrier frequency.

[0084] 2.2. Based on the double-scale decomposition formula of target motion parameters, the target motion parameter variable search space is double-scaled to obtain the target coarse-scale motion parameter search space and the target fine-scale motion parameter search space. The target coarse-scale p-order motion parameter variable search space is defined as follows:

[0085]

[0086] Where: c p,min and c p,max The meanings when p=1, 2, and 3 are as shown in step 1.

[0087] The target fine-scale p-order motion parameter variable search space is defined as follows:

[0088]

[0089] Step 3: For an array of coarse-scale motion parameter variables In the target coarse-scale motion parameter search space, the echo signal y(f k ,t m ) to perform modified Generalized Inverse Fourier Transform (mGIFT):

[0090]

[0091] 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, m=1,…,M represents slow time, PRT is pulse repetition time; M is the number of pulses; τ n =n / f s (n=1,…,K) represents fast time; f s is the fast time domain sampling frequency; echo signal A represents the complex amplitude of the target in the frequency domain; c0, c1, c2 and c3 represent the radial distance, radial velocity, radial acceleration and radial acceleration rate of the target at the initial moment respectively; is the target coarse-scale motion parameter distance migration compensation function; is the Doppler migration pre-compensation function of the target coarse-scale motion parameter; λ is the carrier wavelength; the complex amplitude of the signal after mGIFT transformation c′ p,f =κc p,f is the target fine-scale p-order motion parameter transformed by the scale factor κ, and the scale factor

[0092] Step 4: Radial distance The corresponding slow time domain signal The conditional cubic phase function (CCPF) is constructed in the target fine-scale motion parameter variable search space, and the target fine-scale motion parameter estimation is obtained through a series of calculations. in: represents the target radial distance variable; c represents the speed of light; is the target radial distance variable The corresponding fast time variable; are the estimated target fine-scale radial acceleration and fine-scale radial acceleration rate, respectively.

[0093] 4.1 CCPF Transformation

[0094] Set the slow time position ρ1 = M / 2, ρ2 = M / 2 + floor (0.11M), and let M′ = M-ρ2, then the slow time and Department, CCPF Transform and The definition is as follows:

[0095]

[0096] Where: i = 1, 2; t ι = ι·PRT; Ω i Indicates slow time The corresponding instantaneous frequency change rate variable; (·) *represents complex conjugate; floor(·) represents floor operation.

[0097] Therefore, the peak position of the CCPF spectrum is located at Place.

[0098] 4.2 Estimation of instantaneous frequency change rate

[0099] First, the instantaneous frequency change rate variable search space Defined as:

[0100]

[0101] Where: The instantaneous frequency change rate variable search interval is defined as:

[0102]

[0103] Secondly, let The maximum likelihood estimation criterion (MLE) is used to search the instantaneous frequency change rate variable space for and Estimate the instantaneous frequency change rate and get and Right now:

[0104]

[0105] 4.3. Detailed Parameter Estimation

[0106] From 4.1, we know that Therefore, through and The intersection of the two straight lines can estimate the fine-scale motion parameters of the target and The matrix representation is as follows:

[0107]

[0108] in: is the target fine-scale radial acceleration estimate transformed by the scale factor κ and fine-scale radial acceleration rate estimates The array composed of is the target fine-scale radial acceleration estimate and fine-scale radial acceleration rate estimates The array formed;

[0109] Step 5: Initialize the target fine-scale motion parameter array based on the target fine-scale radial acceleration and fine-scale radial acceleration rate estimated in step 4 right Perform a generalized Fourier transform (GFT) to obtain the temporary range-Doppler (RD) spectrum:

[0110]

[0111] in: represents the target fine-scale radial velocity variable after transformation by the scale factor κ; the scale factor κ is the coefficient in the solution process of step 4; is the target fine-scale radial velocity variable; Is and The array formed; λ is the carrier wavelength; is the Doppler migration compensation function of the target fine-scale motion parameter; p represents the order of the target radial motion parameter; when p = 2 and p = 3, Equal to and

