A joint coherent integration and detection method for radar high-speed range expansion targets

Abstract

The application discloses a kind of radar high-speed range expansion target's joint phase accumulation and detection method, to fully accumulate and utilize the radar echo energy of the purpose of the detected target, first, phase accumulation algorithm is combined with target detection model, constructs new detection statistics, then based on sparse representation theory, the optimization problem of the adaptive estimation of distance expansion target scattering point of different sizes is established and solved, finally the scattering point information obtained is applied to the detection model established, while realizing distance walk correction, radar echo energy multi-pulse phase accumulation, and the non-phase accumulation of the echo energy between each scattering point.The method of the application fully accumulates the energy between radar echo pulses and within pulse, effectively improves the detection probability and motion parameter estimation accuracy of high-speed range expansion target with unknown size, is suitable for air monitoring, traffic monitoring and other fields, and has wide application prospect.

Description

Technical Field

[0001] This invention belongs to the field of radar target detection technology, specifically relating to a joint coherent accumulation and detection method for high-speed, long-range extended targets in radar. Background Technology

[0002] To meet the urgent need for precise target and environmental perception, modern radar has evolved towards high resolution and wide bandwidth. Due to its characteristics of smaller signal fluctuations, richer detailed information, and stronger anti-jamming capabilities, it is widely used in target detection, classification, imaging, and tracking. In short, high-resolution wideband radar has broad development prospects in the civilian field.

[0003] However, with further increases in radar bandwidth, the detected target will be broken down into multiple isolated scattering points along the radar's line of sight. At this point, the detected target is considered a range-extended target, with its energy dispersed to each scattering point. Since the energy at each scattering point is only a fraction of the target's total energy, the signal-to-noise ratio within the range cell is significantly lower compared to narrowband radar. On the other hand, with the rapid development of science and technology, the emergence of high-speed targets poses new challenges to reliable radar detection. Due to their high-speed movement, the target's echo envelope spans multiple range cells during the observation period. This phenomenon, known as range walk, causes echo energy to leak into adjacent range cells, further reducing the target's signal-to-noise ratio. This range walk phenomenon is particularly pronounced in broadband radar detection due to its smaller range cell size. Therefore, to ensure better detection performance for high-speed range-extended targets, both target range extension and target range walk must be considered simultaneously. The fundamental solution is to fully accumulate and utilize the echo energy dispersed and leaked to each scattering point and adjacent range cells due to target range extension and range walk. The paper "Multi-Dimensional SpreadTarget Detection with Across Range-Doppler Unit Phenomenon Based on Generalized Radon-Fourier Transform. Remote Sens. 2023, 15, 2158" first utilizes the Generalized Radon-Fourier Transform (GRFT) coherent accumulation algorithm to achieve range travel correction and coherent accumulation of target echoes. Then, it employs a double-threshold generalized likelihood ratio test (DT-GLRT) method to estimate and detect the scattering points of the target. However, due to an inappropriate setting of the first threshold, its detection performance will be significantly reduced, and this method is only applicable to target models with uniformly distributed scattering point energy. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a joint coherent accumulation and detection method for high-speed radar range-extended targets. This method solves the problem that existing methods cannot achieve robust detection in practical applications due to target range movement and range extension, which leads to echo energy leakage and dispersion, resulting in decreased detection probability and parameter estimation accuracy.

[0005] The technical solution adopted in this invention is: a joint coherent accumulation and detection method for high-speed radar range-extended targets, the specific steps of which are as follows:

[0006] S1. Initialize radar system parameters, read echo measurements from radar receiver and perform pulse compression processing;

[0007] S2. Set the motion parameter search space: Based on the target's velocity range and region of interest, set the distance parameter search space. r and velocity parameter search space Γ v ;

[0008] S3. Based on step S2, a phase compensation function is constructed using the motion parameters to be searched to perform phase compensation on the pulse-compressed echo measurement, and then the covariance matrix of noise / clutter is estimated using auxiliary data.

[0009] S4. Construct coherent accumulation: Substitute the covariance matrix of the echo measurement and estimation after phase compensation in step S3 into the detection model of the range extension target to construct the joint detection statistics of the extension target.

[0010] S5. Based on step S4, construct a normalized energy accumulation vector, and then, based on the detection model of the binary assumption, derive the sparse signal representation model of the normalized energy accumulation vector, and establish an optimization problem for adaptively estimating the distribution of the target scattering points over the extended range.

[0011] S6. Based on the greedy search method, solve the optimization problem of adaptively estimating the distribution of scattering points in step S5, obtain the information on the number and location distribution of scattering points, and feed it back into step S4 for the calculation of the joint detection statistic. Record the calculated value and project it onto the detection statistic plane. Then, traverse the elements in the distance parameter search space and the velocity parameter search space and remove them. If Γ r and Γ v If empty, proceed to step S7; otherwise, return to step S3.

