Method and device for detecting a distant, high-speed and weak target based on DP-RFT

Abstract

The present disclosure provides a kind of long-distance high-speed weak target detection method and device based on DP-RFT, belong to radar detection technical field.In the target detection process, first, dynamic programming (DP) algorithm is used to estimate target motion parameters, and the DP parameter estimation result is used as the prior information of Radon Fourier transform (RFT) algorithm to solve the blind speed sidelobe problem of RFT long-time accumulation;Then, using the target motion parameters obtained by the DP algorithm as the center, the RFT algorithm is used for coherent accumulation to obtain the target motion parameter estimation value for compensation.Then, the target echo after motion parameter compensation is synthesized to obtain the target high-resolution one-dimensional range image.The present application can estimate the target motion parameters and synthesize the target high-resolution one-dimensional range image in the case of range ambiguity, avoid blind speed sidelobe, reduce the amount of calculation, so as to realize the stable detection of long-distance high-speed weak target.

Description

Technical Field

[0001] The present invention belongs to the field of radar detection technology, and in particular relates to a DP-RFT-based long-range, high-speed, and faint target radar detection method and device. Background Art

[0002] Radar is a crucial means of airborne early warning, enabling all-day, all-weather monitoring. The characteristics of current aerial targets are becoming increasingly complex, manifesting in weak echoes, high speeds, and complex motion patterns. Research on radar long-range detection algorithms for high-speed, weak targets, tailored to these characteristics, is crucial for achieving space surveillance and air threat early warning.

[0003] High-speed targets can reach speeds of several Mach to Mach 20, and require long-range detection as early as possible to improve warning time. However, the "plasma sheath" effect makes the aircraft "thermally invisible" to radar detection, with a radar cross-section (RCS) of only 0.02m 2 ~0.5m 2 Targets with weak electromagnetic scattering are difficult to detect. The characteristics of high-speed, weak targets are deeply coupled with the need for long-range detection, resulting in ineffective detection using traditional methods and low detection rates. Therefore, there is an urgent need to develop radar accumulation detection methods that can stably detect high-speed, weak targets at long distances. Summary of the Invention

[0004] In view of this, the present invention provides a long-range, high-speed, and weak target radar detection method based on dynamic programming and Radon Fourier transform (DP-RFT), which can achieve stable detection of long-range, high-speed, and weak targets.

[0005] In order to solve the above technical problems, the present invention is implemented as follows.

[0006] A method for detecting long-range, high-speed, and faint targets based on DP-RFT, comprising the following steps:

[0007] Step 1: Perform range gating filtering on the radar echo;

[0008] Step 2: Motion parameter estimation and compensation: A dynamic programming (DP) algorithm is used to estimate the target motion parameters. The DP algorithm parameter estimation results serve as prior information to solve the problem of long-term accumulation of blind velocity sidelobes in the Radon Fourier transform (RFT) algorithm. With the target motion parameters obtained by the DP algorithm as the center, the RFT algorithm is used for coherent accumulation to obtain the target motion parameter estimation value and perform compensation.

[0009] Step 3: synthesize the target one-dimensional range image of the target echo after the target motion parameter compensation.

[0010] Preferably, the method further comprises: constructing a long-range, high-speed, weak target echo model s(t,m) under range gating as:

[0011]

[0012] Where t is the fast time, m is the sub-pulse sequence number, A r is the target complex amplitude, rect() represents the rectangular function, R m is the instantaneous radial distance of the target center at the mth sub-pulse, T p is the sub-pulse width, exp() represents the exponential function, f0 is the signal carrier frequency, Δf is the frequency hopping interval, d r (m) is the echo frequency sequence, c is the speed of light;

[0013] When the gate frequency is matched, the filter gate frequency and the echo frequency correspond one to one, then d(m) = d r (m), d(m) filter selection frequency sequence, phase encoding decoding error-free, the error term ε(t,m) is 0, the corresponding target echo after down-conversion to baseband is:

[0014]

[0015] When the gating is mismatched, there is a deviation between the filter gating frequency and the echo frequency, there is an error in the phase decoding, and ε(t,m) is not 0. The corresponding target echo after down-conversion to baseband is:

[0016]

[0017] Among them, the error term d′(m) is the offset echo frequency, d(m) is the gated frequency sequence, is the phase-coded sequence of the echo, is the phase coding sequence of the transmitted signal;

[0018] The existence of error terms affects the estimation of target motion parameters and the synthesis of one-dimensional range images;

[0019] The phase encoding of the transmitted signal is optimized to maximize the error caused by phase decoding when the gating does not match, thereby minimizing the range gating amplitude peak when the gating does not match, and preventing false detection of the target.

