Maneuvering target detection method and device based on long-time coherent accumulation

Abstract

The invention provides a maneuvering target detection method and device based on long-time coherent accumulation, and belongs to the technical field of target detection, and the method comprises the steps: firstly obtaining a baseband echo signal, carrying out the pulse compression, obtaining a time domain pulse pressure signal, and initializing distance and speed fuzzy number search parameters; then, a new slow time variable is introduced into the fast time frequency domain signal for Keystone transformation, and a signal after distance correction is obtained; then, track search based on Radon transformation is carried out on the corrected slow time domain signal; and finally, traversing all distance and speed fuzzy numbers, performing phase-coherent accumulation on each searched track through a PCFCRD method, estimating acceleration and outputting a target detection result. Compared with a traditional method, the calculation complexity is reduced.

Description

Technical Field

[0001] This application belongs to the field of target detection technology, and more specifically, relates to a method and apparatus for detecting mobile targets based on long-term coherent accumulation. Background Technology

[0002] Targets such as UAVs and near-space vehicles typically have small radar cross sections, resulting in low signal-to-noise ratios (SNR) for their echo signals. These signals may even be completely submerged in background noise and clutter, posing a significant challenge to effective radar detection. To improve the detection performance of weak and highly maneuverable targets, long-term coherent accumulation techniques have emerged. This method improves the SNR of the target signal by accumulating coherent signal energy over a longer observation period. However, for effective coherent accumulation, the target signal needs to remain focused within a fixed range-Doppler cell throughout the accumulation process. For maneuvering targets, acceleration and velocity cause range migration (RM) and Doppler frequency migration (DFM), disrupting energy focusing and reducing the accumulation gain of traditional Moving Target Detection (MTD) algorithms. Therefore, effectively compensating for RM and DFM during long-term accumulation is one of the key issues for achieving accurate detection of weak and maneuvering targets.

[0003] In recent years, researchers have proposed various detection methods based on joint multidimensional parameter search for uniformly accelerated targets. Typical algorithms include the Generalized Radon-Fourier Transform (GRFT), Radon-Fractional Fourier Transform (RFRFT), and Radon-Lv's Distribution (RLVD). Among these, GRFT is the most representative method. It compensates for the RM through three-dimensional parameter search, extracts the target trajectory, and then constructs a Doppler matched filter to correct the DFM, achieving coherent accumulation. RFRFT and RLVD utilize the characteristic that the target's slow-time-dimensional signal can be modeled as a linear frequency modulation (LFM) waveform, and achieve coherent accumulation using joint time-frequency analysis techniques FRFT and LVD. Although the above methods have achieved good detection performance, their reliance on exhaustive search of high-dimensional parameter space leads to high computational complexity, which has become the main bottleneck for their practical deployment and engineering applications. Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for detecting maneuvering targets based on long-term coherent accumulation, so as to reduce computational complexity.

[0005] A first aspect of this application provides a method for detecting maneuvering targets based on long-term coherent accumulation, comprising:

[0006] The echo signal is down-converted and shifted to the baseband frequency to obtain the baseband echo signal;

[0007] The baseband echo signal is pulse-compressed to obtain the time-domain echo signal, and the range search range and velocity ambiguity number search range are initialized.

[0008] A fast time-dimensional Fourier transform is performed on the time-domain echo signal to obtain a fast time-frequency domain signal;

[0009] By introducing a slow-time variable and performing a Keystone transform on the fast-time frequency domain signal, the corrected fast-time frequency domain signal is obtained.

[0010] The corrected time-domain signal is obtained by performing an inverse fast Fourier transform on the corrected fast time-frequency domain signal;

[0011] Based on the search range of the initial search and the search range of the velocity ambiguity number, a Radon-based trajectory search is performed on the corrected time-domain signal to obtain multiple trajectory signals;

[0012] For each trajectory signal, coherent accumulation is performed using a parameterized center frequency-frequency modulation distribution algorithm to estimate the acceleration.