[0012] S7. Perform threshold decision. Based on steps S1-S6, save the detection statistics for each distance and velocity search to obtain three-dimensional data on the detection statistics and motion parameters. Then, obtain the constant false alarm rate (CFAR) P value through a Monte Carlo experiment. fa Detection threshold T under certain conditions k The results are compared with the detection statistics. If the detection statistics are greater than the detection threshold, the target is determined to exist; otherwise, the target is not found. The positions where the detection statistics are greater than the detection threshold are then extracted as the estimated values ​​of the motion parameters.

[0013] Furthermore, step S1 is specifically as follows:

[0014] S11. Radar system parameter initialization;

[0015] First, initialize the radar system parameters: radar transmit signal carrier frequency f. cBandwidth B, pulse repetition interval T r Sampling frequency f s The radar range resolution Δr, radar Doppler resolution Δv, the number of range dimension resolution cells N corresponding to the maximum radar detection range, and the number of radar pulses M are given. Then, the target parameters are initialized: the target distance is set to the initial radar distance r. p Initial radial velocity v p .

[0016] S12. Acquire radar echo measurements;

[0017] The raw radar echo measurements are read from the radar receiver, i.e., the simulated radar echo measurements and the measured radar echo measurements are acquired. Then, the raw radar echo measurements are preprocessed to obtain the pulse-compressed echo measurements Z = [z1, z2, ..., z...]. N ].

[0018] in, Let M be the complex vector representing the echo within the nth cell, (·). T This indicates a transpose operation. The preprocessing described is pulse compression.

[0019] Furthermore, step S2 is specifically as follows:

[0020] Set the parameter space Γ for the distance and velocity to be searched. r and Γ v The expression is as follows:

[0021]

[0022]

[0023] Where Δr and Δv represent the search interval, i.e., the distance resolution and the velocity resolution; Let $\lambda$ represent the distance parameter search at the $p$-th iteration and the velocity parameter search at the $q$-th iteration, respectively. $P$ represents the distance parameter search space $Γ$. r The length of Γ, where Q represents the velocity parameter search space. v The length.

[0024] Furthermore, step S3 is specifically as follows:

[0025] S31, Phase compensation;

[0026] Select the motion parameters to be searched in step S2. Constructing a phase compensation function Phase compensation is performed on the pulse-compressed echo measurement, as expressed below:

[0027]

[0028] Where λ represents the wavelength of the electromagnetic wave, Δ p The interval represents the pulse repetition interval, and m = 1, 2, ..., M represents the pulse tag.

[0029] The phase compensation function of equation (3) is used to compensate for the echo measurement after pulse compression, and the expression is as follows:

[0030]

[0031] in, f s This represents the sampling frequency, c = 3 × 10⁻⁶. 8 This represents the speed of electromagnetic wave propagation. From equation (4), we can see that... Indicates following the search path In z(m,Δ′) n Phase compensation is performed based on this.

[0032] S32, Noise / Clutter Covariance Matrix Estimation;

[0033] Estimating the covariance matrix of noise / clutter using auxiliary data when the target is absent The expression is as follows:

[0034]

[0035] in,(·) H denoted as conjugate transpose, and R represents the length of the target auxiliary data.

[0036] Furthermore, step S4 is specifically as follows:

[0037] The echo measurement with phase compensation in equation (4) and the covariance matrix of noise / clutter estimated in equation (5) are combined. Substitute it into the detection model of the distance-extended target and construct the joint detection statistic Λ p,q The expression is as follows:

[0038]

[0039] Where p represents the steering vector. This represents the possible range cell tag set for the target scattering point in echo measurement.

[0040] definition Then we can obtain:

[0041]

[0042] in, This indicates that the echo measurement after phase compensation is accumulated between multiple pulses, which is equivalent to performing one GRFT coherent accumulation.

[0043] Introduce an intermediate variable The expression is as follows:

[0044]

[0045] in, This represents the normalized value of the energy after coherent accumulation of M coherent pulses within the nth distance cell.

[0046] The simplified expression for the detection statistic is as follows:

[0047]

[0048] Furthermore, step S5 is specifically as follows:

[0049] Within the detection window, based on the binary hypothesis detection model, we can obtain:

[0050]

[0051] in, Represents the target signal vector. This represents the noise vector. This represents the set of distance cell labels within the detection window, with a length of D. and The range cell labels represent the target signal and noise sets, respectively. Indicates the target scattering point at The possible distance unit label set. Then yes A subset of, i.e. for The supplement to .