[0112] Step 6: Based on the temporary RD spectrum plane obtained in step 5, determine whether there is a target using the likelihood ratio test (LRT) method:

[0113]

[0114] Where: γ LRT is the threshold of the likelihood ratio test; H0 is the null hypothesis of LRT; H1 is the alternative hypothesis of LRT. If LRT is established, all radial motion parameters of the target are estimated: target radial distance estimate Target radial velocity estimate Target radial acceleration estimate Target radial acceleration rate estimate c′ is obtained by the likelihood ratio test 1,f The estimated value, and its relationship with the estimated value of the target radial velocity is: is the estimated radial velocity of the target.

[0115] Step 7: Under the target coarse-scale motion parameters in step 3, if the target radial distance variable search space is not traversed Then return to step 4; if the target radial distance variable search space has been traversed Then proceed to the next step.

[0116] Step 8: If the target coarse-scale motion parameter variable search space in step 3 is not traversed Then return to step 3; if the target coarse-scale motion parameter variable search space has been traversed Then proceed to the next step.

[0117] Step 9: Based on the estimated coarse-scale motion parameters of all targets from Steps 1 to 8 Target fine-scale motion parameters And target radial distance j=1,…,J, J represents the number of targets, and estimates the radial distances and all motion parameters of all targets. in: and They represent the radial distance estimate, coarse-scale radial velocity estimate, coarse-scale radial acceleration estimate and coarse-scale radial acceleration rate estimate of the j-th target respectively; and They represent the fine-scale radial velocity estimate, fine-scale radial acceleration estimate and fine-scale radial acceleration rate estimate of the j-th target respectively; j=1,…,J represent the radial velocity estimate, radial acceleration estimate and radial acceleration rate estimate of the jth target respectively.

[0118] Using all target radial distances and all motion parameter estimates, the target range migration and Doppler migration are compensated to estimate the target RD spectrum:

[0119]

[0120] in: represents the complex amplitude after GIFT and GFT of the jth target; is the estimated Doppler frequency of the jth target, that is The asinc function is the discrete form of the sinc function. 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 is the number of effective frequency units; PRF = 1 / PRT is the pulse repetition frequency; is the radial distance of the jth target.

[0121] The target motion parameters And RD spectrum is used for subsequent radar signal processing.

