A method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT

Abstract

This invention relates to the field of synthetic broadband radar for detecting maneuvering weak targets, specifically to a method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT. The proposed method achieves long-term coherent accumulation of motion parameters estimation for weak targets in circular maneuvering scenarios. Furthermore, it provides motion parameter compensation and a one-dimensional range image focusing method for the target. Compared to methods such as stepped-GRFT, angular-stepped-GRFT can effectively improve the imaging capability of frequency-stepped radar for maneuvering weak targets.

Description

Technical Field

[0001] This invention relates to the field of synthetic broadband radar for detecting maneuvering weak targets, specifically to a method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT. The proposed method achieves long-term coherent accumulation of motion parameters estimation for weak targets in circular maneuver scenarios. Furthermore, it provides motion parameter compensation and a one-dimensional range image focusing method for the target. Compared to methods such as stepped-GRFT, angular-stepped-GRFT can effectively improve the imaging capability of frequency-stepped radar for maneuvering weak targets. Background Technology

[0002] The increasingly complex battlefield environment presents serious threats and challenges to radar detection, particularly to small RCS targets such as aircraft. Furthermore, some small RCS targets possess strong maneuverability, further increasing the difficulty of radar detection. Maintaining high-precision measurement of the motion parameters of small RCS maneuvering targets is one of the most pressing problems that modern radar systems need to solve.

[0003] To address the problem of estimating motion parameters of weak targets, the target energy can be accumulated over a long period by increasing the observation time, thereby improving the radar echo signal-to-noise ratio and enhancing the detection capability of weak targets. In 2011, J. Xu et al. (Xu J, Yu J, Peng YN, et al. Radon-Fourier Transform for Radar Target Detection(Ⅰ): Generalized Doppler Filter Bank[J]. IEEE Transaction on Aearospace and Electronic Systems, 2011, 47(2): 1186-1202.) analyzed the relationship between the target ARU phenomenon and motion parameters of various orders. They combined the generalized Radon transform with the Fourier transform and proposed a long-term coherent accumulation method based on the generalized Radon Fourier transform (GRFT), which achieved good detection results. In 2021, J Guo et al. (Liu Q, Guo J, Liang Z, et al. MotionParameter Estimation and HRRP Construction for High-Speed ​​Weak Targets Based on Modified GRFT for Synthetic-Wideband Radar With PRF Jittering[J]. IEEE Sensors Journal, 2021, 21(20): 23234-23244.) proposed a broadband coherent accumulation method based on stepped-GRFT (Stepped Generalized Radon Fourier Transform) by combining GRFT and frequency-stepped signals. Frequency-stepped signals are an important type of high-resolution radar signal with wide applications in both civilian and military fields. They utilize a series of sequentially transmitted narrowband pulses with progressively stepped carrier frequencies to achieve high-resolution range through broadband synthesis. This method enables weak target detection and radial motion parameter estimation based on long-term coherent accumulation. Furthermore, it investigates motion parameter compensation and target high-resolution range image focusing methods, effectively improving the detection and measurement capabilities of broadband radar for weak targets.

[0004] However, stepped-GRFT is limited to estimating targets moving radially. When a weak target maneuvers, stepped-GRFT cannot accurately estimate the target's motion parameters, thus failing to obtain a correct one-dimensional range image. To address the problem of detecting and imaging maneuvering weak targets with frequency-stepped radar, this invention proposes angular-stepped-GRFT based on stepped-GRFT. This effectively solves the problem of inaccurate estimation of motion parameters for maneuvering weak targets by stepped-GRFT. The coherent accumulation method based on angular-stepped-GRFT can achieve correct motion compensation and one-dimensional range image focusing for the target, effectively improving the signal-to-noise ratio of the signal after coherent accumulation processing.