[0020] Preferably, the optimization of phase encoding is achieved by using a genetic algorithm.

[0021] Preferably, the genetic algorithm uses the minimum autocorrelation sidelobe peak as a fitness function to optimize phase coding; the minimum autocorrelation sidelobe peak indicates that the error term is maximum when the gating is mismatched;

[0022] in, represents the pth group of phase coding designed by the genetic algorithm The autocorrelation function value at the i-th side lobe in is:

[0023]

[0024] in,(·) * represents conjugation, n is the serial number in the phase encoding, and N is the phase encoding length;

[0025] Then the autocorrelation sidelobe peak is:

[0026]

[0027] Preferably, in the step 2, the coherent accumulation using the RFT algorithm is as follows:

[0028]

[0029]

[0030] Among them, RFT(r,v) is the coherent accumulation result; (r,v) is the target motion parameter to be searched, r is the distance, v is the speed, and the target motion parameter to be searched is centered on the distance and speed estimates obtained by the DP algorithm; p is the sub-pulse number to be searched, M is the total number of sub-pulses, and T r is the PRT of the sub-pulse, H(p,r,v) is the RFT filter bank; f p is the sub-pulse frequency; c is the speed of light; s() is the long-range high-speed weak target echo model when the gate matching is shown in formula (II);

[0031] When the searched target motion parameters match the target's true motion parameters, the output value of RFT(r,v) reaches the maximum.

[0032] The present invention also provides a long-range, high-speed, and faint target detection device based on DP-RFT, comprising a range gating module, a motion parameter estimation and compensation module, and a one-dimensional range image synthesis module connected in sequence;

[0033] The range gating module is used to perform range gating filtering on the radar echo;

[0034] The motion parameter estimation and compensation module is used to estimate the target motion parameters using a dynamic programming (DP) algorithm. The DP algorithm parameter estimation results are used as prior information to solve the problem of long-term accumulation of blind velocity sidelobes in the Radon Fourier Transform (RFT) algorithm. With the target motion parameters obtained by the DP algorithm as the center, the RFT algorithm is used for coherent accumulation to obtain the target motion parameter estimation value and perform compensation.

[0035] The one-dimensional range image synthesis module is used to synthesize the target one-dimensional range image of the target echo after the target motion parameter is compensated.

[0036] Preferably, the device further includes a phase coding optimization module, which is used to determine the target misjudgment factors in the case of gating mismatch based on the construction and analysis of the long-range, high-speed and weak target echo model under range gating, and optimize the phase coding of the transmitted signal to maximize the error caused by phase decoding when the gating mismatch occurs, thereby minimizing the range gating amplitude peak when the gating mismatch occurs, and preventing misjudgment of the detected target.

[0037] Preferably, the long-range, high-speed, and weak target echo model s(t,m) under range gating constructed by the phase encoding optimization module is:

[0038]

[0039] Where t is the fast time, m is the sub-pulse sequence number, A r is the target complex amplitude, rect() represents the rectangular function, R m is the instantaneous radial distance of the target center at the mth sub-pulse, T p is the sub-pulse width, exp() represents the exponential function, f0 is the signal carrier frequency, Δf is the frequency hopping interval, d r (m) is the echo frequency sequence, c is the speed of light;

[0040] When the gate frequency is matched, the filter gate frequency and the echo frequency correspond one to one, then d(m) = d r (m), d(m) filter selection frequency sequence, phase encoding decoding error-free, the error term ε(t,m) is 0, the corresponding target echo after down-conversion to baseband is:

[0041]

[0042] When the gating is mismatched, there is a deviation between the filter gating frequency and the echo frequency, there is an error in the phase decoding, and ε(t,m) is not 0. The corresponding target echo after down-conversion to baseband is:

[0043]

[0044] Among them, the error term d′(m) is the offset echo frequency, d(m) is the gated frequency sequence, is the phase-coded sequence of the echo, is the phase coding sequence of the transmitted signal;

[0045] The existence of error terms affects the target motion parameter estimation and one-dimensional range image synthesis.