[0052] When the target exists, under the H1 assumption, the normalized energy expression after coherent accumulation is derived from equation (10) as follows:

[0053]

[0054] in, and They are represented as follows:

[0055]

[0056]

[0057] Define a D-dimensional normalized energy accumulation vector as well as Equation (11) can be expressed in vector form as follows:

[0058]

[0059] Equation (14) is equivalent to a sparse recovery signal model; I D It can serve as the perceptual matrix or the dictionary matrix in a sparse recovery model. Let represent a complex Gaussian noise vector with mean υ and covariance δ. 2 . Represents the observation vector. Represents the field of complex numbers. Let represent the sparse vector corresponding to each perceptual vector coefficient, and satisfy . ||·|| p Let L denote the p-norm, and let L denote the number of non-zero elements in the vector.

[0060] but The recovery is equivalent to the recovery from the observed vector Estimating sparse vectors Establish an optimization problem for adaptively estimating the distribution of scattering points from a range-extended target:

[0061]

[0062] Where ∈ represents the acceptable error, I D This represents a D-dimensional unit vector.

[0063] By introducing the Lagrange multiplier method, the optimization problem of equation (15) is redefined as follows:

[0064]

[0065] in, Let represent the loss function, and η represent the Lagrange multiplier. Its empirical value can be set as:

[0066]

[0067] Finally, the optimization problem of equation (16) is solved using a greedy search method. The recovered vector... Estimation of scattering points, carrying information about their distribution. It can be represented as a vector non-zero elements Right now:

[0068]

[0069] Furthermore, step S6 is specifically as follows:

[0070] The estimated scattering point information in step S5 The feedback is input into the detection statistic expression in equation (9) of step S4 to calculate the joint detection statistic, as shown in the following expression:

[0071]

[0072] Iterate through all combinations of the distance and velocity parameters to be searched. If p≠P and q≠Q, return to step S3. At the same time, calculate the detection statistic Λ corresponding to each parameter search. p,q Projected onto the (r,v) plane Λ GRFT (p,q), that is:

[0073] Λ GRFT (p,q)=Λ p,q (20)

[0074] Furthermore, step S7 is specifically as follows:

[0075] Through N c The Monte Carlo simulation experiment, under constant false alarm rate P fa In this case, the detection threshold T is obtained. k The detection statistic in equation (20) is compared with the detection threshold to complete the detection decision, as shown in the following expression:

[0076]

[0077] When the detection statistic is greater than the detection threshold, the target is considered to exist; otherwise, the target is not found. The positions where the detection statistic is greater than the detection threshold are the estimated values ​​of the motion parameters. By obtaining the peak position in equation (20), the estimated value of the distance parameter can be obtained. and velocity parameter estimates

[0078]

[0079]

[0080]

[0081] Finally, an evaluation index system was designed, namely the detection probability P. d Root mean error of distance estimation (RMSE(r)) p ), the root mean square error of velocity estimation (RMSE(v)) q ), and the estimation error of the number of scattering points. sc The expression is as follows:

[0082]

[0083]

[0084]

[0085]

[0086] Where, N c Indicates the number of Monte Carlos. Indicates the number of successful tests; L e (·) indicates the length operation, L T This indicates the number of true scattering points of the target in the range extension.

[0087] The beneficial effects of this invention are as follows: The method of this invention aims to fully accumulate and utilize the radar echo energy of the detected target. First, it combines a coherent accumulation algorithm with a target detection model to construct new detection statistics. Then, based on sparse representation theory, it establishes and solves an optimization problem for adaptive estimation of scattering points of range-extended targets of varying sizes. Finally, it applies the obtained scattering point information to the established detection model, simultaneously achieving range travel correction, multi-pulse coherent accumulation of radar echo energy, and non-coherent accumulation of echo energy between each scattering point. This method of the present invention fully accumulates the energy between and within radar echo pulses, effectively improving the detection probability and motion parameter estimation accuracy of high-speed range-extended targets of unknown size. It is applicable to multiple fields such as air surveillance and traffic monitoring, and has broad application prospects.

[0088] The method of this invention constructs an integrated architecture for range travel correction, adaptive scattering point estimation, and target detection of high-speed range-extended targets of arbitrary size radar under uniform Gaussian noise background. It realizes the full accumulation and maximum utilization of radar echo energy within and between pulses, and solves the problems of existing methods, such as the dispersion of echo energy due to range travel and extension, which leads to a decrease in detection probability and parameter estimation accuracy. Attached Figure Description

[0089] Figure 1 This is a flowchart of a joint coherent accumulation and detection method for high-speed radar range-extended targets according to the present invention.

[0090] Figure 2 This is a schematic diagram of the distance profiles for five target models in an embodiment of the present invention.

[0091] Figure 3 This is a detection probability curve diagram for five target models in an embodiment of the present invention.