[0005] Therefore, based on the frequency stepping radar system, researching a method for estimating the motion parameters of maneuvering weak targets has important practical significance and application value. Summary of the Invention

[0006] The technical problem solved by this invention is to overcome the shortcomings of the prior art and propose a motion parameter estimation method for maneuvering weak targets based on angular-stepped-GRFT. This method first performs pulse compression on the target baseband echo and compensates for the phase term. Then, it searches for the target motion parameters in the three-dimensional parameter space. Finally, based on the target motion parameter estimation results, it performs motion compensation on the maneuvering weak target in the frequency domain to achieve the effect of one-dimensional range image focusing.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] A method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT includes the following steps:

[0009] Step 1: Down-convert the target radar echo to obtain the target baseband echo, and then perform pulse compression on the obtained target baseband echo to obtain the pulse-compressed target baseband echo.

[0010] Step two: Construct a phase compensation factor and use the constructed phase compensation factor to compensate for the phase term of the target baseband echo after pulse compression obtained in step one. The constructed phase compensation factor includes target motion parameters, including the target's initial position and the distance to the radar. Target initial velocity and target maneuver angular velocity ;

[0011] Step 3: Perform a traversal search on the target motion parameters in the phase compensation factor constructed in Step 2 to obtain the accumulation result matrix. The accumulation result matrix will show a peak if and only if the searched target motion parameters are equal to the actual target motion parameters. The target motion parameters corresponding to this peak are the accurate estimation results of the target motion parameters.

[0012] Step four: Evaluate the accuracy of the estimated target motion parameters. The method is as follows: Use the accurate estimation results of the target motion parameters obtained in step three to transform the target baseband echo after pulse compression obtained in step one to the frequency domain and perform motion compensation. The frequency domain target baseband echo after motion compensation is synthesized into a broadband signal by spectrum splicing to obtain a one-dimensional range profile of each frame. Since the target is a weak target with low signal-to-noise ratio, the one-dimensional range profile of each frame is summed in the slow time dimension to obtain a one-dimensional range profile after coherent accumulation. This realizes the focusing of the one-dimensional range profile of the maneuvering weak target. The accuracy of the estimated target motion parameters is obtained based on the error between the one-dimensional range of the peak value of the focusing result and the target distance set by the simulation parameters.

[0013] In step one, the target radar echo is

[0014]

[0015] in, The sub-pulse number. The frame number, M is the number of frames in the target baseband echo. The number of sub-pulses in each frame of the signal. This is the sequence number of the sub-pulse within the frame. Indicates a fast time. Given the pulse width, and defined... ,but , For frequency modulation slope, For each sub-pulse carrier frequency, , As the initial carrier frequency, The frequency step interval; The bandwidth of the frequency-stepped signal sub-pulse;

[0016] In step one, the target baseband echo is:

[0017]

[0018] in, The instantaneous radial distance of the target;

[0019]

[0020] If a target is At any given moment, a maneuver occurs, with an initial radial distance of... The speed is The initial velocity direction is radial; the target performs a turning maneuver with a turning angular velocity of... Assuming the target's speed remains unchanged during maneuvering;

[0021] , The pulse repetition interval is when the target moves toward the radar. ;

[0022] For the target echo recovery amplitude, The speed of light;

[0023] In step one, the target baseband echo after pulse compression for:

[0024]

[0025] In step two, the method for constructing the phase compensation factor is as follows:

[0026] make The target slant distance, The target baseband echo obtained in step one after pulse compression is rewritten as follows:

[0027]

[0028] in, For the target complex scattering coefficient, in practical applications, It fluctuates and introduces phase noise; for simplicity, let's assume... It is a constant. To achieve narrowband distance resolution for frequency-stepped signals and to realize effective coherent accumulation of multi-frame signals, the phase term in the above equation needs to be adjusted. Compensation is performed, and the corresponding phase compensation factor is:

[0029]

[0030] In step three, the accumulated result matrix is... for:

[0031]

[0032] Motion parameters of the target: , , Perform a traversal search if and only if the search parameter set equals the actual motion parameters of the target. By maximizing the output and performing peak detection on the accumulated result matrix, an accurate estimate of the target motion parameters can be obtained. Considering the traversal of angular velocity, unlike stepped-GRFT which only traverses radial motion parameters, this invention refers to the algorithm defined above for detecting maneuvering weak targets using frequency step signals as the angular-stepped-GRFT algorithm.

