A Maneuvering Target Detection and Parameter Estimation Method for Wideband Radar

Abstract

The present invention discloses a method for detecting and estimating maneuvering targets for broadband radar. The main steps include: S1: dividing the target velocity search parameter space at equal intervals and shifting the velocity component in the pulse pressure signal to the center of the parameter space; S2: dividing the spectrum of the pulse pressure signal into several narrow frequency bands and extracting them in sequence; S3: applying the MGWO algorithm to the extracted sub-aperture pulse pressure signals to obtain a rough estimate of the target maneuvering parameters of the corresponding sub-aperture; S4: applying least squares estimation to the full aperture to obtain a precise estimate of the target parameters; S5: constructing a corresponding high-order phase compensation function based on the precise estimate to complete Doppler frequency compensation, and performing a first-order Keystone transform on the compensated signal to complete range-azimuth decoupling and complete coherent accumulation of target energy. The present invention effectively solves the problems of range and Doppler movement and can achieve long-term coherent accumulation of maneuvering targets. The invention has good real-time performance, strong noise robustness, and certain engineering practical value.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing, and in particular to a method for detecting and estimating maneuvering targets and parameters for wideband radar. Background Art

[0002] With the development of new radar technologies, wideband radar has attracted extensive attention and research in recent years due to its precise target recognition and imaging capabilities. In the wideband radar system, targets are typically considered to be composed of multiple physical scatterers. Unlike the ideal point target model used in narrowband radar, wideband radar, with its high range resolution, can acquire a high-resolution one-dimensional range profile of the target, enabling target detection, imaging, classification, and recognition. To obtain a clear target range profile, the echo signal-to-noise ratio (SNR) must be sufficiently high. However, current wideband radars still face the following challenges and difficulties: First, compared with narrowband radars, targets in wideband radars are no longer ideal point targets; their scatterers are dispersed across multiple range bins, resulting in weak echo SNR. Second, because the range resolution bins of wideband radars are much smaller than those of narrowband radars, slow-moving targets and those crossing range bins are still technically more susceptible to cross-range bin problems. Finally, because the carrier frequency of wideband radars is typically higher, Doppler frequency spread is more pronounced when processing maneuvering targets.

[0003] Traditional moving target detection algorithms are limited by the target's dwell time within a single range bin and its motion type, resulting in significant signal-to-noise ratio degradation and making them unsuitable for wideband radar systems. While algorithms based on the generalized Radon Fourier transform, developed for narrowband radars, can continue to be applied to wideband radars, they require multidimensional joint search and inherently high sampling rates, making them of little practical value. Some researchers have proposed the variable-scale moving target detection (VSMTD) algorithm for wideband radars. However, this algorithm is only applicable to targets with uniform speed and does not consider their maneuvering characteristics. Others have proposed the subband Keystone transform-extended Lu distribution (SKT-ELVD) algorithm. However, this algorithm does not consider frequency band extraction for high-speed targets. For high-speed targets, it cannot guarantee that the target energy falls within a single range bin within the subband. Furthermore, the incoherent integration between subbands also results in a certain loss of integration gain.

[0004] In summary, it is of great value to design an effective wideband radar maneuvering target detection and parameter estimation algorithm. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the present invention provides a method for maneuvering target detection and parameter estimation for wideband radar, which solves the problem of low accumulated signal-to-noise ratio caused by range variation, Doppler variation and scattering point dispersion in maneuvering target detection in the prior art, and can achieve a compromise between detection performance and computational efficiency.

[0006] In order to achieve the above-mentioned purpose of the invention, the technical solutions adopted to solve the technical problems are as follows:

[0007] The present invention discloses a method for detecting and estimating maneuvering targets and parameters for a wideband radar, comprising the following steps:

[0008] Step S1: Determine the target speed search range, divide the target speed search parameter space into equal intervals, and shift the speed component in the pulse pressure signal to the center of the parameter space by phase compensation;

[0009] Step S2: Divide the spectrum of the frequency domain pulse compression signal into several narrow frequency bands and extract them sequentially;

[0010] Step S3: applying the MGWO algorithm to the extracted sub-aperture pulse pressure signals in sequence to obtain a rough estimate of the target maneuvering parameter of the corresponding sub-aperture;

[0011] Step S4: applying least squares estimation to the full aperture to obtain a precise estimation value after the estimation values ​​of each sub-aperture are fused;

[0012] Step S5: Construct a corresponding high-order phase compensation function based on the precise estimation value to complete Doppler frequency compensation, and perform a first-order Keystone transform on the compensated signal to complete range-azimuth decoupling;

[0013] Step S6: Perform a range-wise fast inverse Fourier transform and an azimuth-wise fast Fourier transform on the signal to complete the coherent accumulation of target energy, and confirm the presence of the target through constant false alarm rate detection.