[0092] Figure 4 This is a graph showing the relationship between the signal-to-noise ratio and the estimation of distance and velocity parameters for five target models in an embodiment of the present invention.

[0093] Figure 5 This is a graph showing the relationship between the signal-to-noise ratio and scattering point estimation for five target models in an embodiment of the present invention.

[0094] Figure 6 This is a graph showing the comparison of the detection probabilities of the method of the present invention and four traditional methods for the target model 1 in an embodiment of the present invention.

[0095] Figure 7 This is a comparison curve of the distance and velocity estimation accuracy of the method of the present invention and four traditional methods under a specific signal-to-noise ratio condition for five target models in an embodiment of the present invention.

[0096] Figure 8 This is a three-dimensional effect diagram of the accumulation results of the method of the present invention and the conventional method 2 on target models 1, 3, and 5 in an embodiment of the present invention. Detailed Implementation

[0097] The method of this invention is mainly verified through simulation experiments, and all steps and conclusions have been verified to be correct using Matlab 2022. The method of this invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0098] like Figure 1 The flowchart shown is a method for joint coherent accumulation and detection of high-speed radar range-extended targets according to the present invention. The specific steps are as follows:

[0099] S1. Initialize radar system parameters, read echo measurements from radar receiver and perform pulse compression processing;

[0100] S2. Set the motion parameter search space (search parameter set): Based on the target's velocity range and region of interest, set the distance parameter search space Γ. r and velocity parameter search space Γ v ;

[0101] S3. Based on step S2, a phase compensation function is constructed using the motion parameters to be searched to perform phase compensation on the pulse-compressed echo measurement, and then the covariance matrix of noise / clutter is estimated using auxiliary data.

[0102] S4. Construct coherent accumulation: Substitute the covariance matrix of the echo measurement and estimation after phase compensation in step S3 into the detection model of the range extension target to construct the joint detection statistics of the extension target.

[0103] S5. Based on step S4, construct a normalized energy accumulation vector, and then, based on the detection model of the binary assumption, derive the sparse signal representation model of the normalized energy accumulation vector, and establish an optimization problem for adaptively estimating the distribution of the target scattering points over the extended range.

[0104] S6. Based on the greedy search method, solve the optimization problem of adaptively estimating the distribution of scattering points in step S5, obtain the information on the number and location distribution of scattering points, and feed it back into step S4 for the calculation of the joint detection statistic. Record the calculated value and project it onto the detection statistic plane. Then, traverse the elements in the distance parameter search space and the velocity parameter search space and remove them. If Γr and Γ v If empty, proceed to step S7; otherwise, return to step S3.

[0105] S7. Perform threshold decision. Based on steps S1-S6, save the detection statistics for each distance and velocity search to obtain three-dimensional data on the detection statistics and motion parameters (distance, velocity). Then, obtain the constant false alarm rate (CFAR) P value through a Monte Carlo experiment. fa Detection threshold T under certain conditions k The results are compared with the detection statistics. If the detection statistics are greater than the detection threshold, the target is determined to exist; otherwise, the target is not found. The positions where the detection statistics are greater than the detection threshold are then extracted as the estimated values ​​of the motion parameters.

[0106] In this embodiment, step S1 is specifically as follows:

[0107] S11. Radar system parameter initialization;

[0108] In this embodiment, in order to verify the beneficial effect of the method of the present invention on the detection of range-extended targets of different sizes, the detection of high-speed range-extended targets with different numbers of scattering points is directly simulated.

[0109] First, initialize the radar system parameters: radar transmit signal carrier frequency f. c =15GHz, bandwidth B=1GHz, pulse repetition interval T r =100us, sampling frequency f s =20MHz, radar range resolution Δr, radar Doppler resolution Δv, radar transmit pulse count M = 500, number of range-dimension resolution cells corresponding to the maximum radar detection range N = 1000. Then initialize the target parameters: set the target distance to the radar initial distance as r. p =100m, initial radial velocity v p =30m / s;

[0110] Five range-extended target scattering models are shown in Table 1. The target has five scattering points. Each element in each row of Table 1 represents the ratio of the echo energy at that scattering point to the total target energy. Model 1 indicates that the target energy is uniformly distributed within each scattering point, with no sparsity differences between scattering points. Models 2 through 5 show a gradually increasing sparsity between scattering points. Models 2, 3, and 4 have the same number of scattering points but different target energy distributions, reflecting varying sparsity. Model 5 represents a point target.

[0111] Table 1

[0112]

[0113] S12. Acquire radar echo measurements;

[0114] The raw radar echo measurements are read from the radar receiver, i.e., the simulated radar echo measurements and the measured radar echo measurements are acquired. Then, the raw radar echo measurements are preprocessed to obtain the pulse-compressed echo measurements Z = [z1, z2, ..., z...]. N ].

