Long-time accumulation and parameter estimation method under frequency-agile radar system

Abstract

The application relates to a long-time accumulation and parameter estimation method under a frequency agile radar system, which comprises the following steps: radar echo signal down-conversion and pulse compression; search parameter interval and search interval initialization; non-uniform frequency domain generalized Radon Fourier transform is adopted to complete distance correction, Doppler phase compensation and hopping phase elimination under specific search parameters, and coherent accumulation is completed; search parameters are traversed, constant false alarm detection is carried out after signal energy is projected to the search parameter domain, if there is a target, the target motion parameters are determined according to the position of the energy peak, and the target motion track is output. The application effectively improves the echo signal-to-noise ratio of a complex maneuvering target and has target detection capability under a strong noise background.

Description

Long-term accumulation and parameter estimation methods under frequency-agile radar system Technical Field

[0001] This invention relates to a method for long-term accumulation and parameter estimation under a frequency-agile radar system. Background Technology

[0002] Frequency-agile signals are widely used in the military field due to their low probability of detection and interception, high agility, and strong anti-interference capabilities. These signals have a large synthesis bandwidth, which can effectively improve range resolution, eliminate Doppler blur, and increase data rate. Common sparse recovery techniques such as compressed sensing and iterative mesh optimization can be used to process frequency-agile signals, extract target Doppler information, and accumulate it. It should be noted that these techniques only perform well in high signal-to-noise ratio (SNR) scenarios; their detection performance significantly decreases in complex scenarios.

[0003] With the rapid development of modern aircraft such as stealth targets, anti-radiation missiles, and near-space vehicles in recent years, traditional detection algorithms have failed due to their inability to effectively improve the accumulated SNR caused by their high maneuverability and low detectability. For the search and detection of these targets, excessively low SNR has become a major factor restricting radar detection performance. New radar systems typically utilize signal processing methods such as digital beamforming to increase the target's dwell time within the radar beam, thereby increasing echo energy and thus improving SNR by extending the accumulation time. However, long-term accumulation faces two main problems: the high speed of the target and the increased radar range resolution will cause the echo pulse envelope to span multiple range cells, known as range...

[0004] Doppler walk effect: Due to the high mobility of the target or the complex environment, the target echo generates a high-order phase, and the Doppler frequency therefore has time-varying characteristics. This will cause the echo to broaden and shift in the Doppler dimension, which is called the Doppler walk effect. These effects will significantly reduce the echo accumulation gain.

[0005] To address the aforementioned issues, existing mainstream algorithms primarily include the generalized Radon Fourier transform (GRFT), Radon Lv's distribution (RLVD), Radon fractional ambiguity function (RFrAF), and Radon linear canonical transform (RLCT). These methods simultaneously compensate for distance travel and Doppler travel during the accumulation time through a multi-dimensional grid search, projecting the accumulated echo energy into a multi-dimensional parameter space constructed from the search parameters to form a focused spike, thereby achieving coherent accumulation and parameter estimation. Because these algorithms do not involve nonlinear processing, they suffer from no SNR loss and perform excellently in low SNR scenarios. However, the effectiveness of these methods relies on the coherence of the echo pulse. Since the carrier frequency of frequency-agile radar is not constant between pulses, the coherence of the echo pulse is disrupted. Random bias of carrier frequency will produce phase jump effect. Therefore, this type of method cannot fully achieve coherent accumulation of pulse energy, which leads to a sharp decrease in target detection performance.

[0006] In summary, eliminating the range drift, Doppler drift, and phase jump effects in the detection of high-speed maneuvering weak targets under frequency-agile radar systems is crucial for achieving long-term accumulation and urgently needs to be addressed. Summary of the Invention

[0007] The purpose of this invention is to provide a long-term accumulation and parameter estimation method under frequency-agile radar system, which can solve the problems of range migration, Doppler migration and phase jump generated during the accumulation of weak targets at high speed under frequency-agile radar system, so as to improve the echo signal accumulation gain and improve radar detection performance.

[0008] To achieve the above objectives, this invention proposes a long-term accumulation and parameter estimation algorithm, which includes the following steps:

[0009] S1: Acquire radar echo data of moving targets and discretize it, then perform down-conversion and pulse compression to obtain a fast-time-slow-time two-dimensional data matrix;

[0010] S2: Determine the parameter search dimension, search range, search interval, and search sequence length of NUFD-GRFT based on the target motion state and actual detection requirements;

[0011] S3: Transform the time-domain pulse compression signal to the range frequency domain, construct the phase compensation function corresponding to the search parameters, and perform NUFD-GRFT on the signal to simultaneously compensate for range travel, Doppler travel and phase jump, thus completing the coherent accumulation of the echo signal;

[0012] S4: Traverse all search parameters, construct a NUFD-GRFT domain detection unit graph, perform constant false alarm rate (CFAR) detection on it, and determine the presence or absence of the target.