[0014] Furthermore, the step S1 includes the following steps:

[0015] Step S11: Determine the search range of the target speed [v L ,v R ] and the subspace length Δv, and the number of subspaces is calculated as K = (v R -v L ) / Δv;

[0016] Step S12: For any subspace i (i∈[1,K]), construct a corresponding phase compensation function to translate the velocity contained in subspace i to the center v of the search parameter space center =[-Δv / 2,Δv / 2]:

[0017]

[0018] Where f is the fast time frequency, t m =mT r ,m=1,2,...,M is the slow time series, f c is the carrier frequency, c is the speed of light, v i is the velocity translation: And perform IFFT on the compensated signal to find the maximum amplitude of all range gates of the current pulse compression signal:

[0019]

[0020] Among them, IFFT f (·) is to do IFFT along the fast time frequency;

[0021] Step S13: Using the above formula as the objective function with a single extreme value, the bisection method is applied to the K subspaces. Without traversal search, the subspace i0 where the target true speed is located can be quickly determined, that is, the optimal subspace. The evaluation criteria are:

[0022]

[0023] Step S14: Construct a phase compensation function corresponding to the subspace index value i0 and multiply it with the original pulse pressure signal to obtain the compensation result S T (f,t m ), so that the residual velocity component after compensation falls in the subspace at the center of the parameter space.

[0024] Furthermore, step S13 includes the following steps:

[0025] Step S13-1: Let i min =1 and i max =K, calculate the index value of the bisection point: i mid =round((1+K) / 2), set the threshold value ε=1;

[0026] Step S13-2: Calculate the range gate amplitude of the pulse pressure signal after compensation of the subspaces around the bisection point and And calculate the difference between the two

[0027] Step S13-3: Determine whether ΔA is positive or negative. If ΔA>0, then otherwise

[0028] Step S13-4: If |i max -i min|≤ε, then the optimal subspace index is determined to be i0=i mid Otherwise, steps S13-1 to S13-3 are repeated until the above conditions are met.

[0029] Furthermore, the expression of the sub-aperture signal after the signal spectrum is divided and extracted in step S2 is:

[0030]

[0031] Among them, B s =B / N is the sub-aperture bandwidth, N is the number of divided sub-apertures, f c (n) = f c +(nN / 2-0.5)×ΔL×Δf, n=1,2,...,N is the carrier frequency of the nth subaperture, ΔL=L / N is the number of fast time sampling points contained in each subaperture, Δf=f s / L is the fast time sampling interval, L is the total number of sampling points in the fast time dimension of each pulse, and the fast time frequency range of this subaperture can be expressed as -B / 2+[1+(n-1)×ΔL]×Δf≤f≤-B / 2+n×ΔL×Δf

[0032] After spectrum division, the range resolution of each sub-aperture signal can be expressed as:

[0033]

[0034] After the range resolution is expanded N times, the target energy originally dispersed in several range units is concentrated in the same widened range unit. Therefore, in a longer coherent processing interval, the range movement of the target will not occur.

[0035] Furthermore, step S3 includes the following steps:

[0036] Step S31: Initialize the population size N, parameter optimization dimension D, maximum number of iterations P, convergence factor a, random vectors A and C, gray wolf population X (x1, x2, ..., x N ) and the position X of each gray wolf i =(x i1 ,x i2 ,…,x iD ) T ,i=1,2,…,N;

[0037] Step S32: Construct the corresponding phase compensation function:

[0038]

[0039] Among them, c1 and c2 are the search values ​​of first-order acceleration and second-order acceleration;

[0040] S sub (f,t m ; n) after multiplying with the phase compensation function and performing IFFT transformation to the time domain, s is obtained sub (τ,t m ; n), and take the maximum accumulated peak value as the objective function after doing FFT along the slow time:

[0041]

[0042] Calculate the objective function values ​​of all gray wolf positions after initialization, and mark the current three best individuals α, β and δ according to the sorting results of the objective function values;

[0043] Step S33: Calculate the distance between the remaining ω wolves and the three best individuals, and guide the remaining gray wolf individuals ω to move;

[0044] Step S34: Update the positions of α, β, δ and the objective function value according to the current position of the wolf pack;

[0045] Step S35: When the number of iterations reaches the maximum value, output the optimal solution X i , which is the estimated value of motion parameters;

[0046] Step S36: Repeat the above operation for each extracted sub-aperture signal to obtain N sets of motion parameter estimation values.

[0047] Furthermore, in step S31, in order to improve the global convergence speed of the optimization algorithm and balance the local search capability of the optimization algorithm, the convergence factor a is changed from linearly decreasing to nonlinear exponentially decreasing, and its expression is:

[0048] Furthermore, in step S33, in order to distinguish the leadership of the top three levels of gray wolves in the entire wolf pack, the inertia weight is introduced into the wolf pack position update to speed up the algorithm convergence speed. The new position update formula is:

[0049]

[0050] in, is the weight coefficient.