[0115] in, Let M be the complex vector representing the echo within the nth cell, (·). T This indicates a transpose operation. The preprocessing described is pulse compression.

[0116] In this embodiment, the distance images of the five target models after pulse compression are as follows: Figure 2 As shown, and denoted as Z k ={z(m,n),1≤m≤M,1≤n≤N}.

[0117] Figure 2 (a)~ Figure 2 (e) represents the distance images of the five target models.

[0118] Where m represents the Doppler resolution cell number, n represents the range resolution cell number, and z(m,n) represents the amplitude values ​​of the Doppler cell number m, the range cell number n, and the corresponding measurement data, as shown in the following expressions:

[0119]

[0120] Here, w(m,n) represents complex Gaussian noise independent of the target signal and is limited by bandwidth B. Furthermore, The signal amplitude at the l-th scattering point is represented by Δ, where L represents the number of scattering points of the range-extended target. f R represents the sampling interval. l (mΔ p ) represents the radial distance between the l-th scattering point and the radar platform, i.e.:

[0121] r l (mT r ) = r 0,l +v l mT r (30)

[0122] Where, r 0,l v represents the initial radial distance of the l-th scattering point. l This represents the radial velocity of the l-th scattering point.

[0123] In this embodiment, step S2 is specifically as follows:

[0124] Set the parameter space Γ for the distance and velocity to be searched. r and Γ v The expression is as follows:

[0125]

[0126]

[0127] Where Δr and Δv represent the search interval, i.e., range resolution (radar range resolution) and velocity resolution (radar Doppler resolution); Let $\lambda$ represent the distance parameter search at the $p$-th iteration and the velocity parameter search at the $q$-th iteration, respectively. $P$ represents the distance parameter search space $Γ$. r The length of Γ, where Q represents the velocity parameter search space. v The length.

[0128] In this embodiment, step S3 is specifically as follows:

[0129] S31, Phase compensation;

[0130] Select the motion parameters to be searched in step S2. Constructing a phase compensation function Phase compensation is performed on the pulse-compressed echo measurement, as expressed below:

[0131]

[0132] Where λ represents the wavelength of the electromagnetic wave, Δ p The interval represents the pulse repetition interval, and m = 1, 2, ..., M represents the pulse tag.

[0133] The phase compensation function of equation (33) is used to compensate for the pulse compression echo measurement, and the expression is as follows:

[0134]

[0135] in, f s This represents the sampling frequency, c = 3 × 10⁻⁶. 8 This represents the speed of electromagnetic wave propagation. From equation (34), it can be seen that... Indicates following the search path In z(m,Δ′) n Phase compensation is performed based on this.

[0136] S32, Noise / Clutter Covariance Matrix Estimation;

[0137] Estimating the covariance matrix of noise / clutter using auxiliary data when the target is absent The expression is as follows:

[0138]

[0139] in,(·) H denoted as conjugate transpose, and R represents the length of the target auxiliary data.

[0140] In this embodiment, step S4 is specifically as follows:

[0141] The echo measurement for phase compensation in equation (34) and the covariance matrix of noise / clutter estimated in equation (35) are combined. Substitute it into the detection model of the distance-extended target and construct the joint detection statistic Λ p,q The expression is as follows:

[0142]

[0143] Where p represents the steering vector. This represents the possible range cell tag set for the target scattering point in echo measurement.

[0144] definition Then we can obtain:

[0145]

[0146] in, This indicates that the echo measurement after phase compensation is accumulated between multiple pulses, which is equivalent to performing one GRFT coherent accumulation.

[0147] Introduce an intermediate variable The expression is as follows:

[0148]

[0149] in, This represents the normalized value of the energy after coherent accumulation of M coherent pulses within the nth distance cell.

[0150] The simplified expression for the detection statistic is as follows:

[0151]

[0152] This detection statistic can simultaneously achieve target range movement correction, coherent accumulation of radar echo pulse energy, and non-coherent accumulation of energy at each scattering point. However, since the target size is unknown, the scattering point distribution requires adaptive estimation in subsequent steps.

[0153] In this embodiment, step S5 is specifically as follows:

[0154] Within the detection window, based on the binary hypothesis detection model, we can obtain:

[0155]

[0156] in, Represents the target signal vector. This represents the noise vector. This represents the set of distance cell labels within the detection window, with a length of D. and The range cell labels represent the target signal and noise sets, respectively. Indicates the target scattering point at The possible distance unit label set. Therefore, yes A subset of, i.e. for The supplement to .