[0013] The target detection result is output based on the coherent accumulation result corresponding to each trajectory signal.

[0014] A second aspect of this application provides a maneuvering target detection device based on long-term coherent accumulation, comprising:

[0015] The baseband conversion module is used to downconvert the echo signal to the baseband frequency to obtain the baseband echo signal;

[0016] The initialization module is used to perform pulse compression on the baseband echo signal to obtain the time-domain echo signal, and to initialize the range search range and the velocity ambiguity number search range.

[0017] The Fourier transform module is used to perform a fast time-dimensional Fourier transform on the time-domain echo signal to obtain a fast time-frequency domain signal;

[0018] The correction module is used to introduce a slow-time variable to perform Keystone transform on the fast-time frequency domain signal to obtain the corrected fast-time frequency domain signal;

[0019] The inverse Fourier transform module is used to perform an inverse fast Fourier transform on the corrected fast time-frequency domain signal to obtain the corrected time-domain signal.

[0020] The trajectory search module is used to perform Radon-based trajectory search on the corrected time-domain signal based on the search range of the initial search and the search range of the velocity ambiguity number, so as to obtain multiple trajectory signals;

[0021] The coherent accumulation module is used to coherently accumulate each trajectory signal using a parameterized center frequency-frequency modulation distribution algorithm to estimate acceleration.

[0022] The results output module is used to output target detection results based on the coherent accumulation results corresponding to each trajectory signal.

[0023] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for detecting a moving target based on long-term coherent accumulation.

[0024] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for detecting a moving target based on long-term coherent accumulation.

[0025] The beneficial effects of the maneuvering target detection method and apparatus based on long-term coherent accumulation provided in this application are as follows:

[0026] This application proposes a maneuvering target detection method based on long-term coherent accumulation, namely the Keystone-Radon-Parameterized-Velocity-Acceleration Distribution (KT-R-PVAD) method. First, the Keystone method is used to compensate for distance travel caused by unambiguous velocities, allowing subsequent processing to focus solely on the velocity ambiguity number, avoiding an exhaustive search of the entire velocity space. Second, since the distance travel caused by acceleration is small and can usually be ignored within a limited accumulation time, KT-R-PVAD avoids searching for acceleration compared to other Radon Fourier Transform-based methods, thus exhibiting lower computational complexity than traditional search methods such as GR FT and RFRFT. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a maneuvering target detection method based on long-term coherent accumulation, provided as an embodiment of this application;

[0029] Figure 2 A flowchart illustrating another method for detecting maneuvering targets based on long-term coherent accumulation, provided in an embodiment of this application;

[0030] Figure 3 This application provides a schematic diagram illustrating the variation of computational complexity of different algorithms with the number of accumulated pulses, as an embodiment of the present application.

[0031] Figure 4 This application provides a schematic diagram of a time-domain echo signal after pulse compression, according to one embodiment of the present application.

[0032] Figure 5 This application provides a schematic diagram of MTD coherent accumulation results in one embodiment.

[0033] Figure 6 A schematic diagram of the coherent accumulation result of a maneuvering target detection method based on long-term coherent accumulation provided in an embodiment of this application;

[0034] Figure 7 A schematic diagram illustrating the variation of detection performance of different algorithms with signal-to-noise ratio, provided for an embodiment of this application;

[0035] Figure 8 A structural block diagram of a maneuvering target detection device based on long-term coherent accumulation is provided in one embodiment of this application;

[0036] Figure 9 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0037] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0039] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for detecting a maneuvering target based on long-term coherent accumulation, provided in an embodiment of this application. The method can be executed by an electronic device and may include steps S101-S108.

[0040] S101: Down-convert the echo signal to the baseband frequency to obtain the baseband echo signal.