[0013] S5: If the determination is that a target exists, then determine the estimated value of the target motion parameters based on the location of the target peak, and use the corresponding search curve as the estimated motion trace of the target.

[0014] A preferred example of a long-term accumulation and parameter estimation algorithm based on NUFD-GRFT under a frequency-agile system includes the following steps:

[0015] S1: At the receiver of the coherent radar, the radar samples the received radar echo data, after amplification and clipping, along the fast and slow time axes. Typically, the range-axis sampling time interval equals the radar range resolution cell, and the slow-time dimension sampling frequency equals the pulse repetition frequency. The radar down-converts and compresses the received baseband echo signal to obtain a fast-time-slow-time two-dimensional pulse compression signal containing target information, as follows:

[0016]

[0017] S2: Based on the target's maneuverability and actual detection requirements, determine the target parameters' search dimension, search range, search interval, and search sequence length. Specifically, the initial radial distance search range is [r min ,r max The search range must cover the area where the target's initial position is located. The search interval is the size of the radar range element resolution, i.e., Δr = c / 2B. The initial radial range search sequence length is... Based on the expected target motion state, the radial velocity search range is [v min ,v max The search interval is the size of the radar Doppler cell resolution, i.e., Δv = λ / 2T. The radial velocity search sequence length is... The search range for radial first-order acceleration is [a 1,min ,a 1,max The search interval is Δa1=λ / 2T 2 The length of the radial first-order acceleration search sequence is The search range for radial second-order acceleration is [a 2,min ,a 2,max The search interval is Δa2=λ / 2T3 The length of the radial second-order acceleration search sequence is

[0018] S3: Performing an FFT on equation (16) along the fast time path yields the frequency domain pulse compression signal:

[0019]

[0020] Next, the following phase compensation function is constructed:

[0021]

[0022] Secondly, by multiplying the compensation function (18) by the two-dimensional pulse compression data matrix (17), we can obtain:

[0023]

[0024] Perform an IFFT along the fast time path of the above equation to restore the frequency domain signal to the time domain:

[0025] g r (t m ,τ)=IFFT f [G r (t m ,f)](20),

[0026] Finally, the energy of the accumulated pulse train is summed along the slow time domain, and the amplitude value within the distance cell where the echo pulse energy is located is projected onto the corresponding search parameter domain to complete one NUFD-GRFT:

[0027]

[0028] S4: By iterating through all search parameters and obtaining the accumulated echo energy amplitude under all search parameters, the four-dimensional NUFD-GRFT search parameter space R(r) can be constructed. i ,v j ,a 1,p ,a 2,q Its amplitude is used as a detection statistic and compared with an adaptive detection threshold given a false alarm probability.

[0029]

[0030] Where η is the detection threshold. If the amplitude of the detection unit exceeds the threshold, it is determined that a target exists; if the amplitude of the detection unit is below the threshold, it is determined that no target exists, and subsequent detection units are processed.

[0031] S5: If a target exists within the decision unit graph, the estimated target parameters can be obtained based on the peak coordinates of the detection unit in the NUFD-GRFT domain where the target is located, as follows:

[0032]

[0033] Therefore, the estimate of the target's motion trace can be expressed as:

[0034]

[0035] The present invention also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the high-speed maneuvering weak target long-term accumulation and parameter estimation detection method described above.

[0036] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the high-speed maneuvering weak target long-term accumulation and parameter estimation detection method described in any of the above claims.

[0037] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0038] 1. This invention provides a long-term accumulation and parameter estimation algorithm, computer equipment, and computer-readable storage medium for frequency-agile radar systems. Unlike conventional pulse-Doppler detection methods, this invention proposes a non-uniform frequency domain generalized Radon Fourier transform target detection method, which solves the phase jump problem caused by random carrier frequency offset during accumulation and effectively improves the accumulation SNR.

[0039] 2. The present invention provides a long-term accumulation and parameter estimation method under frequency-agile radar system, which effectively solves the range migration and Doppler migration effects generated during long-term coherent accumulation of weak targets with high speed and maneuverability, greatly improves the echo signal accumulation gain, and thus improves radar detection performance.