[0051] Furthermore, step S4 includes the following steps:

[0052] Step S41: Construct a measurement matrix Ψ containing the instantaneous Doppler frequencies of all sub-apertures according to N groups of estimated values:

[0053] Ψ=[F(1)F(2)…F(N)] T

[0054] Where F(n) is the Doppler frequency vector of the M pulses contained in the nth sub-aperture:

[0055] F(n)=[F(1;n)F(2;n)...F(M;n)]

[0056] Where F(m;n) is the instantaneous Doppler frequency component of the m-th pulse in the n-th subaperture:

[0057]

[0058] Step S42: Construct the theoretical Doppler frequency matrix Θ of the maneuvering target by taking the first-order derivative of the motion model:

[0059]

[0060] Where Q is the highest order of the motion model, and D(n,q) is the theoretical Doppler frequency vector of the M pulses contained in the nth sub-aperture:

[0061] D(n,q)=[D(1;n,q)D(2;n,q)...D(M;n,q)]

[0062] Where D(m;n,q) is the instantaneous Doppler frequency component of the m-th pulse in the n-th subaperture:

[0063]

[0064] Step S43: Perform singular value decomposition on the instantaneous Doppler frequency measurement matrix and the theoretical Doppler frequency matrix:

[0065] [ΨΘ]=USV T

[0066] Step S44: Calculate the Frobenius norm of the matrix Θ:

[0067]

[0068] Among them, s i are all non-zero singular values ​​in the matrix S;

[0069] Next, set a threshold η close to 1, find the smallest integer r0 that satisfies δ(r) ≥ η, and construct the matrix U accordingly. r Used to fit the matrix U:

[0070] U r =[u r o]

[0071] Among them, u r Represents all elements from the 1st column to the r0th column on the left side of the matrix U, o is a zero-filled matrix to ensure that Ur The number of rows and columns is the same as U; if r0>MN, then U r =U;

[0072] Step S45: Using the least squares method, obtain the precise estimate of the high-order motion component after the N groups of estimated values ​​are fused:

[0073]

[0074] Furthermore, step S5 includes the following steps:

[0075] Step S51: Obtain estimated values ​​of first-order and second-order accelerations and Finally, the following phase compensation function is constructed:

[0076]

[0077] Step S52: Combine the above formula with formula S T (f,t m ; i0) after multiplication to compensate for Doppler frequency spread:

[0078]

[0079] Among them, A3 is the complex amplitude value of the signal;

[0080] Step S53: comp (f,t m ) Perform a first-order Keystone transform to decouple the range frequency and azimuth:

[0081]

[0082] Furthermore, step S6 includes the following steps:

[0083] Step S61: Perform IFFT on the above equation along the range frequency to obtain a two-dimensional time domain signal:

[0084]

[0085] Next, perform FFT on the slow time to obtain the coherent accumulation result:

[0086]

[0087] Step S62: The amplitude of the constructed echo signal range-Doppler domain detection unit map is used as a detection statistic and compared with a given false alarm probability adaptive detection threshold:

[0088]

[0089] Where ρ is the adaptive detection threshold, H1 corresponds to the presence of the target, and H0 corresponds to the absence of the target;

[0090] If the energy exceeds a given threshold, the target is determined to exist; based on the location of the energy peak, the estimated value of the target's initial radial distance and residual speed can be obtained.

[0091] The present invention also discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor can implement the steps of the above-mentioned wideband radar maneuvering target detection and parameter estimation method when executing the computer program.

[0092] The present invention further discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned wideband radar maneuvering target detection and parameter estimation method can be implemented.

[0093] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0094] The detection method provided by this invention effectively addresses the range drift and Doppler frequency spread that occur during radar maneuvering target detection, effectively increasing the target echo accumulation gain and enhancing detection performance. Secondly, the invention avoids the traversal search process and employs a metaheuristic algorithm to iteratively obtain estimated values, significantly improving computational efficiency. Finally, the invention employs the least squares method between subapertures to improve parameter estimation accuracy, facilitating subsequent reconstruction of high-resolution one-dimensional range profiles and target identification, thus possessing significant engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work. In the drawings:

[0096] Figure 1 Flowchart of the broadband radar maneuvering target detection and parameter estimation method of the present invention;

[0097] Figure 2 Schematic diagram of search parameter subspace division and translation operation of the present invention;

[0098] Figure 3 Schematic diagram of sub-aperture carrier distribution after spectrum division in the present invention;

[0099] Figure 4 Schematic diagram of energy distribution of original pulse pressure signal of the present invention;

[0100] Figure 5 Schematic diagram of energy distribution of a single sub-aperture pulse compression signal of the present invention;

[0101] Figure 6 A graph showing the process of estimating subspaces in an iterative manner using the bisection method according to the present invention;

[0102] Figure 7 This is the original pulse pressure signal spectrum diagram of the present invention;

[0103] Figure 8 This is a diagram showing the phenomenon of signal energy occurring across distance units in the present invention;

[0104] Figure 9 This is the sub-aperture signal spectrum diagram extracted by the present invention;