[0041] In this embodiment, the high-frequency echo signal is shifted to the baseband frequency range, reducing the complexity of signal processing and laying the foundation for subsequent signal processing.

[0042] S102: Perform pulse compression on the baseband echo signal to obtain the time-domain echo signal, and initialize the distance search range and the velocity ambiguity number search range.

[0043] In this embodiment, the baseband echo signal is pulse-compressed to obtain the time-domain echo signal s. p (t,t m The time-domain echo signal is represented as:

[0044]

[0045] Where A0 is the signal amplitude, f c Let C be the carrier frequency, c be the speed of light, sinc(x) = sin(πx) / πx, which represents the sigma function, B be the signal bandwidth, and t be the fast time. m For slow time, R0 is the initial radial distance, v0 is the unambiguous velocity, n is the velocity ambiguity number, and v m Here, 'a' represents blind velocity, 'j' represents acceleration, and 'j' represents the imaginary unit.

[0046] In this embodiment, R(t) m Substituting into equation 1, we get equation 2:

[0047]

[0048] In this embodiment, λ is the signal wavelength, the signal amplitude refers to the intensity amplitude of the baseband echo signal, the Singer function is used for signal shaping after pulse compression, and t m =mT r m = 0, 1, ..., M-1, T rM is the pulse repetition interval, M is the number of accumulated pulses, the initial radial distance refers to the initial distance between the radar and the target, the unambiguous velocity represents the portion of the target's true velocity that does not exceed the radar's velocity measurement range, the velocity ambiguity number is the ambiguity correction parameter when the target's velocity exceeds the radar's velocity measurement range, and the blind velocity refers to the critical velocity for radar velocity measurement, exceeding which will result in velocity ambiguity.

[0049] S103: Perform a fast time-dimensional Fourier transform on the time-domain echo signal to obtain a fast time-frequency domain signal.

[0050] In this embodiment, the fast time-frequency domain signal refers to the signal in the time domain that is distance-dimensional, which is converted into a frequency domain signal to prepare for subsequent transformations. In this embodiment, the fast time-frequency domain signal is represented as:

[0051]

[0052] Where f is the fast time frequency, f c For carrier frequency, rect represents a rectangular window function, A0 is the signal amplitude, B is the signal bandwidth, n is the velocity ambiguity number, and v m Let 'a' be the blind velocity, 'c' be the acceleration, 'R0' be the initial radial distance, and 't' be the speed of light. m This is a slow time.

[0053] This embodiment uses Fourier transform to convert the time-domain pulse compression signal to the fast time-frequency domain, removing the phase influence of the static distance component and separately representing the phase changes caused by the target motion (velocity, acceleration), thus providing a frequency domain basis for subsequent transformation compensation of distance movement.

[0054] S104: Introduce a slow-time variable to perform Keystone transform on the fast-time frequency domain signal to obtain the corrected fast-time frequency domain signal.

[0055] In this embodiment, the slow time variable is By introducing a slow-time variable and performing a Keystone transform on the fast-time frequency domain signal, the corrected fast-time frequency domain signal is obtained as follows:

[0056]

[0057] For the narrowband radar in this embodiment, f ≤ f c Then equation 4 can be simplified to:

[0058]

[0059] Among them, China This represents the distance travel caused by acceleration. In real-world radar scenarios, the distance travel caused by acceleration is small and usually negligible. Taking an accumulation time T = 500 ms and a range resolution of 10 m as an example, the distance travel caused by acceleration is... Therefore, the distance traveled due to acceleration needs to reach 80 m / s² for the acceleration-induced distance to exceed the distance resolution. Thus, the distance traveled due to acceleration can be ignored within a finite accumulation time. Ignoring the distance traveled due to acceleration, Equation 5 further simplifies to:

[0060]

[0061] In this embodiment, specifically, a Keystone transform can be performed on the fast time-frequency domain signal. This embodiment introduces a new slow-time variable to offset the distance movement caused by the unambiguous velocity, so that the signal is focused and stabilized in the distance dimension of the fast time-frequency domain, retaining only the influence of the velocity ambiguity number n and acceleration a, thus reducing the subsequent search dimensions.