[0040] 3. This invention provides a long-term accumulation and parameter estimation method for frequency-agile radar systems. It employs a multi-dimensional joint search approach to complete coherent accumulation and obtain target motion parameters. Since there are no nonlinear operations during processing, the SNR loss is almost negligible. Therefore, the algorithm of this invention exhibits strong robustness for detecting weak targets in low SNR scenarios.

[0041] 4. This invention provides a long-term accumulation and parameter estimation method for frequency-agile radar systems. Unlike conventional Radon-type processing methods, this invention converts the time-domain pulse compression signal to the frequency domain for processing. Doppler matched filtering is achieved by constructing a phase compensation function in the frequency domain and multiplying it by the conjugate of the original signal in the frequency domain. This processing method eliminates the pulse offset quantization error caused by cyclic shifting and addressing operations in the time-domain processing. Therefore, the method provided by this invention improves the estimation accuracy. Attached Figure Description

[0042] Figure 1 is a flowchart of a long-term accumulation and parameter estimation method under a frequency-agile radar system in an embodiment of the present invention.

[0043] Figure 2 is a schematic diagram of the processing flow of a long-term accumulation and parameter estimation method under a frequency-agile radar system in an embodiment of the present invention.

[0044] Figure 3 is a flowchart of a long-term accumulation and parameter estimation method under a frequency-agile radar system in an embodiment of the present invention.

[0045] Figure 4(a) shows the pulse compression results of the echo signal;

[0046] Figure 4(b) is a cross-sectional view of the cumulative results of radial velocity and first-order acceleration;

[0047] Figure 4(c) is a cross-sectional view of the cumulative results of first-order acceleration and second-order acceleration;

[0048] Figure 4(d) is a cross-sectional view of the initial radial distance accumulation results. Detailed Implementation

[0049] This invention relates to a technique for performing envelope alignment and phase compensation and eliminating phase jumps in radar echo signals across range cells, across Doppler cells, and in the presence of phase jumps, and for completing coherent accumulation of target echo energy and estimation of target motion parameters.

[0050] Referring to the accompanying drawings illustrating embodiments of the invention, the invention will be described in more detail below. However, the invention may be implemented in different forms, specifications, etc., and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are presented to achieve a full and complete disclosure and to enable those skilled in the art to fully understand the scope of the invention. In these drawings, relative dimensions may be enlarged or reduced for clarity.

[0051] As shown in Figure 1, this embodiment provides a long-term accumulation and parameter estimation algorithm for frequency-agile radar systems. The specific implementation steps are as follows:

[0052] S1: Acquire moving target radar echo data and discretize it, then perform down-conversion and pulse compression processing.

[0053] The coherent radar transmits observation signals and receives the returned echo data. The M sets of echo data to be accumulated are sampled, and the sampled data are discretized to extract the target observation value S in the fast-time-slow-time two-dimensional plane. r (t m The discretized radar echo data is down-converted and pulse compressed to obtain a two-dimensional pulse compression matrix for coherent accumulation detection.

[0054] Specifically, the frequency-agile radar transmits a set of linear frequency modulated signals:

[0055] s t (t m ,τ)=rect(τ / T p )exp(jπγτ 2 )exp[j2π(f c +χ(n)Δf)(t m +τ)] (25),

[0056] In the formula, rect() represents the gate function, and γ = B / T p Indicates frequency modulation, B is the signal bandwidth, and T is the frequency. p f represents the pulse duration. c Let be the reference frequency of the carrier frequency, χ(n) be a random sequence satisfying a uniform distribution in the range [-α, +α], where 0 < α ≤ 0.5, Δf be the frequency interval, τ be the fast time, i.e., the distance time, and t be the distance time. m =mT r m = 0, 1, ..., M-1 represents slow time, T r M represents the pulse repetition period, and M represents the accumulated pulse number.

[0057] Assuming a single target exists within the radar detection area, the baseband echo signal received by the radar within one coherent processing interval takes the following form:

[0058]

[0059] Where A0 is the complex amplitude value and c is the speed of light.

[0060] Where r(t) m The instantaneous radial distance of the target relative to the radar is given by the following formula:

[0061]

[0062] In the formula, r0 is the initial radial distance of the target, v0 is the radial velocity of the target, a1 is the first-order radial acceleration of the target, and a2 is the second-order radial acceleration of the target.