[0105] Figure 10 This is a sub-aperture pulse pressure signal diagram after range movement elimination according to the present invention;

[0106] Figure 11 Iterative curve diagram of modified grey wolf optimization of sub-aperture signal of the present invention;

[0107] Figure 12 A schematic diagram of the results of the ergodic search for high-order motion parameters of sub-aperture signals according to the present invention;

[0108] Figure 13 Schematic diagram of MTD accumulation results after eliminating distance movement and Doppler spread for the purpose of the present invention. DETAILED DESCRIPTION

[0109] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0110] Example 1

[0111] like Figure 1 As shown in the flowchart of , this embodiment provides a method for detecting and estimating maneuvering targets and parameters for a wideband radar, including the following steps:

[0112] Assume that a broadband radar transmits a set of linear frequency modulation signals:

[0113] s t (τ,t m )=rect(τ / T p )exp(jπγτ 2 )exp[j2πf c (t m +τ)]

[0114] Where rect(·) represents the gate function, γ=B / T p represents the modulation frequency, B is the signal bandwidth, T p represents the pulse duration, f c is the base frequency of the carrier frequency, τ represents the fast time, that is, the distance time, t m =mT r ,m=1,2,...,M represents slow time, T r represents the pulse repetition period, and M represents the number of accumulated pulses.

[0115] Assume that there is a target in the radar detection area. The target consists of P scattering points that are not in the same range unit. The baseband echo signal of a single scattering point received by the radar within a coherent processing interval is as follows:

[0116]

[0117] Where A0 is the complex amplitude value, c is the speed of light, R(t m ) is the instantaneous radial distance of the target relative to the radar, and its specific form is as follows:

[0118]

[0119] Where r0 is the initial radial distance of the target, v is the target radial velocity, a1 is the target radial first-order acceleration, and a2 is the target radial second-order acceleration.

[0120] The baseband echo signal is down-converted and pulse compressed to obtain the pulse compression signal as follows:

[0121]

[0122] Where A1 is the complex amplitude of the signal.

[0123] According to the theory of spectrum segmentation, we need to ensure that the pulse signal energy of each sub-aperture is concentrated within the same range unit. However, for wideband radar systems, the range unit is significantly smaller than that of narrowband radar. Therefore, for certain high-speed targets, in order to meet the above requirements, the sub-aperture time will be very short, which will result in a small number of range frequency sampling points, poor detection accuracy, and high computational complexity. To avoid this, we need to pre-compensate for a portion of the target speed. The specific implementation steps are as follows:

[0124] Step S1: Determine the target speed search range, divide the target speed search parameter space into equal intervals, and shift the speed component in the pulse pressure signal to the center of the parameter space by phase compensation;

[0125] Furthermore, the step S1 includes the following steps:

[0126] Step S11: Determine the search range of the target speed [v L ,v R ] and divide it into several parameter subspaces at equal intervals such as Figure 2 As shown in , the length of the subspace is determined by the following method: To improve computational efficiency, we generally divide the signal spectrum into N (N is generally 10 to 15) subapertures (see step S2 for detailed division process). To eliminate range wandering, the maximum range offset caused by target motion within a coherent processing interval should not exceed the range resolution of the subaperture signal, i.e. in, and is the maximum possible motion parameter value of the target in the prior information. Ignoring the high-order motion components, the subspace length can be determined From this, we can calculate the number of parameter subspaces as K = (v R -v L ) / Δv, the speed range covered by the i-th subspace is [v L +(i-1)Δv,v L +iΔv];

[0127] Step S12: For any subspace i (i∈[1,K]), construct a corresponding phase compensation function to translate the velocity contained in subspace i to the center v of the search parameter space center =[-Δv / 2,Δv / 2]:

[0128]

[0129] Where f is the fast time frequency, t m =mT r ,m=1,2,...,M is the slow time series, f c is the carrier frequency, c is the speed of light, v i is the velocity translation: And perform IFFT on the compensated signal to find the maximum amplitude of all range gates of the current pulse compression signal:

[0130]

[0131] Among them, IFFT f (·) is to do IFFT along the fast time frequency;

[0132] Step S13: Taking the above formula as the objective function with a single extreme value, in order to avoid the traversal search process, it is necessary to apply the bisection method to the K subspaces. Without the traversal search, the subspace i0 where the target true speed is located can be quickly determined, that is, the optimal subspace. The evaluation criteria are:

[0133]

[0134] Furthermore, step S13 includes the following steps:

[0135] Step S13-1: Let i min =1 and i max =K, calculate the index value of the bisection point: i mid =round((1+K) / 2), set the threshold value ε=1;

[0136] Step S13-2: Calculate the range gate amplitude of the pulse pressure signal after compensation of the subspaces around the bisection point and And calculate the difference between the two

[0137] Step S13-3: Determine whether ΔA is positive or negative. If ΔA>0, then otherwise

[0138] Step S13-4: If |i max -i min |≤ε, then the optimal subspace index is determined to be i0=i mid Otherwise, steps S13-1 to S13-3 are repeated until the above conditions are met.