[0033] In step four, the frequency domain target baseband echo after motion compensation is obtained. for:

[0034]

[0035] in, For faster time frequency, The target baseband echo after pulse compression The result is obtained by performing a fast-time Fourier transform to the frequency domain.

[0036] Beneficial effects

[0037] This invention proposes a motion parameter estimation method for maneuvering weak targets based on angular-stepped-GRFT, achieving long-term coherent accumulation of motion parameter estimation for weak targets in circular maneuvering scenarios. Furthermore, it presents motion parameter compensation and a one-dimensional range image focusing method for the target. Compared with methods such as stepped-GRFT, angular-stepped-GRFT can effectively improve the imaging capability of frequency-stepped radar for maneuvering weak targets. Attached Figure Description

[0038] Figure 1 The results of the stepped-RFT parameter estimation;

[0039] Figure 2 Coherently superimpose one-dimensional range images onto stepped-RFT images;

[0040] Figure 3 The results of stepped-GRFT parameter estimation (distance-velocity plane);

[0041] Figure 4 The results of stepped-GRFT parameter estimation (acceleration-velocity plane);

[0042] Figure 5 Coherent accumulation results for one-dimensional range images of stepped-GRFT targets;

[0043] Figure 6 The parameter estimation results for angular-stepped-GRFT (distance-velocity plane);

[0044] Figure 7 The parameter estimation results for angular-stepped-GRFT (acceleration-velocity plane);

[0045] Figure 8 Coherent accumulation results for one-dimensional distance images of angular-stepped-GRFT targets;

[0046] Figure 9 Coherent accumulation results of one-dimensional distance image for angular-stepped-GRFT target (enlarged local view). Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0048] The specific steps of a method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT are as follows:

[0049] Step 1: Pulse compression.

[0050] The transmitted signal of a frequency-stepping radar can be expressed as:

[0051]

[0052] in, The sub-pulse number. The frame number, . The number of sub-pulses in each frame of the signal. This is the sequence number of the sub-pulse in the frame. Indicates a fast time. Given the pulse width, and defined... . This represents the frequency modulation slope. For each sub-pulse carrier frequency, , As the initial carrier frequency, This represents the frequency step interval.

[0053] If a target is At any given moment, a maneuver occurs, with an initial radial distance of... The speed is The initial velocity direction is radial; the target performs a turning maneuver with a turning angular velocity of... Assuming the target's speed remains constant during maneuvering, the target's instantaneous radial distance can be expressed as:

[0054]

[0055] in, , This refers to the pulse repetition interval. This invention only discusses the case where the target's radial velocity is positive, i.e., the target is moving towards the radar direction. .

[0056] Based on the stop-and-go model, the baseband echo obtained by downconverting the target echo is:

[0057]

[0058] in, For the target echo recovery amplitude, The speed is the speed of light. Pulse compression is applied to the target baseband echo to obtain the range image.

[0059]

[0060] in, The bandwidth of the frequency-stepped signal sub-pulse is denoted as .

[0061] Step 2: Phase compensation.

[0062] From step one, let The target slant distance, Then the range image of the target baseband echo after pulse compression can be written as:

[0063]

[0064] in, Let be the target complex scattering coefficient. In practical applications, It fluctuates and introduces phase noise. For simplicity, let's assume... It is a constant. This represents the narrowband distance resolution of the frequency-stepped signal. To achieve effective coherent accumulation of multi-frame signals, the phase term in the above equation needs to be adjusted. Compensation is performed, and the corresponding phase compensation factor is:

[0065]

[0066] Step 3: Parameter estimation.

[0067] By following steps one and two, a coherent accumulation algorithm for detecting maneuvering weak targets using frequency-stepped signals can be obtained.

[0068]

[0069] Motion parameters of the target: , , Perform a traversal search if and only if the search parameter set equals the actual motion parameters of the target. The output is maximized. Peak detection is performed on the accumulated result matrix to obtain an accurate estimate of the target motion parameters. Considering the traversal of angular velocity, unlike stepped-GRFT which only traverses radial motion parameters, this invention refers to the algorithm defined above for detecting maneuvering weak targets using frequency step signals as the angular-stepped-GRFT algorithm.