[0062] S105: Perform an inverse fast Fourier transform on the corrected fast time-frequency domain signal to obtain the corrected time-domain signal.

[0063] In this embodiment, the corrected time-domain signal is represented as:

[0064]

[0065] At this point, the peak position of the signal in the fast time dimension changes with the slow time dimension, and the slow time dimension signal has a frequency response with respect to t'. m The forms of the first and second phases are similar to those of the LFM signal. Therefore, this embodiment introduces an LFM signal model and implements the coherent accumulation process of the LFM signal through PCFCRD.

[0066] In this embodiment, the frequency domain signal after Keystone correction is converted back to the time domain. At this time, the fast time dimension of the signal has eliminated the distance movement caused by the non-ambiguous velocity, and the slow time dimension exhibits the characteristics of a linear frequency modulated signal, providing a suitable signal form for subsequent transformation trajectory search.

[0067] S106: Based on the search range of the initial search and the search range of the velocity ambiguity number, perform Radon-based trajectory search on the corrected time-domain signal to obtain multiple trajectory signals.

[0068] In this embodiment, the corrected time-domain signal s KT (t,t' m The process of trajectory search is shown in Equation 8:

[0069]

[0070] Where, n s R is the fuzzy number for the search speed. s This is the initial distance for the search. When the search trajectory matches the actual trajectory, i.e., R... s =R0 and n sWhen n = , the slow time dimension signal has the form of an LFM signal (i.e., multiple trajectory signals), represented as:

[0071]

[0072] in, It is a constant representing the signal amplitude.

[0073] S107: For each trajectory signal, coherent accumulation is performed using a parameterized center frequency-frequency modulation distribution algorithm to estimate the acceleration.

[0074] In this embodiment, each trajectory signal is coherently accumulated using a parameterized center frequency-frequency modulation distribution algorithm, including:

[0075] For each trajectory signal, perform the following operations:

[0076] Based on this trajectory signal, a correlation function containing a delay variable and a constant delay is determined;

[0077] A non-uniform Fourier transform is performed on the delayed variables in the correlation function to achieve energy accumulation on the delayed variables;

[0078] The signal after energy accumulation is demodulated to obtain the linear phase signal corresponding to the trajectory signal, so as to eliminate the influence of the secondary phase.

[0079] A Fourier transform is performed on the linear phase signal corresponding to the trajectory signal to achieve coherent accumulation of the trajectory signal.

[0080] In this embodiment, the Parameterized Centroid Frequency-Chirp Rate Distribution (PCFCRD) algorithm is used. In this embodiment, a correlation function can be constructed based on the LFM signal model and the CICPF algorithm. The LFM signal model is shown in Equation 10.

[0081]

[0082] Among them, A L0 t L f0 and γ represent the signal amplitude, time variable, center frequency, and frequency modulation rate, respectively; construct a two-dimensional symmetric instantaneous autocorrelation function R(t) L ,τ), as shown in Equation 11:

[0083]

[0084] Where τ and h represent the delay variable and constant delay, respectively, and can be set in combination with historical frame data to meet the demand for the delay variable.

[0085] In this embodiment, a non-uniform Fourier transform is performed on the delay variable in the correlation function to achieve energy accumulation on the delay variable, as shown in Equation 11. By introducing the non-uniform Fourier transform, R is obtained. N (t L ,f τ )

[0086]

[0087] Where A L1 f represents the amplitude. τ Let τ be the frequency variable, and δ(·) be the impulse function. This embodiment effectively concentrates energy along the delay variable axis. However, because the signal contains elements related to t... L The influence of the associated second phase prevents the signal from achieving energy accumulation through the time-dimensional fast Fourier transform, so the influence of the second phase needs to be eliminated through demodulation processing first.