[0157] When the target exists, under the H1 assumption, the normalized energy expression after coherent accumulation is derived from equation (40) as follows:

[0158]

[0159] in, and They are represented as follows:

[0160]

[0161]

[0162] For ease of analysis, we define the energy accumulation of a D-dimensional vector normalized. as well as Equation (41) can be expressed in vector form as follows:

[0163]

[0164] Equation (44) can be equivalent to a sparsely recovered signal model (a sparse signal representation model of the normalized energy accumulation vector). Where, I D It serves as a perceptual or dictionary matrix in sparse recovery models. Let represent a complex Gaussian noise vector with a mean of υ (0 in this example) and a covariance of δ. 2 . Represents the observation vector. Represents the field of complex numbers. Let represent the sparse vector corresponding to each perceptual vector coefficient, and satisfy . Among them ||·|| p Let L denote the p-norm, and L denote the number of non-zero elements in the vector (equivalent to the number of scattering points of the range-extended target).

[0165] but The recovery is equivalent to the recovery from the observed vector Estimating sparse vectors Establish an optimization problem for adaptively estimating the distribution of scattering points from a range-extended target:

[0166]

[0167] Where ∈ represents the acceptable error, I D This represents a D-dimensional unit vector.

[0168] By introducing the Lagrange multiplier method, the optimization problem of equation (45) is redefined as follows:

[0169]

[0170] in, Let represent the loss function, and η represent the Lagrange multiplier. Its empirical value can be set as:

[0171]

[0172] Finally, this embodiment uses a greedy search method to solve the optimization problem of equation (46). The recovered vector... It carries information about the distribution of scattering points. Therefore, the estimation of scattering points... Represented as a vector non-zero elements Right now:

[0173]

[0174] In this embodiment, step S6 is specifically as follows:

[0175] The estimated scattering point information in step S5 The feedback is input into the detection statistic expression of equation (39) in step S4 to calculate the joint detection statistic, as shown in the following expression:

[0176]

[0177] Iterate through all combinations of the distance and velocity parameters to be searched; if p≠P and q≠Q, return to step S3. Simultaneously, calculate the detection statistic Λ corresponding to each parameter search. p,q Projected onto the (r,v) plane Λ GRFT (p,q), that is:

[0178] Λ GRFT (p,q)=Λ p,q (50)

[0179] In this embodiment, step S7 is specifically as follows:

[0180] This embodiment uses constant false alarm rate (CFAR) P fa =10 -4 In the case of 10 6 The Monte Carlo simulation experiment yielded the detection threshold T. k The detection statistic in equation (50) is compared with the detection threshold to complete the detection decision, as shown in the following expression:

[0181]

[0182] When the detection statistic is greater than the detection threshold, the target is considered to exist; otherwise, the target is not found. The positions where the detection statistic is greater than the detection threshold are the estimated values ​​of the motion parameters. By obtaining the peak position in equation (50), the estimated value of the distance parameter can be obtained. and velocity parameter estimates

[0183]

[0184]

[0185]

[0186] To evaluate the detection performance and parameter estimation accuracy of the simulation, this embodiment designs an evaluation index system, namely the detection probability P. d Root mean error of distance estimation (RMSE(r)) p ), the root mean square error of velocity estimation (RMSE(v)) p ), and the estimation error of the number of scattering points. sc The expression is as follows:

[0187]

[0188]

[0189]

[0190]

[0191] Where, N c Indicates the number of Monte Carlos. Indicates the number of successful tests; L e (·) indicates the length operation, L T This indicates the number of true scattering points of the target in the range extension.

[0192] In this embodiment, the detection probability curves of the method of the present invention for high-speed range-extended targets of five different models are as follows: Figure 3As shown, the relationship curves between the signal-to-noise ratio and the range and velocity parameter estimations for high-speed range-extended targets using the method of this invention are as follows: Figure 4 As shown.

[0193] in, Figure 4 (a) Root mean square error of distance parameter estimation for different target models. Figure 4 (b) Root mean square error of velocity parameter estimation for different target models. The relationship between signal-to-noise ratio and scattering point estimation for high-speed range-extended targets using the method of this invention for five different models is shown in the figure below. Figure 5 As shown, this illustrates the scattering point estimation for different target models.

[0194] In addition, the method of this invention was compared with four traditional methods in terms of detection performance and motion parameter estimation accuracy, such as... Figure 6 and Figure 7 As shown. Traditional method 1 is one of the few methods that simultaneously considers both target distance movement and target distance expansion; traditional method 2 only considers the target distance movement; traditional method 3 only considers the target distance expansion; traditional method 4 does not consider either target distance expansion or distance movement. Figure 6 The detection performance of different methods on target model 1 is presented. Figure 7 (a) The performance of different methods in estimating distance parameters for different target models is presented. Figure 7 (b) The performance of different methods for estimating velocity parameters for different target models under a signal-to-noise ratio of -30dB is presented.