[0139] Step S14: Construct a phase compensation function corresponding to the subspace index value i0 and compare it with the original pulse pressure signal S(f,t m ) to obtain the compensation result S T (f,t m ), so that the residual velocity component after compensation Falling in the subspace at the center of the parameter space, the schematic diagram of the process is as follows Figure 2 shown.

[0140] Step S2: Divide the spectrum of the frequency domain pulse compression signal into several narrow frequency bands and extract them sequentially;

[0141] Furthermore, the expression of the sub-aperture signal after the signal spectrum is divided and extracted in step S2 is:

[0142]

[0143] Among them, B s =B / N is the sub-aperture bandwidth, N is the number of divided sub-apertures, f c (n) = f c +(nN / 2-0.5)×ΔL×Δf, n=1,2,...,N is the carrier frequency of the nth subaperture, ΔL=L / N is the number of fast time sampling points contained in each subaperture, Δf=f s / L is the fast time sampling interval, L is the total number of sampling points in the fast time dimension of each pulse, and the fast time frequency range of the subaperture can be expressed as -B / 2+[1+(n-1)×ΔL]×Δf≤f≤-B / 2+n×ΔL×Δf, as shown in the schematic diagram. Figure 3 shown.

[0144] After spectrum division, the range resolution of each sub-aperture signal can be expressed as:

[0145]

[0146] It is not difficult to see that after the range resolution is expanded N times, the target energy originally dispersed in several range units is concentrated in the same widened range unit. Therefore, in a longer coherent processing interval, the range movement of the target will not occur. Figure 4 and 5 As shown, Figure 4 is a schematic diagram of the energy distribution of the original pulse pressure signal. Figure 5 Schematic diagram of the energy distribution of a single sub-aperture pulse compression signal.

[0147] Step S3: applying the MGWO algorithm to the extracted sub-aperture pulse pressure signals in sequence to obtain a rough estimate of the target maneuvering parameter of the corresponding sub-aperture;

[0148] Furthermore, step S3 includes the following steps:

[0149] Step S31: Initialize the population size N = 50, parameter optimization dimension D = 2, maximum number of iterations P = 25, convergence factor The coefficient vectors A=a(2r1-1) and C=2r2, r1 and r2 are random vectors, and the gray wolf population X(x1,x2,...,x N ) and the position X of each gray wolf i =(x i1 ,x i2 ,...,x iD ) T ,i=1,2,…,N;

[0150] Step S32: Construct the corresponding phase compensation function:

[0151]

[0152] Among them, c1 and c2 are the search values ​​of first-order acceleration and second-order acceleration;

[0153] S sub (f,t m ; n) after multiplying with the phase compensation function and performing IFFT transformation to the time domain, s is obtained sub (τ,t m; n), and take the maximum accumulated peak value as the objective function after doing FFT along the slow time:

[0154]

[0155] Calculate the objective function values ​​of all gray wolf positions after initialization, and mark the current three best individuals α, β and δ according to the sorting results of the objective function values, and calculate X α 、X β With X δ ;

[0156] Step S33: Calculate the distance D and position vector X between the three best individuals and the remaining ω wolves:

[0157] D α =|C1X α -X|, X1=X α -A1·D α

[0158] D β =|C2X β -X|, X2=X β -A2·D β

[0159] D δ =|C3X δ -X|, X3=X δ -A3·D δ

[0160] And guide the rest of the gray wolf individuals ω to move:

[0161]

[0162] in, is the weight coefficient;

[0163] Step S34: Update the positions of α, β, and δ and the objective function value according to the current wolf pack position:

[0164] If Γ>Γ α , then X α =X,Γ α =Γ; if Γ<Γ α And Γ>Γ β , then X β =X,Γ β =Γ; if Γ<Γ α ,Γ<Γ β With Γ>Γ δ , then X δ =X,Γ=Γ δ ;

[0165] Step S35: Repeat steps S31-S34. When the number of iterations reaches the maximum, the position of wolf α is output, which is the optimal solution X. i , corresponding to the estimated values ​​of the motion parameters of the target;

[0166] Step S36: Repeat the above operation for each extracted sub-aperture signal to obtain N sets of motion parameter estimation values.