[0122] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An efficient long-term coherent integration detection method for maneuvering targets using airborne bistatic radar, characterized in that: The following steps are involved: Step 1: Based on the airborne bistatic radar parameters and the relative motion characteristics of the maneuvering target, construct the slant range variable search space between the airborne bistatic radar and the maneuvering target and the variable search space of radial motion parameters of maneuvering targets in: They represent the radial distance variable search space, radial velocity variable search space, radial acceleration variable search space and radial acceleration rate variable search space of the maneuvering target respectively; c 0,min and c 0,max Respectively represent the minimum and maximum values ​​of the target radial distance variable; c 1,min and c 1,max Respectively represent the minimum and maximum values ​​of the target radial velocity variable; c 2,min and c 2,max Respectively represent the minimum and maximum values ​​of the target radial acceleration variable; c 3,min and c 3,max Respectively represent the minimum and maximum values ​​of the target radial acceleration variable; Step 2: Search space of maneuvering target motion parameter variables and Perform dual-scale decomposition to obtain the target coarse-scale motion parameter variable search space determined by the minimum scale of motion parameters required by range migration correction The search space of target fine-scale motion parameter variables is determined by the minimum motion parameter scale required by Doppler motion correction. in: They represent the target coarse-scale radial velocity variable search space, the coarse-scale radial acceleration variable search space, and the coarse-scale radial acceleration rate variable search space respectively; They represent the target fine-scale radial velocity variable search space, the fine-scale radial acceleration variable search space, and the fine-scale radial acceleration rate variable search space respectively; Step 3: For an array of target coarse-scale motion parameter variables In the target coarse-scale motion parameter variable search space, the echo signal y(f k ,t m ) performs mGIFT transformation to obtain the fast time domain-slow time domain signal in: and denote the target coarse-scale radial velocity variable, coarse-scale radial acceleration variable and coarse-scale radial acceleration rate variable respectively; Indicated by and The array formed; 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, m=1,…,M represents slow time, PRT is pulse repetition time; M is the number of pulses; τ n =n / f s (n=1,…,K) represents fast time; f s is the fast time domain sampling frequency; Step 4: Radial distance The corresponding slow time domain signal CCPF transformation is performed in the target fine-scale motion parameter variable search space, and after a series of calculations, the target fine-scale motion parameter estimation is obtained. in: represents the target radial distance variable; c represents the speed of light; is the target radial distance variable The corresponding fast time variable; are the estimated target fine-scale radial acceleration and fine-scale radial acceleration rate, respectively; Step 5: Initialize the target fine-scale motion parameter array based on the target fine-scale radial acceleration and fine-scale radial acceleration rate estimated in step 4 right Perform GFT transformation to obtain the temporary range-Doppler spectrum: in: represents the target fine-scale radial velocity variable after transformation by the scale factor κ; the scale factor κ is the coefficient in the solution process of step 4; is the target fine-scale radial velocity variable; Is and The array formed; λ is the carrier wavelength; is the Doppler migration compensation function of the target fine-scale motion parameter; p represents the order of the target radial motion parameter; when p = 2 and p = 3, Equal to and Step 6: Based on the temporary range-Doppler spectrum plane obtained in step 5, determine whether there is a target according to the likelihood ratio test. If there is a target, estimate all the motion parameters of the target. Step 7: Under the target coarse-scale motion parameters in step 3, if the target radial distance variable search space is not traversed Then return to step 4; if the target radial distance variable search space has been traversed Then proceed to the next step; Step 8: If the target coarse-scale motion parameter variable search space in step 3 is not traversed Then return to step 3; if the target coarse-scale motion parameter variable search space has been traversed Then proceed to the next step; Step 9: Based on the estimated coarse-scale motion parameters of all targets from Steps 1 to 8 Target fine-scale motion parameters And target radial distance j=1,…,J, J represents the number of targets, and estimates the radial distances and all motion parameters of all targets. in: and They represent the radial distance estimate, coarse-scale radial velocity estimate, coarse-scale radial acceleration estimate and coarse-scale radial acceleration rate estimate of the j-th target respectively; and They represent the fine-scale radial velocity estimate, fine-scale radial acceleration estimate and fine-scale radial acceleration rate estimate of the j-th target respectively; j=1,…,J represent the radial velocity estimate, radial acceleration estimate and radial acceleration rate estimate of the jth target respectively; Using all target radial distances and all motion parameter estimates, the range migration and Doppler migration caused by the radial motion parameters of the maneuvering target are compensated, and the range-Doppler spectrum of the maneuvering target is estimated: in: represents the complex amplitude after GIFT and GFT of the jth target; is the estimated Doppler frequency of the jth target, that is The asinc function is the discrete form of the sinc function. 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 is the number of effective frequency units; PRF = 1 / PRT is the pulse repetition frequency; is the radial distance of the jth target; The motion parameters of the maneuvering target and range-Doppler spectrum for subsequent radar signal processing.