[0195] Figure 8 The accumulation results of the method of this invention and the traditional method 2 for target models 1, 3, and 5 at a signal-to-noise ratio of -35dB are presented, wherein... Figure 8 (a), Figure 8 (c), Figure 8 (e) represents the accumulated results of the method of the present invention. Figure 8 (b), Figure 8 (d), Figure 8 (f) shows the accumulation result of the traditional method 2, and it can be seen that the method of the present invention can better achieve the accumulation of target energy.

[0196] In summary, as can be seen from the specific embodiments of this invention, the method of this invention can effectively improve the detection performance and estimation accuracy of scattering point distribution and motion parameters (range, velocity) for high-speed, range-extended targets of arbitrary size and unknown origin. The method of this invention constructs an integrated architecture for range migration correction, adaptive scattering point estimation, and target detection of high-speed, range-extended radar targets of arbitrary size under a uniform Gaussian noise background. This achieves full accumulation and maximized utilization of radar echo energy within and between pulses, solving the problems of existing methods where range migration and extension lead to echo energy dispersion, resulting in decreased detection probability and parameter estimation accuracy.

[0197] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A joint coherent accumulation and detection method for high-speed extended-range targets using radar, comprising the following specific steps: S1. Initialize radar system parameters, read echo measurements from radar receiver and perform pulse compression processing; S2. Set the motion parameter search space: Based on the target's velocity range and region of interest, set the distance parameter search space. and velocity parameter search space ; S3. Based on step S2, a phase compensation function is constructed using the motion parameters to be searched to perform phase compensation on the pulse-compressed echo measurement, and then the covariance matrix of noise / clutter is estimated using auxiliary data. S4. Construct coherent accumulation: Substitute the covariance matrix of the echo measurement and estimation after phase compensation in step S3 into the detection model of the range extension target to construct the joint detection statistics of the extension target. S5. Based on step S4, construct a normalized energy accumulation vector, and then, based on the detection model of the binary assumption, derive the sparse signal representation model of the normalized energy accumulation vector, and establish an optimization problem for adaptively estimating the distribution of the target scattering points over the extended range. S6. Based on the greedy search method, solve the optimization problem of adaptively estimating the scattering point distribution in step S5, obtain the information on the number and location distribution of scattering points, and feed it back into step S4 for the calculation of the joint detection statistic. Record the calculated value and project it onto the detection statistic plane. Then, traverse the elements in the distance parameter search space and the velocity parameter search space and remove them. and If empty, proceed to step S7; otherwise, return to step S3. S7. Perform threshold decision. Based on steps S1-S6, save the detection statistics for each distance and velocity search to obtain three-dimensional data on the detection statistics and motion parameters. Then, use a Monte Carlo experiment to obtain the constant false alarm rate (CFAR) value. Detection threshold under certain conditions The results are compared with the detection statistics. If the detection statistics are greater than the detection threshold, the target is determined to exist; otherwise, the target is not found. The positions where the detection statistics are greater than the detection threshold are then extracted as the estimated values ​​of the motion parameters.

2. The method for joint coherent accumulation and detection of high-speed extended-range radar targets according to claim 1, characterized in that, The specific steps of S1 are as follows: S11. Radar system parameter initialization; First, initialize the radar system parameters: radar transmission signal carrier frequency. ,bandwidth Pulse repetition interval sampling frequency Radar range resolution Radar Doppler resolution The number of range-dimension resolution units corresponding to the maximum detection range of the radar Radar pulse count ; Then initialize the target parameters: set the target distance to the radar initial distance. Initial radial velocity ; S12. Acquire radar echo measurements; The raw radar echo measurements are read from the radar receiver, i.e., the simulated radar echo measurements and the actual radar echo measurements are acquired. Then, the raw radar echo measurements are preprocessed to obtain the pulse-compressed echo measurements. ; in, Describes the M-dimensional complex vector of the echo within the nth cell. This indicates a transpose operation; the preprocessing is pulse compression.

3. The method for joint coherent accumulation and detection of high-speed radar range-extended targets according to claim 2, characterized in that, Step S2 is as follows: Set the parameter space for the distance and speed to be searched. and The expression is as follows: (1) (2) in, and This indicates the search interval, i.e., the distance resolution and the Doppler resolution; Let P represent the distance parameter search at the p-th iteration and the velocity parameter search at the q-th iteration, respectively; P represents the distance parameter search space. The length of Q represents the velocity parameter search space. The length.