[0167] Step S4: applying least squares estimation to the full aperture to obtain a precise estimation value after the estimation values ​​of each sub-aperture are fused;

[0168] Furthermore, step S4 includes the following steps:

[0169] Step S41: Construct a measurement matrix Ψ containing the instantaneous Doppler frequencies of all sub-apertures according to the N groups of estimated values ​​obtained in step S3:

[0170] Ψ=[F(1)F(2)…F(N)] T

[0171] Where F(n) is the Doppler frequency vector of the M pulses contained in the nth sub-aperture:

[0172] F(n)=[F(1;n)F(2;n)...F(M;n)]

[0173] Where F(m;n) is the instantaneous Doppler frequency component of the m-th pulse in the n-th subaperture:

[0174]

[0175] Step S42: Construct the theoretical Doppler frequency matrix Θ of the maneuvering target by taking the first-order derivative of the motion model:

[0176]

[0177] Where Q is the highest order of the motion model, and D(n,q) is the theoretical Doppler frequency vector of the M pulses contained in the nth sub-aperture:

[0178] D(n,q)=[D(1;n,q)D(2;n,q)...D(M;n,q)]

[0179] Where D(m;n,q) is the instantaneous Doppler frequency component of the m-th pulse in the n-th subaperture:

[0180]

[0181] Step S43: Assume that the motion parameter to be estimated is the undetermined coefficient, and vectorize it into:

[0182] σ=[σ1 σ2 … σ Q ] T

[0183] Therefore, the parameter estimation problem is transformed into a least squares problem:

[0184] Ψ=Θσ

[0185] Perform singular value decomposition on the instantaneous Doppler frequency measurement matrix and the theoretical Doppler frequency matrix:

[0186] [Ψ Θ]=USV T

[0187] Step S44: Calculate the Frobenius norm of the matrix Θ:

[0188]

[0189] Among them, s i are all non-zero singular values ​​in the matrix S;

[0190] Next, set a threshold η close to 1 (for example, η is 0.98), find the smallest integer r0 that satisfies δ(r) ≥ η, and construct the matrix U accordingly. r Used to fit the matrix U:

[0191] U r =[u r o]

[0192] Among them, u r Represents all elements from the 1st column to the r0th column on the left side of the matrix U, o is a zero-filled matrix to ensure that U r The number of rows and columns is the same as U; if r0>MN, then U r =U;

[0193] Step S45: Using the least squares method, obtain the precise estimate of the high-order motion component after the N groups of estimated values ​​are fused:

[0194]

[0195] Step S5: Construct a corresponding high-order phase compensation function based on the precise estimation value to complete Doppler frequency compensation, and perform a first-order Keystone transform on the compensated signal to complete range-azimuth decoupling;

[0196] Furthermore, step S5 includes the following steps:

[0197] Step S51: Obtain estimated values ​​of first-order and second-order accelerations and Finally, the following phase compensation function is constructed:

[0198]

[0199] Step S52: Combine the above formula with formula S T (f,t m ; i0) after multiplication to compensate for Doppler frequency spread:

[0200]

[0201] Among them, A3 is the complex amplitude value of the signal;

[0202] Step S53: comp (f,t m ) Perform a first-order Keystone transform to decouple the range frequency and azimuth:

[0203]

[0204] Step S6: Perform inverse fast Fourier transform (IFFT) in range and fast Fourier transform (FFT) in azimuth on the signal to complete the coherent accumulation of target energy, and confirm the presence of the target through constant false alarm rate (CFAR) detection.

[0205] Furthermore, step S6 includes the following steps:

[0206] Step S61: Perform IFFT on the above equation along the range frequency to obtain a two-dimensional time domain signal:

[0207]

[0208] Next, perform FFT on the slow time to obtain the coherent accumulation result:

[0209]

[0210] Step S62: The amplitude of the constructed echo signal range-Doppler domain detection unit map is used as a detection statistic and compared with a given false alarm probability adaptive detection threshold:

[0211]

[0212] Where ρ is the adaptive detection threshold, H1 corresponds to the presence of the target, and H0 corresponds to the absence of the target;

[0213] If the energy exceeds a given threshold, the target is determined to exist; based on the location of the energy peak, the estimated value of the target's initial radial distance and residual speed can be obtained.

[0214] The present invention provides a method for detecting and estimating maneuvering targets in a wideband radar system, demonstrating strong robustness in a variety of complex scenarios. Implemented through software, it enables real-time calculation and correction during signal processing. The method is simple, requires minimal hardware, and offers high reliability.

[0215] The present invention is further described below by means of simulation experiment examples:

[0216] Assume that the broadband radar system parameters and target motion parameters are shown in Table 1. The simulation results are shown in Figure 6-13 shown.

[0217] Table 1 Example simulation system parameters and target parameters

[0218] System parameters Value (unit) Target parameters Value (unit) Center frequency 10GHz Initial radial distance 50km Signal bandwidth 100MHz Radial velocity 200m / s Pulse sampling frequency 150MHz First-order radial acceleration <![CDATA[20m / s 2 ]]> Pulse repetition frequency 800Hz Second-order radial acceleration <![CDATA[10m / s 2 ]]> Coherent pulse number 200 Post-pulse compression signal-to-noise ratio 0dB

[0219] Figure 6 The process curve of estimating the subspace in an iterative manner using the bisection method. The original pulse pressure signal spectrum is as follows Figure 7 shown. Figure 8 Demonstrates the phenomenon of signal energy occurring across distance units. Figure 9 is the extracted sub-aperture signal spectrum. Figure 10 The sub-aperture pulse pressure signal after range walk elimination is shown. Figure 11 Iterative curve of modified grey wolf optimization for sub-aperture signals. It is not difficult to see that this method has fast convergence and significantly improved computational efficiency. Figure 12 The results of the ergodic search for the high-order motion parameters of the subaperture signal are shown in Figure 2. The signal amplitude shows the accuracy of the modified grey wolf optimization algorithm. Figure 13 The MTD accumulation results after eliminating range movement and Doppler spread for the target can prove the effectiveness of this method.