[0063] The baseband echo signal is down-converted and pulse compressed to obtain a fast-time-slow-time two-dimensional pulse compression signal containing target information, as follows:

[0064]

[0065] Where A1 is the complex amplitude of the signal.

[0066] S2: Initialize search parameters, determine the search parameter dimensions, search range, search interval and discretized search sequence.

[0067] S2-1: Determine the search range for the initial radial distance, radial velocity, first-order radial acceleration, and second-order radial acceleration as [r]. min ,r max ]、[v min ,v max ]、[a 1,min ,a 1,max ] and [a 2,min ,a 2,max ], where r min and r max These represent the minimum search distance and the maximum search distance, respectively. min and v max These represent the minimum search speed and the maximum search speed, respectively. 1,min and a 1,max Let a represent the minimum search first-order acceleration and the maximum search first-order acceleration, respectively. 2,min and a 2,max These represent the minimum and maximum second-order search accelerations, respectively. It should be noted that the range of search parameters can be determined based on radar observation data, as well as some prior information and motion characteristics of the target.

[0068] S2-2: Determine the search intervals for the initial radial distance, radial velocity, first-order radial acceleration, and second-order radial acceleration, expressed as Δr = c / 2B, Δv = λ²T, and Δa1 = λ / 2T, respectively. 2 With Δa2=λ / 2T 3 , where λ=c / f c Where T is the signal wavelength, and T is a coherent processing interval;

[0069] S2-3: Determine the initial radial distance, radial velocity, first-order radial acceleration, and second-order radial acceleration search sequence lengths, respectively expressed as... and in This represents the floor function;

[0070] S2-4: Determine the search sequences for the initial discretized radial distance, radial velocity, first-order radial acceleration, and second-order radial acceleration, respectively, as follows:

[0071] r i =r min +iΔr,i=0,1,...,N r -1 (29),

[0072] v j =v min +jΔv,j=0,1,...,N v -1 (30),

[0073]

[0074]

[0075] This completes the search for parameters (r) i ,v j ,a 1,p ,a 2,q Initialization settings.

[0076] S3: Perform NUFD-GRFT on the signal to complete distance correction, Doppler correction and eliminate phase jump, and achieve coherent accumulation of the echo signal.

[0077] Specifically, step S3 further includes:

[0078] S3-1: Performing an FFT on equation (28) along a fast time, the frequency domain pulse compression signal can be expressed as:

[0079]

[0080] Where A2 is the complex amplitude value, and f is the frequency corresponding to the fast time, i.e., the distance frequency.

[0081] S3-2: Constructing the conjugate phase compensation function in the fast time-frequency domain for the exponential terms that cause range travel and Doppler travel:

[0082]

[0083] Where, r i ∈[r min ,r max ], i = 1, 2, ..., N r ;v j ∈[v min ,v max ], j = 1, 2, ..., Nv ;a 1,p ∈[a 1,min ,a 1,max p = 1, 2, ..., N a1 ;a 2,q ∈[a 2,min ,a 2,max ], q=1,2,…,N 2,q .

[0084] S3-3: Multiplying the compensation function (34) by the fast-time frequency-slow-time two-dimensional pulse compression data matrix (33), we get:

[0085]

[0086] S3-4: Perform an IFFT along the fast time path on the above equation to restore the frequency domain signal to the time domain:

[0087] g r (t m ,τ)=IFFT f [G r (t m ,f)] (36),

[0088] S3-5: Accumulate the energy of the accumulated pulse train along the slow time domain, and project the amplitude value within the distance cell where the echo pulse energy is located onto the corresponding search parameter domain to complete one NUFD-GRFT:

[0089]

[0090] Specifically, the frequency domain discrete form of NUFD-GRFT can be expressed as:

[0091]

[0092] Where m and n are the index values ​​of the slow-time and fast-time frequency sequences, respectively. F r =f s / L represents a fast time-frequency resolution unit, f s Where L is the fast time sampling frequency, and L is the number of fast time dimension sampling points.

[0093] Performing an IFFT on equation (38), transforming the compensated pulse compression signal to the time domain, and then adding the accumulated pulse trains in phase, the discrete form of this process can be expressed as:

[0094]

[0095] Where k is the fast time series index corresponding to n.

[0096] S4: After traversing all search parameters, perform CFAR detection on the output results to determine whether the target exists.