[0070] Step 4: Motion compensation and focusing.

[0071] Based on the motion parameter estimation results of angular-stepped-GRFT, motion compensation for maneuvering weak targets is performed in the frequency domain.

[0072]

[0073] in, For faster time frequency, After pulse compression of the baseband signal, a fast-time Fourier transform is performed to obtain the following in the frequency domain:

[0074] This represents the distance migration of each sub-pulse.

[0075] After motion compensation based on angular-stepped-GRFT, a one-dimensional range profile of each frame is synthesized using a spectral stitching method. Since the target is a weak target with low signal-to-noise ratio characteristics, therefore... By summing the one-dimensional range image of the frame signal in the slow time dimension, a coherently accumulated one-dimensional range image can be obtained, thus achieving the focusing of the one-dimensional range image of the maneuvering weak target.

[0076] The present invention provides the following embodiments to illustrate the method:

[0077] The verification conditions for the implementation examples provided by this invention are shown in Table 1:

[0078] Table 1 Implementation Example Verification Conditions

[0079]

[0080] Stepped-RFT is a range-velocity two-dimensional parameter search algorithm for radially moving targets in frequency-stepped radar systems. When using stepped-RFT, the velocity search range is set to... The parameter estimation results obtained using the stepped-RFT algorithm are as follows: Figure 1 As shown, the initial radial distance of the target is estimated to be 60.93 km, with an error of 0.93 km; the target velocity is estimated to be 121.713 km / h. The error is 78.287. . Figure 2 The one-dimensional range image of the target obtained after motion compensation, synthetic broadband processing, and coherent accumulation is presented. It can be seen that the stepped-RFT algorithm cannot achieve accurate estimation and compensation of motion parameters for maneuvering weak targets. The one-dimensional range image of the target obtained after coherent accumulation is submerged in noise, and the one-dimensional range image cannot be properly focused.

[0081] Stepped-GRFT is a search algorithm for range-velocity-higher-order acceleration of radially moving targets in frequency-stepped radar systems. This invention selects a three-dimensional stepped-GRFT with the same search dimension as angular-stepped-GRFT, i.e., a range-velocity-acceleration three-dimensional search, for simulation. When using three-dimensional stepped-GRFT, the velocity search range is set to... The acceleration search range is And ensure that the acceleration search range covers the range of radial acceleration changes during the target's maneuver. Figure 3 and Figure 4 The results of estimating the initial radial distance, initial velocity, and acceleration of the target using three-dimensional stepped-GRFT are presented. The estimated initial radial distance is 62.28 km with an error of 2.28 km; the estimated target velocity is 250.934 km / h. The error is 50.934. The target acceleration is estimated to be 1.2366. . Figure 5 The one-dimensional range image of the target obtained after motion compensation, synthetic broadband processing, and coherent accumulation is presented. It can be seen that the 3D stepped-GRFT algorithm cannot accurately estimate and compensate for the motion parameters of maneuvering weak targets. The one-dimensional range image obtained after coherent accumulation shows the target submerged in noise, and the one-dimensional range image cannot be properly focused. The 3D stepped-GRFT approximates the target's motion parameters through range-velocity-acceleration parameter search; however, when the target maneuvers, the radial acceleration changes, leading to inadequate motion compensation based on the 3D stepped-GRFT, thus failing to achieve focusing of the one-dimensional range image. While selecting higher-order accelerations for the stepped-GRFT parameter search can reduce the error in motion parameter estimation and make the estimation results closer to the actual target motion state, using higher-order radial acceleration to describe the target motion state implies a four-dimensional or even more-dimensional search. Compared to a three-dimensional search, this would impose a significant computational burden, making it impractical.