[0220] Example 2

[0221] The present invention also discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor can implement the steps of the above-mentioned wideband radar maneuvering target detection and parameter estimation method when executing the computer program.

[0222] Example 3

[0223] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned wideband radar maneuvering target detection and parameter estimation method can be implemented.

[0224] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for detecting and estimating maneuvering targets and parameters for wideband radar, characterized in that: The following steps are involved: Step S1: Determine the target speed search range, divide the target speed search parameter space into equal intervals, and shift the speed component in the pulse pressure signal to the center of the parameter space by phase compensation; The step S1 comprises the following steps: Step S11: Determine the search range of the target speed [v L ,v R ] and the subspace length Δv, and the number of subspaces is calculated as K = (v R -v L ) / Δv; Step S12: For any subspace i (i∈[1,K]), construct a corresponding phase compensation function to translate the velocity contained in subspace i to the center v of the search parameter space center =[-Δv / 2,Δv / 2]: Where f is the fast time frequency, t m =mT r ,m=1,2,...,M is the slow time series, f c is the carrier frequency, c is the speed of light, v i is the velocity translation: And perform IFFT on the compensated signal to find the maximum amplitude of all range gates of the current pulse compression signal: Among them, IFFT f (·) is to do IFFT along the fast time frequency; Step S13: Using the above formula as the objective function with a single extreme value, the bisection method is applied to the K subspaces. Without traversal search, the subspace i0 where the target true speed is located can be quickly determined, that is, the optimal subspace. The evaluation criteria are: The step S13 includes the following steps: Step S13-1: Let i min =1 and i max =K, calculate the index value of the bisection point: i mid =round((1+K) / 2), set the threshold value ε=1; Step S13-2: Calculate the range gate amplitude of the pulse pressure signal after compensation of the subspaces around the bisection point and And calculate the difference between the two Step S13-3: Determine whether ΔA is positive or negative. If ΔA>0, then otherwise Step S13-4: If |i max -i min |≤ε, then the optimal subspace index is determined to be i0=i mid Otherwise, repeat steps S13-1 to S13-3 until the above conditions are met; Step S14: Construct a phase compensation function corresponding to the subspace index value i0 and multiply it with the original pulse pressure signal to obtain the compensation result S T (f,t m ), so that the residual velocity component after compensation Falling in the subspace at the center of the parameter space; Step S2: Divide the spectrum of the frequency domain pulse compression signal into several narrow frequency bands and extract them sequentially; Step S3: applying the MGWO algorithm to the extracted sub-aperture pulse pressure signals in sequence to obtain a rough estimate of the target maneuvering parameter of the corresponding sub-aperture; Step S4: applying least squares estimation to the full aperture to obtain a precise estimation value after the estimation values ​​of each sub-aperture are fused; Step S5: Construct a corresponding high-order phase compensation function based on the precise estimation value to complete Doppler frequency compensation, and perform a first-order Keystone transform on the compensated signal to complete range-azimuth decoupling; Step S6: Perform a range-wise fast inverse Fourier transform and an azimuth-wise fast Fourier transform on the signal to complete the coherent accumulation of target energy, and confirm the presence of the target through constant false alarm rate detection.

2. The method for detecting and estimating maneuvering targets and parameters for a wideband radar according to claim 1, wherein: The expression of the sub-aperture signal after the signal spectrum division extracted in step S2 is: Among them, B s =B / N is the sub-aperture bandwidth, N is the number of divided sub-apertures, f c (n) = f c +(nN / 2-0.5)×ΔL×Δf, n=1,2,...,N is the carrier frequency of the nth subaperture, ΔL=L / N is the number of fast time sampling points contained in each subaperture, Δf=f s / L is the fast time sampling interval, L is the total number of sampling points in the fast time dimension of each pulse, and the fast time frequency range of the subaperture is expressed as -B / 2+[1+(n-1)×ΔL]×Δf≤f≤-B / 2+n×ΔL×Δf After spectrum division, the range resolution of each sub-aperture signal is expressed as: After the range resolution is expanded N times, the target energy originally dispersed in several range units can be concentrated in the same widened range unit. Therefore, the range movement of the target will not occur within a longer coherent processing interval.