[0097] Iterate through all search parameters (r) i ,v j ,a 1,p ,a 2,q Repeat steps S3-1 to S3-5 above to obtain the echo energy accumulation amplitude under all search parameters, thus completing the four-dimensional NUFD-GRFT search parameter space R(r i ,v j ,a 1,p ,a 2,q The accumulated amplitude is used as the detection statistic and compared with an adaptive detection threshold given a false alarm probability.

[0098]

[0099] Here, η is the detection threshold, determined by a given false alarm probability and reference cells surrounding the detection unit. H1 corresponds to the presence of the target, and H0 corresponds to the absence of the target. If the amplitude of the detection unit exceeds the threshold, it is determined that a target exists; if the amplitude of the detection unit is below the threshold, it is determined that no target exists, and subsequent detection units are processed.

[0100] S5: Based on the above analysis, if CFAR detection determines the existence of a target, and when the search parameter r i =r0,v j =v0,a 1,p =a1,a 2,q When = a2, the constructed frequency domain phase compensation function H(t) m f) can accurately compensate for and eliminate the distance travel, Doppler travel, and phase jump caused by the phase term in equation (33). Therefore, the NUFD-GRFT output under this condition is:

[0101]

[0102] From the above formula, we can see that when the search parameters match the true parameters of the target, the echo energy can be completely extracted and accumulated, and the accumulated output reaches its maximum value. Therefore, based on the peak coordinates corresponding to the NUFD-GRFT domain detection unit where the target is located, we can obtain the estimated values ​​of the target parameters as follows:

[0103]

[0104] This leads to the target motion point trace corresponding to equation (27):

[0105]

[0106] Figure 2 shows a schematic diagram of the processing flow of the NUFD-GRFT detection method.

[0107] Figure 3 shows the full flowchart of the long-term accumulation and parameter estimation method provided by the present invention.

[0108] As shown in Figures 4(a) to 4(d), the performance of the long-term accumulation and parameter estimation method for high-speed maneuvering weak targets provided by this invention was also verified by simulation experiments. The target motion parameters were set as r0 = 50km, v = 1700m / s, a1 = 20m / s. 2 a2=10m / s 3 The simulation results show that the method provided by this invention can effectively detect targets and accurately estimate their motion parameters.

[0109] In particular, the present invention also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the high-speed maneuvering weak target long-term accumulation and parameter estimation detection method described above.

[0110] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the high-speed maneuvering weak target long-term accumulation and parameter estimation detection method described in any of the above claims.

[0111] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the method for long-term accumulation and parameter estimation of high-speed, high-maneuverability, and weak targets, which will not be repeated here.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for long-term accumulation and parameter estimation under frequency-agile radar technology, characterized in that, include: Step S1: Acquire radar echo data of moving targets and discretize it, then perform down-conversion and pulse compression processing to obtain a two-dimensional pulse compression data matrix with fast and slow time. Step S2: Based on the radar system parameters and target motion type, establish the instantaneous radial distance equation between the target and the radar, and determine the search range and search interval for the target motion parameters to be compensated according to actual engineering requirements. Step S3: After transforming the time-domain pulse compression signal to the range-frequency domain, construct a frequency-domain phase compensation function based on specific initial search range, search velocity, first-order search acceleration, and second-order search acceleration. Multiply the compensation function with the frequency-domain pulse compression signal and perform an inverse Fourier transform on the signal. After transforming the signal to the time domain, extract and accumulate the pulse energy data from the range and Doppler two-dimensional data matrices to obtain the phase... Step S4: Traverse all target initial radial distance, velocity, first-order acceleration, and second-order acceleration search parameters, repeat step S3, construct the accumulated energy amplitude under different search parameters into a NUFD-GRFT domain detection unit map, and perform constant false alarm detection on the NUFD-GRFT domain detection unit map to determine the presence of the target; Step S5: If the target is determined to exist, complete the target motion parameter estimation based on the corresponding peak position coordinates in the NUFD-GRFT domain detection unit where the target is located; simultaneously, use the corresponding search curve as the third-order motion trace equation of the high-speed maneuvering target: Where r(t) m ) for t m The instantaneous radial distance between the radar and the target at time t is given by r0, where r0 is the initial radial distance of the target, v0 is the radial velocity of the target, a1 is the first-order radial acceleration of the target, and a2 is the second-order radial acceleration of the target. m =mT r m = 0, 1, ..., M-1 is a slow time series, T r is the pulse repetition interval, and M is the accumulated pulse number.