2. The efficient long-term coherent integration detection method for maneuvering targets by airborne bistatic radar as claimed in claim 1, characterized in that: In step 2, the target motion parameter dual-scale decomposition method includes: Step 2.1: Dual-scale decomposition formula of maneuvering target motion parameter variables: in: is the target p-order motion parameter variable; is the target coarse-scale p-order motion parameter variable, which is determined by the minimum scale of motion parameters required by range migration correction; is the target fine-scale p-order motion parameter variable, which is determined by the minimum scale of motion parameters required by Doppler motion correction and is the value after range motion correction. the remainder; The target coarse-scale p-order motion parameter variable is defined as: Among them: round(·) means rounding operation; c p represents the true value of the target p-order motion parameter; c p,min represents the minimum value of the target p-order motion parameter variable search space; the target coarse-scale p-order motion parameter search interval Δc p,c =Δ RM,p ; Δ RM,p is the minimum interval on the distance unit to ensure that no p-order distance migration occurs: Where: 0≤α p ≤1, p=1,2,3 satisfies c is the speed of light; f s is the fast-time sampling frequency; The target fine-scale p-order motion parameter variable is defined as: Where: The target fine-scale p-order motion parameter search interval Δc p,f =Δ DFM,p , Δ DFM,p is the minimum interval on the Doppler unit to ensure that no p-order Doppler migration occurs: Where: f c is the carrier frequency; Step 2.2: Based on the double-scale decomposition formula of the target motion parameter, the target motion parameter variable search space is double-scaled to obtain the target coarse-scale motion parameter search space and the target fine-scale motion parameter search space; the target coarse-scale p-order motion parameter variable search space is defined as follows: The target fine-scale p-order motion parameter variable search space is defined as follows: 。 3. The efficient long-term coherent integration detection method for airborne bistatic radar maneuvering targets as claimed in claim 1, characterized in that: The mGIFT transformation formula in step 3 is: For an array of coarse-scale motion parameter variables In the target coarse-scale motion parameter search space, the echo signal y(f k ,t m ) to perform mGIFT transformation: Among them: echo signal A represents the complex amplitude of the target in the frequency domain; c0, c1, c2 and c3 represent the radial distance, radial velocity, radial acceleration and radial acceleration rate of the target at the initial moment respectively; is the target coarse-scale motion parameter distance migration compensation function; is the Doppler migration pre-compensation function of the target coarse-scale motion parameter; λ is the carrier wavelength; the complex amplitude of the signal after mGIFT transformation c′ p,f =κc p,f ,p=1,2,3 are the target fine-scale p-order motion parameters after transformation by the scale factor κ, and the scale factor 4. The efficient long-term coherent integration detection method for maneuvering targets by airborne bistatic radar as claimed in claim 1, characterized in that: The method for estimating target fine-scale motion parameters through CCPF transformation in step 4 includes: Step 4.1: CCPF transformation Set the slow time position ρ1 = M / 2, ρ2 = M / 2 + floor (0.11M), and let M′ = M-ρ2, then the slow time and Department, CCPF Transform and The definition is as follows: Where: i = 1, 2; t ι = ι·PRT; Ω i Indicates slow time The corresponding instantaneous frequency change rate variable; (·) * represents complex conjugate; floor(·) represents floor operation; Therefore, the peak position of the CCPF spectrum is located at Ω i =2c′ 2,f +6c′ 3,f t ρi Department; Step 4.2: Estimation of instantaneous frequency change rate First, the instantaneous frequency change rate variable search space Defined as: Where: The instantaneous frequency change rate variable search interval is defined as: Secondly, let Using the maximum likelihood estimation criterion, we can find the instantaneous frequency change rate variable search space. and Estimate the instantaneous frequency change rate and get and Right now: Step 4.3: Fine-scale motion parameter estimation From step 4.1, we know that Therefore, through and The intersection of the two straight lines can estimate the fine-scale motion parameters of the target and The matrix representation is as follows: in: is the target fine-scale radial acceleration estimate transformed by the scale factor κ and fine-scale radial acceleration rate estimates The array composed of is the target fine-scale radial acceleration estimate and fine-scale radial acceleration rate estimates The array formed; 5. The efficient long-term coherent integration detection method for maneuvering targets by airborne bistatic radar as claimed in claim 1, characterized in that: The likelihood ratio test formula in step 6 is as follows: Where: γ LRT is the threshold of the likelihood ratio test; H0 is the null hypothesis of the likelihood ratio test; H1 is the alternative hypothesis of the likelihood ratio test; if the likelihood ratio test is established, all radial motion parameters of the target are estimated: target radial distance estimate Target radial velocity estimate Target radial acceleration estimate Target radial acceleration rate estimate c′ is obtained by the likelihood ratio test 1,f The estimated value, and its relationship with the estimated value of the target radial velocity is: is the estimated radial velocity of the target.

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