3. The method for detecting and estimating maneuvering targets and parameters for wideband radar according to claim 2, wherein: The step S3 comprises the following steps: Step S31: Initialize the population size L, parameter optimization dimension D, maximum number of iterations P, convergence factor a, random vectors A and C, gray wolf population X (x1, x2, ..., x L ) and the position X of each gray wolf i =(x i1 ,x i2 ,…,x iD ) T ,i=1,2,…,L; Step S32: Construct the corresponding phase compensation function: Among them, c1 and c2 are the search values ​​of first-order acceleration and second-order acceleration; S sub (f,t m ; n) after multiplying with the phase compensation function and performing IFFT transformation to the time domain, s is obtained sub (τ,t m ; n), and take the maximum accumulated peak value as the objective function after doing FFT along the slow time: Calculate the objective function values ​​of all gray wolf positions after initialization, and mark the current three best individuals α, β and δ according to the sorting results of the objective function values; Step S33: Calculate the distances between the remaining gray wolf individuals and the three best individuals, and guide the remaining gray wolf individuals to move; Step S34: Update the positions of α, β, δ and the objective function value according to the current position of the wolf pack; Step S35: When the number of iterations reaches the maximum value, output the optimal solution X i , which is the estimated value of motion parameters; Step S36: Repeat the operations of steps S31 to S35 for each extracted sub-aperture signal to obtain N sets of motion parameter estimation values.

4. The method for detecting and estimating maneuvering targets and parameters for wideband radar according to claim 3, wherein: In step S31, in order to improve the global convergence speed of the optimization algorithm and balance the local search ability of the optimization algorithm, the convergence factor a is changed from linear decreasing to nonlinear exponential decreasing, and its expression is:

5. The method for detecting and estimating maneuvering targets and parameters for wideband radar according to claim 4, wherein: In step S33, in order to distinguish the leadership of the top three levels of gray wolves in the entire wolf pack, the inertia weight is introduced into the wolf pack position update to speed up the algorithm convergence speed. The new position update formula is: in, is the weight coefficient.

6. The method for detecting and estimating maneuvering targets and parameters for wideband radar according to claim 5, wherein: The step S4 comprises the following steps: Step S41: Construct a measurement matrix Ψ containing the instantaneous Doppler frequencies of all sub-apertures according to N groups of estimated values: Ψ=[F(1)F(2)…F(N)] T Where F(n) is the Doppler frequency vector of the M pulses contained in the nth sub-aperture: F(n)=[F(1;n)F(2;n)...F(M;n)] Where F(m;n) is the instantaneous Doppler frequency component of the m-th pulse in the n-th subaperture: Step S42: Construct the theoretical Doppler frequency matrix Θ of the maneuvering target by taking the first-order derivative of the motion model: Where Q is the highest order of the motion model, and D(n,q) is the theoretical Doppler frequency vector of the M pulses contained in the nth sub-aperture: D(n,q)=[D(1;n,q)D(2;n,q)...D(M;n,q)] Where D(m;n,q) is the instantaneous Doppler frequency component of the m-th pulse in the n-th subaperture: Step S43: Perform singular value decomposition on the instantaneous Doppler frequency measurement matrix and the theoretical Doppler frequency matrix: [ΨΘ]=USV T Step S44: Calculate the Frobenius norm of the matrix Θ: Among them, s i are all non-zero singular values ​​in the matrix S; Next, set a threshold η close to 1, find the smallest integer r0 that satisfies δ(r) ≥ η, and construct the matrix U accordingly. r Used to fit the matrix U: IN r =[in r on] Among them, u r Represents all elements from the 1st column to the r0th column on the left side of the matrix U, o is a zero-filled matrix to ensure that U r The number of rows and columns is the same as U; if r0>MN, then U r =U; Step S45: Using the least squares method, obtain the precise estimate of the high-order motion component after the N groups of estimated values ​​are fused:

7. The method for detecting and estimating maneuvering targets and parameters for wideband radar according to claim 6, wherein: The step S5 comprises the following steps: Step S51: Obtain estimated values ​​of first-order and second-order accelerations and Finally, the following phase compensation function is constructed: Step S52: Combine the above formula with formula S T (f,t m ; i0) after multiplication, compensate for Doppler frequency spread: Among them, A3 is the complex amplitude value of the signal; Step S53: comp (f,t m ) performs a first-order Keystone transform to decouple the range frequency and azimuth:

8. The method for detecting and estimating maneuvering targets and parameters for wideband radar according to claim 7, wherein: The step S6 comprises the following steps: Step S61: Perform IFFT along the range frequency on the formula in step 53 to obtain a two-dimensional time domain signal: Next, perform FFT on the slow time to obtain the coherent accumulation result: Step S62: The amplitude of the constructed echo signal range-Doppler domain detection unit map is used as a detection statistic and compared with a given false alarm probability adaptive detection threshold: Where ρ is the adaptive detection threshold, H1 corresponds to the presence of the target, and H0 corresponds to the absence of the target; If the energy exceeds a given threshold, the target is determined to exist; based on the location of the energy peak, the estimated value of the target's initial radial distance and residual speed is obtained.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method for detecting and estimating maneuvering targets and parameters for a wideband radar according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting and estimating maneuvering targets and parameters for a wideband radar according to any one of claims 1 to 8 is implemented.

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