2. The method for long-term accumulation and parameter estimation under frequency-agile radar system according to claim 1, characterized in that, In step S1, assuming the transmitted signal is a linearly frequency-modulated signal, the time-domain signal after pulse compression can be expressed as: In the formula, A1 is the target backscattering coefficient, c is the speed of light, B is the signal bandwidth, χ(n) is a random sequence that satisfies a uniform distribution in the range [-α, +α], Δf is the frequency interval, and f c The reference frequency is the carrier frequency.

3. The method for long-term accumulation and parameter estimation under frequency-agile radar system according to claim 1, characterized in that, Step S2 includes: S2-1, determining the search range for the initial radial distance, radial velocity, first-order radial acceleration, and second-order radial acceleration as [r]. min ,r max ]、[v min ,v max ]、[a 1,min ,a 1,max ] and [a 2,min ,a 2,max ], where r min and r max These represent the minimum search distance and the maximum search distance, respectively. min and v max These represent the minimum search speed and the maximum search speed, respectively. 1,min and a 1,max Let a represent the minimum search first-order acceleration and the maximum search first-order acceleration, respectively. 2,min and a 2,max S2-2 represents the minimum and maximum search second-order accelerations, respectively; S2-2 determines the search intervals for the initial radial distance, radial velocity, first-order radial acceleration, and second-order radial acceleration, expressed as Δr = c / 2B, Δv = λ / 2T, and Δa1 = λ / 2T, respectively. 2 With Δa2=λ / 2T 3 , where λ=c / f c Where is the signal wavelength, and T is a coherent processing interval; S2-3, the initial radial distance, radial velocity, first-order radial acceleration, and second-order radial acceleration search sequence lengths are respectively expressed as... and in, S2-4 represents the floor function; it determines the initial discretization search sequences for radial distance, radial velocity, first-order radial acceleration, and second-order radial acceleration, respectively, and is expressed as: r i =r min +iΔr,i=0,1,...,N r -1 (3), v j =v min +jΔv,j=0,1,...,N v -1 (4), This completes the search for parameters (r) i ,v j ,a 1,p ,a 2,q Initialization settings.

4. The method for long-term accumulation and parameter estimation under frequency-agile radar system according to claim 1, characterized in that, Step S3 includes: S3-1, performing an FFT on equation (2) along the fast time, the frequency domain pulse compression signal can be expressed as: Where A2 is the complex amplitude value, f is the frequency corresponding to the fast time, i.e., the range frequency; S3-2, construct the conjugate phase compensation function of the exponential terms that cause range travel and Doppler travel in the fast time frequency domain: Where, r i v[r min ,r max ], i = 1, 2, ..., N r ;v j ∈[v min ,v max ], j = 1, 2, ..., N v ;a 1,p ∈[a1,min,a 1,max p = 1, 2, ..., N a1 ;a 2,q ∈[a 2,min ,a 2,max ], q=1,2,…,N 2,q S3-3, multiplying the compensation function (8) by the fast-time frequency-slow-time two-dimensional pulse compression data matrix (9), we get: S3-4, Perform an IFFT along the fast time path of the above equation to restore the frequency domain signal to the time domain: g r (t m ,τ)=IFFT f [G r (t m ,f)](11),S3-5, accumulate the energy of the accumulated pulse train along the slow time domain, and project the amplitude value of the echo pulse energy in the distance cell to the corresponding search parameter domain to complete one NUFD-GRFT: By iterating through all search parameters and obtaining the echo energy accumulation amplitude under all search parameters, the four-dimensional NUFD-GRFT search parameter space can be constructed.

5. The method for long-term accumulation and parameter estimation under frequency-agile radar system according to claim 1, characterized in that, The constant false alarm rate (CFAR) detection is performed in step S4, and the expression is: Where η is the adaptive detection threshold, which is determined by the given false alarm probability and the reference cells around the detection unit. H1 corresponds to the existence of the target, and H0 corresponds to the non-existence of the target.

6. The long-term accumulation and parameter estimation method for frequency-agile radar systems according to claim 1 or claim 4, characterized in that, The methods for estimating target motion parameters and motion traces are as follows: Based on the peak coordinates corresponding to the NUFD-GRFT domain detection unit where the target is located, the estimated target parameters can be obtained as follows: in, This is an estimate of the initial radial distance. This is an estimate of the radial velocity. This is an estimate of the first-order radial acceleration. The radial second-order acceleration is estimated; then the target motion point corresponding to equation (1) is estimated:

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