[0088] In this embodiment, coherent accumulation is achieved through FFT.

[0089]

[0090] Where A L2 Indicates amplitude, Indicates about t L The frequency. As can be seen from Equation 10, the PCFCRD method achieves... Coherent accumulation on a plane.

[0091] Comparing Equations 9 and 10, it can be seen that Equation 9 has the form of an LFM signal, i.e., s KT-R s in Equation 10 L Its center frequency is Frequency modulation is Therefore, equation (13) can be written as:

[0092]

[0093] Where A2 represents the signal amplitude. As can be seen from Equation 14, the signal energy is concentrated in the velocity-acceleration plane, and a significant peak is observed at (2f0,γ), i.e. (-4v0 / λ,-2a / λ).

[0094] S108: Output target detection results based on the coherent accumulation results corresponding to each trajectory signal.

[0095] In this embodiment, the distance search range and velocity fuzzy number search range are initialized in step S102. After Radon trajectory search, coherent accumulation, and estimation of the corresponding acceleration are completed, the energy peak value in the coherent accumulation result corresponding to each trajectory signal needs to be compared with the preset false alarm threshold. If the peak value exceeds the threshold, the corresponding trajectory is likely to be the real target; if the peak value is lower than the threshold, the corresponding trajectory is noise or clutter.

[0096] In this embodiment, all combinations with peak values ​​exceeding a preset false alarm threshold are selected, and their corresponding key parameters are output: target distance, velocity ambiguity, and acceleration. If all peak values ​​are below the threshold, the output "No target detected" is displayed.

[0097] As can be seen from the above, the maneuvering target detection method based on long-term coherent accumulation proposed in this application, namely the Keystone-Radon-Parameterized-Velocity-Acceleration Distribution (KT-R-PVAD) method based on Keystone and Radon transform, firstly uses the Keystone method (which can be simply referred to as KT in this application) to compensate for the distance movement caused by unambiguous velocities, so that subsequent processing only needs to search on the velocity ambiguity number, avoiding exhaustive search of the entire velocity space; secondly, since the distance movement caused by acceleration is small, it can usually be ignored within a limited accumulation time. Compared with other methods based on Radon Fourier transform, KT-R-PVAD avoids the search for acceleration, and therefore has lower computational complexity than traditional search methods such as GRFT and RFRFT.

[0098] refer to Figure 2 A method for detecting maneuvering targets based on long-term coherent accumulation may further include:

[0099] Step S100: Down-convert the echo signal to the baseband frequency to obtain the baseband echo signal;

[0100] Step S200: Perform pulse compression on the baseband echo signal to obtain the time-domain echo signal, and initialize the search distance and velocity ambiguity number parameters;

[0101] Step S300: Perform a fast time-dimensional Fourier transform on the time-domain echo signal to obtain a fast time-frequency domain signal;

[0102] Step S400: For the fast time frequency domain signal, introduce a new slow time variable and perform Keystone transform to obtain the corrected fast time frequency domain signal;

[0103] Step S500: Perform inverse fast Fourier transform on the corrected fast time-frequency domain signal to obtain the corrected time-domain signal.

[0104] Step S600: Perform a trajectory search based on Radon transform on the corrected time-domain signal;

[0105] Step S700: The searched trajectory signal is coherently accumulated using the parameterized center frequency-frequency modulation distribution algorithm PCFCRD to estimate the acceleration;

[0106] In step S800, if the distance and velocity ambiguities have not been completely traversed, proceed to step S600 until all distance and velocity ambiguities have been traversed. Then, target detection is performed through peak detection, and the estimated results of distance, velocity ambiguities, and acceleration parameters are output.

[0107] In this embodiment, the implementation method is the same as that in the previous embodiment, and will not be repeated here. The difference is that the cyclic execution step S800 is described as multiple trajectory signals in S106 in the previous embodiment.