[0082] When using the angular-stepped-GRFT proposed in this invention for parameter estimation, the velocity search range is set to... The angular velocity search range is In contrast, Figure 6 and Figure 7 The initial distance, initial velocity, and angular velocity of the target are estimated based on angular-stepped-GRFT. The initial distance is estimated to be 60 km with an error of 0; the initial velocity is estimated to be 199.994. The error is 0.006. The estimated angular velocity is 0.19994. The error is . Figure 8 and Figure 9 The coherent accumulation results of the one-dimensional range image of the target obtained using the angular-stepped-GRFT algorithm proposed in this invention are presented. The peak value of the target can be clearly observed from the coherent accumulation results, and the initial range estimate is 60 km, which is consistent with the simulation conditions. Clearly, when using frequency-stepped signals to measure the motion parameters of maneuvering weak targets, the target motion parameter estimation and one-dimensional range image focusing algorithm based on angular-stepped-GRFT proposed in this invention has better performance.

[0083] The results above show that after parameter estimation and motion compensation using the method proposed in this invention, accurate estimation of target motion parameters and focusing of one-dimensional range images are achieved in the scenario of weak target circular maneuvering. Compared with existing methods such as stepped-RFT and stepped-GRFT, the method of this invention demonstrates its superiority.

[0084] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT, characterized in that... Includes the following steps: Step 1: Down-convert the target radar echo to obtain the target baseband echo, and then perform pulse compression on the obtained target baseband echo to obtain the pulse-compressed target baseband echo. Step 2: Construct a phase compensation factor and use the constructed phase compensation factor to compensate for the phase term of the target baseband echo after pulse compression obtained in Step 1. Step 3: Perform a traversal search on the target motion parameters in the phase compensation factor constructed in Step 2 to obtain the accumulation result matrix. The accumulation result matrix will show a peak if and only if the searched target motion parameters are equal to the actual target motion parameters. The target motion parameters corresponding to this peak are the accurate estimation results of the target motion parameters. In step two, the method for constructing the phase compensation factor is as follows: make The target slant distance, The target baseband echo obtained in step one after pulse compression is rewritten as follows: in, The target complex scattering coefficient, It is a constant. For the narrowband range resolution of frequency-stepped signals, the phase term Compensation is performed, and the corresponding phase compensation factor is: In step two, the constructed phase compensation factor includes target motion parameters, which include the distance between the target's initial position and the radar. Target initial velocity and target maneuver angular velocity ; In step one, the target radar echo is: in, The sub-pulse number. The frame number, M is the number of frames in the target baseband echo. The number of sub-pulses in each frame of the signal. This is the sequence number of the sub-pulse within the frame. Indicates a fast time. Given the pulse width, and defined... ,but , For frequency modulation slope, For each sub-pulse carrier frequency, , As the initial carrier frequency, The frequency step interval; The bandwidth of the frequency-stepped signal sub-pulse; In step one, the target baseband echo is: in, The instantaneous radial distance of the target; If a target is At any given moment, a maneuver occurs, with an initial radial distance of... The speed is The initial velocity direction is radial; the target performs a turning maneuver with a turning angular velocity of... Assume that the target's speed remains unchanged during maneuvering; , The pulse repetition interval is when the target moves toward the radar. ; For the target echo recovery amplitude, It is the speed of light.

2. The method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT according to claim 1, characterized in that: In step one, the target baseband echo after pulse compression for: 。 3. The method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT according to claim 1, characterized in that: In step three, the accumulated result matrix is... for: 。 4. The method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT according to claim 1, characterized in that: In step two, the frequency domain target baseband echo after motion compensation is... for: in, For faster time frequency, The target baseband echo after pulse compression The result is obtained by performing a fast-time Fourier transform to the frequency domain.

5. The method for estimating motion parameters of maneuvering weak targets based on angular-stepped-GRFT according to claim 1, characterized in that: The accuracy of the estimated target motion parameters is evaluated as follows: using the accurate estimation results of the target motion parameters obtained in step three, the target baseband echo after pulse compression obtained in step one is transformed to the frequency domain and motion compensation is performed. The frequency domain target baseband echo after motion compensation is synthesized into a broadband signal by spectrum splicing to obtain a one-dimensional range image of each frame. The one-dimensional range image of each frame is summed in the slow time dimension to obtain a coherently accumulated one-dimensional range image, thus realizing the focusing of the one-dimensional range image of the maneuvering weak target. The accuracy of the estimated target motion parameters is obtained based on the error between the one-dimensional range of the peak value of the focusing result and the target range set by the simulation parameters.

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