4. The method for joint coherent accumulation and detection of high-speed extended-range radar targets according to claim 3, characterized in that, Step S3 is as follows: S31, Phase compensation; Select the motion parameters to be searched in step S2. Construct a phase compensation function Phase compensation is performed on the pulse-compressed echo measurement, as expressed below: (3) in, Indicates the wavelength of electromagnetic waves. Indicates the pulse repetition interval. Indicates a pulse tag; The phase compensation function of equation (3) is used to compensate for the pulse compression echo measurement, and the expression is as follows: (4) in, , Indicates the sampling frequency. This represents the speed of electromagnetic wave transmission; from equation (4), it can be seen that... Indicates following the search path exist Phase compensation is performed on this basis; S32, Noise / Clutter Covariance Matrix Estimation; Estimating the covariance matrix of noise / clutter using auxiliary data when the target is absent The expression is as follows: (5) in, denoted as conjugate transpose, and R represents the length of the target auxiliary data.

5. The method for joint coherent accumulation and detection of high-speed radar range-extended targets according to claim 4, characterized in that, Step S4 is as follows: The echo measurement with phase compensation in equation (4) and the covariance matrix of noise / clutter estimated in equation (5) are combined. Substitute these values ​​into the target detection model to construct a joint detection statistic. The expression is as follows: (6) in, Represents the steering vector. This represents the possible range cell tag set for the target scattering point in echo measurement; definition Then we can obtain: (7) in, This indicates that the phase-compensated echo measurement is accumulated between multiple pulses, which is equivalent to performing a GRFT coherent accumulation. Introduce an intermediate variable The expression is as follows: (8) in, This represents the normalized value of the energy after coherent accumulation of M coherent pulses within the nth distance cell; The simplified expression for the detection statistic is as follows: (9)。 6. The method for joint coherent accumulation and detection of high-speed extended-range radar targets according to claim 5, characterized in that, Step S5 is as follows: Within the detection window, based on the binary hypothesis detection model, we can obtain: (10) in, Represents the target signal vector. Represents a noise vector; This represents the set of distance cell labels within the detection window, with a length of D. and The range cell labels represent the target signal and noise sets, respectively. Indicates the target scattering point at The possible distance unit label set; then yes A subset of, i.e. , for The complement; When the target exists, Under the assumption that, according to equation (10), the normalized energy expression after coherent accumulation is derived as follows: (11) in, and They are represented as follows: (12) (13) Define a D-dimensional normalized energy accumulation vector ,as well as , Then equation (11) can be expressed in vector form as: (14) Equation (14) is equivalent to a sparse recovery signal model; It can serve as the perceptual matrix or the dictionary matrix in a sparse recovery model. This represents a complex Gaussian noise vector with a mean of 1 / 2. covariance is ; Represents the observation vector. Represents the field of complex numbers. Let represent the sparse vector corresponding to each perceptual vector coefficient, and satisfy . , Describing the p-norm, This indicates the number of non-zero elements in the vector; but The recovery is equivalent to the recovery from the observed vector Estimating sparse vectors Establish an optimization problem for adaptively estimating the distribution of scattering points of a range-extended target: (15) in, This indicates acceptable error. Represents a D-dimensional unit vector; By introducing the Lagrange multiplier method, the optimization problem of equation (15) is redefined as follows: (16) in, Represents the loss function. Represents the Lagrange multiplier; its empirical value can be set as: (17) Finally, the optimization problem of equation (16) is solved using a greedy search method; where the recovered vector Estimation of scattering points, carrying information about their distribution. It can be represented as a vector non-zero elements ,Right now: (18)。 7. The method for joint coherent accumulation and detection of high-speed extended-range radar targets according to claim 6, characterized in that, Step S6 is as follows: The estimated scattering point information in step S5 The feedback is input into the detection statistic expression in equation (9) of step S4 to calculate the joint detection statistic, as shown in the following expression: (19) Iterate through all combinations of the distance and velocity parameters to be searched, i.e., if Return to step S3, and simultaneously, calculate the detection statistics corresponding to each parameter search. Project to flat ,Right now: (20)。 8. The method for joint coherent accumulation and detection of high-speed extended-range radar targets according to claim 7, characterized in that, Step S7 is as follows: pass The Monte Carlo simulation experiment, under constant false alarm rate In this case, the detection threshold is obtained. The detection statistic in equation (20) is compared with the detection threshold to complete the detection decision. The expression is as follows: (21) When the detection statistic is greater than the detection threshold, the target is considered to exist; otherwise, the target is not found. The positions where the detection statistic is greater than the detection threshold are the estimated values ​​of the motion parameters. By obtaining the peak position in equation (20), the estimated value of the distance parameter can be obtained. and velocity parameter estimates : (22) (23) (24) Finally, an evaluation index system was designed, namely the detection probability. Root mean error of distance estimation Root mean square error of velocity estimation And the estimation error of the number of scattering points. The expression is as follows: (25) (26) (27) (28) in, Indicates the number of Monte Carlos. Indicates the number of successful tests; This indicates a length-taking operation. This indicates the number of true scattering points of the target in the range extension.

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