[0108] This embodiment compares and analyzes the computational complexity of the algorithm (KT-R-PVAD) involved in this invention with RFRFT and GRFT algorithms. Taking a velocity fuzzy number search count of 10 as an example, the computational complexity of the three algorithms changes with the number of pulses as follows: Figure 3 As shown.

[0109] Depend on Figure 3 As can be seen, since KT-R-PVAD first corrects the distance movement caused by unambiguous velocities through Keystone Transform, it only needs to search the velocity ambiguity number of the target instead of the entire velocity space, thus significantly reducing the computational complexity compared to RFRFT. Furthermore, by utilizing the characteristic that the distance movement caused by acceleration is negligible within a finite accumulation time, it avoids searching for acceleration, thus also having lower computational complexity compared to the GRFT algorithm, which requires searching for acceleration.

[0110] The advantages of KT-R-PVAD in detection performance are verified through simulation below. The simulated radar system parameters and target parameters are shown in Tables 1 and 2, and the signal-to-noise ratio after pulse compression is 10dB.

[0111] Table 1 Radar System Parameters

[0112] Radar system parameters numerical values carrier 1GHz Pulse repetition frequency 1kHz bandwidth 5MHz Sampling rate 10MHz Pulse width 50us Accumulated pulse count 512

[0113] Table 2 Target motion parameters

[0114] Target motion parameters numerical values Distance unit 200 speed 320m / s acceleration <![CDATA[20m / s 2 ]]>

[0115] The algorithm simulation results are as follows Figures 4-7 As shown. Figure 4 This is the time-domain echo signal after pulse compression. Figure 5 The results of coherent accumulation for MTD. Figure 6 The results show the coherent accumulation of the KT-R-PVAD algorithm. It can be seen that the signal-to-noise ratio (SNR) is significantly improved after algorithm processing, verifying the algorithm's effectiveness. To evaluate the detection performance of each algorithm under noisy conditions, a constant false alarm rate (CFAR) detector was applied to each algorithm, and 300 Monte Carlo simulation experiments were conducted. The simulation parameters remained consistent with those listed in Tables 1 and 2, with the SNR after pulse compression ranging from -20 dB to 0 dB. Figure 7 The curves showing the detection probability of each method as a function of signal-to-noise ratio under the condition of a false alarm probability of 10⁻⁶ are presented. It can be seen that, thanks to the advantages of PCFCRD in accumulation performance, the KT-R-PVAD algorithm proposed in this invention has the best detection performance.

[0116] Corresponding to the above embodiment of a maneuvering target detection method based on long-term coherent accumulation, Figure 8 This is a structural block diagram of a maneuvering target detection device based on long-term coherent accumulation, provided as an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 8 The mobile target detection device 20 based on long-term coherent accumulation includes: a baseband conversion module 21, an initialization module 22, a Fourier transform module 23, a correction module 24, an inverse Fourier transform module 25, a trajectory search module 26, a coherent accumulation module 27, and a result output module 28.

[0117] Among them, the baseband conversion module 21 is used to downconvert the echo signal to the baseband frequency to obtain the baseband echo signal;

[0118] Initialization module 22 is used to perform pulse compression on the baseband echo signal to obtain the time-domain echo signal, and to initialize the range search range and the velocity ambiguity number search range;

[0119] Fourier transform module 23 is used to perform fast time-dimensional Fourier transform on the time-domain echo signal to obtain a fast time-frequency domain signal;

[0120] The correction module 24 is used to introduce a slow-time variable to perform Keystone transform on the fast-time frequency domain signal to obtain the corrected fast-time frequency domain signal;

[0121] The inverse Fourier transform module 25 is used to perform an inverse fast Fourier transform on the corrected fast time-frequency domain signal to obtain the corrected time-domain signal.

[0122] The trajectory search module 26 is used to perform Radon-based trajectory search on the corrected time-domain signal based on the search initialization search range and the velocity ambiguity number search range to obtain multiple trajectory signals;

[0123] The coherent accumulation module 27 is used to coherently accumulate each trajectory signal using a parameterized center frequency-frequency modulation distribution algorithm to estimate the acceleration.

[0124] The result output module 28 is used to output the target detection result based on the coherent accumulation result corresponding to each trajectory signal.

[0125] In one embodiment of this application, the coherent accumulation module 27 is specifically used to perform the following operations for each trajectory signal:

[0126] Based on this trajectory signal, a correlation function containing a delay variable and a constant delay is determined;

[0127] A non-uniform Fourier transform is performed on the delayed variables in the correlation function to achieve energy accumulation on the delayed variables;

[0128] The signal after energy accumulation is demodulated to obtain the linear phase signal corresponding to the trajectory signal, so as to eliminate the influence of the secondary phase.

[0129] A Fourier transform is performed on the linear phase signal corresponding to the trajectory signal to achieve coherent accumulation of the trajectory signal.

[0130] In one embodiment of this application, the time-domain echo signal is represented as:

[0131]

[0132] Where A0 is the signal amplitude, λ is the signal wavelength, c is the speed of light, sinc(x)=sin(πx) / πx represents the Singer function, B is the signal bandwidth, and t is the fast time. m For slow time, R0 is the initial radial distance, v0 is the unambiguous velocity, n is the velocity ambiguity number, and v m 'a' represents blind velocity, and 'a' represents acceleration.

[0133] In one embodiment of this application, the fast time-frequency domain signal is represented as:

[0134]

[0135] Where f is the fast time frequency, f c For carrier frequency, rect represents a rectangular window function, A0 is the signal amplitude, B is the signal bandwidth, n is the velocity ambiguity number, and v m Let 'a' be the blind velocity, 'c' be the acceleration, 'R0' be the initial radial distance, and 't' be the speed of light. m This is a slow time.

[0136] In one embodiment of this application, the slow time variable is

[0137] By introducing a slow-time variable and performing a Keystone transform on the fast-time frequency domain signal, the corrected fast-time frequency domain signal is obtained as follows:

[0138]

[0139] In one embodiment of this application, the corrected time-domain signal is represented as:

[0140]

[0141] In one embodiment of this application, the process of performing trajectory search on the corrected time-domain signal based on the search initialization search range and the velocity ambiguity number search range is implemented based on the following formula:

[0142]

[0143] Where, n s R is the fuzzy number for the search speed. s This is the initial distance for the search.

[0144] See Figure 9 , Figure 9 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 9 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 8 The functions of the baseband conversion module 21, initialization module 22, Fourier transform module 23, correction module 24, inverse Fourier transform module 25, trajectory search module 26, coherent accumulation module 27, and result output module 28 are shown.

[0145] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0146] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0147] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0148] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the embodiments of this application for a maneuvering target detection method based on long-term coherent accumulation, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0149] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0150] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0151] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces or units, or they may be electrical, mechanical, or other forms of connection.

[0154] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0155] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0156] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting maneuvering targets based on long-term coherent accumulation, characterized in that, include: The echo signal is down-converted and shifted to the baseband frequency to obtain the baseband echo signal; The baseband echo signal is pulse-compressed to obtain a time-domain echo signal, and the range search range and velocity ambiguity number search range are initialized. The time-domain echo signal is subjected to a fast time-dimensional Fourier transform to obtain a fast time-frequency domain signal; A slow-time variable is introduced to perform a Keystone transform on the fast-time frequency domain signal to obtain a corrected fast-time frequency domain signal. The corrected fast time-frequency domain signal is subjected to inverse fast Fourier transform to obtain the corrected time-domain signal; Based on the search range of the initial search and the search range of the velocity ambiguity number, a Radon-based trajectory search is performed on the corrected time-domain signal to obtain multiple trajectory signals; For each trajectory signal, coherent accumulation is performed using a parameterized center frequency-frequency modulation distribution algorithm to estimate the acceleration. The target detection result is output based on the coherent accumulation result corresponding to each trajectory signal.

2. The method for detecting maneuvering targets based on long-term coherent accumulation as described in claim 1, characterized in that, The coherent accumulation of each trajectory signal using a parameterized center frequency-frequency modulation distribution algorithm includes: For each trajectory signal, perform the following operations: Based on this trajectory signal, a correlation function containing a delay variable and a constant delay is determined; A non-uniform Fourier transform is performed on the delayed variables in the correlation function to achieve energy accumulation on the delayed variables; The signal after energy accumulation is demodulated to obtain the linear phase signal corresponding to the trajectory signal, so as to eliminate the influence of the secondary phase. A Fourier transform is performed on the linear phase signal corresponding to the trajectory signal to achieve coherent accumulation of the trajectory signal.

3. The method for detecting maneuvering targets based on long-term coherent accumulation as described in claim 1, characterized in that, The time-domain echo signal is represented as follows: Where A0 is the signal amplitude, λ is the signal wavelength, c is the speed of light, sinc(x)=sin(πx) / πx represents the Singer function, B is the signal bandwidth, and t is the fast time. m For slow time, R0 is the initial radial distance, v0 is the unambiguous velocity, n is the velocity ambiguity number, and v m 'a' represents blind velocity, and 'a' represents acceleration.

4. The method for detecting maneuvering targets based on long-term coherent accumulation as described in claim 1, characterized in that, The fast time-frequency domain signal is represented as: Where f is the fast time frequency, f c For carrier frequency, rect represents a rectangular window function, A0 is the signal amplitude, B is the signal bandwidth, n is the velocity ambiguity number, and v m Let 'a' be the blind velocity, 'c' be the acceleration, 'R0' be the initial radial distance, and 't' be the speed of light. m This is a slow time.

5. The method for detecting maneuvering targets based on long-term coherent accumulation as described in claim 4, characterized in that, The slow time variable is By introducing a slow-time variable and performing a Keystone transform on the fast-time frequency domain signal, the corrected fast-time frequency domain signal is obtained as follows:

6. The method for detecting maneuvering targets based on long-term coherent accumulation as described in claim 5, characterized in that, The corrected time-domain signal is represented as follows:

7. The method for detecting maneuvering targets based on long-term coherent accumulation as described in claim 6, characterized in that, The process of performing trajectory search on the corrected time-domain signal based on the initial search range and the velocity ambiguity number search range is implemented using the following formula: Where, n s R is the fuzzy number for the search speed. s This is the initial distance for the search.

8. A mobile target detection device based on long-term coherent accumulation, characterized in that, include: The baseband conversion module is used to downconvert the echo signal to the baseband frequency to obtain the baseband echo signal; An initialization module is used to perform pulse compression on the baseband echo signal to obtain a time-domain echo signal, and to initialize the distance search range and the velocity ambiguity number search range. The Fourier transform module is used to perform a fast time-dimensional Fourier transform on the time-domain echo signal to obtain a fast time-frequency domain signal. The correction module is used to introduce a slow-time variable to perform Keystone transform on the fast-time frequency domain signal to obtain the corrected fast-time frequency domain signal; The inverse Fourier transform module is used to perform an inverse fast Fourier transform on the corrected fast time-frequency domain signal to obtain the corrected time-domain signal. The trajectory search module is used to perform Radon-based trajectory search on the corrected time-domain signal based on the search initialization search range and the velocity ambiguity number search range to obtain multiple trajectory signals; The coherent accumulation module is used to coherently accumulate each trajectory signal using a parameterized center frequency-frequency modulation distribution algorithm to estimate acceleration. The results output module is used to output target detection results based on the coherent accumulation results corresponding to each trajectory signal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

Images