[0046] Preferably, the phase coding optimization module uses a genetic algorithm to optimize the phase coding of the transmission signal; the genetic algorithm uses the minimum autocorrelation sidelobe peak as the fitness function; the minimum autocorrelation sidelobe peak indicates that the error term is maximum when the selection is mismatched.

[0047] Preferably, the motion parameter estimation and compensation module uses the RFT algorithm to perform coherent accumulation as follows:

[0048]

[0049]

[0050] Among them, RFT(r,v) is the coherent accumulation result; (r,v) is the target motion parameter to be searched, r is the distance, v is the speed, and the target motion parameter to be searched is centered on the distance and speed estimates obtained by the DP algorithm; p is the sub-pulse number to be searched, M is the total number of sub-pulses, and T r is the PRT of the sub-pulse; H(p,r,v) is the RFT filter bank; f p is the sub-pulse frequency; c is the speed of light; s() is the long-range high-speed weak target echo model when the gate matching is shown in formula (II);

[0051] When the searched target motion parameters match the target's true motion parameters, the output value of RFT(r,v) reaches the maximum.

[0052] Beneficial effects:

[0053] (1) When estimating target motion parameters, the present invention adopts a Radon Fourier transform parameter estimation method based on DP optimization. Radon Fourier transform is a long-term accumulation algorithm with the largest target accumulation gain, but the use of this algorithm alone cannot avoid the problem of blind velocity sidelobes and cannot meet the problem of long-range, high-speed, weak, and small targets. Therefore, the present invention adopts DP to solve the problem of RFT. DP provides a priori. The DP algorithm has the characteristics of low signal-to-noise ratio, fast trajectory acquisition, and is not easily affected by velocity ambiguity. Therefore, the DP algorithm can avoid blind velocity sidelobes, while reducing the amount of computation and solving the velocity ambiguity problem; based on DP optimization, RFT solves the problem of high-precision parameter estimation. Moreover, both DP and RFT are applicable to scenarios with weak target echoes. Therefore, Radon Fourier transform parameter estimation based on DP optimization can estimate target motion parameters and synthesize a high-resolution one-dimensional range image of the target under range ambiguity, avoid blind velocity sidelobes, reduce the amount of computation, and thus achieve stable detection of long-range, high-speed, and weak targets. After verification, the speed estimation accuracy of the maneuvering target detection algorithm based on DP-RFT can be reduced to below 0.5m / s, the accuracy is improved by 2 times, and the distance error accuracy is reduced to 10 -4 The accuracy is increased by nearly 2 times.

[0054] (2) The present invention also constructs a long-range, high-speed, and weak target echo model under range gating, and analyzes and verifies the error situation of gating mismatch. Based on the error factor, the present invention considers from the perspective of phase decoding and designs to maximize the error caused by phase decoding when gating mismatch occurs, thereby minimizing the range gating amplitude peak when gating mismatch occurs, reducing or eliminating the misjudgment of the target detection, and thus achieving optimization of phase coding. By adopting an optimized phase-coded transmission signal, it is possible to reduce or avoid the misjudgment of the target detection under gating mismatch conditions, further improving the stability of long-range, high-speed, and weak target detection.

[0055] (3) In a preferred embodiment, a genetic algorithm is used to optimize phase encoding, and autocorrelation is selected as an indicator to measure the performance of phase encoding, so as to quickly determine the optimized phase encoding. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the long-range, high-speed, and faint target detection method based on DP-RFT of the present invention;

[0057] Figure 2 This is a schematic diagram of distance gating;

[0058] Figure 3 The target motion parameter estimation error histograms: (a) is the distance error histogram of the DP algorithm; (b) is the velocity error histogram of the DP algorithm; (c) is the distance error histogram of the DP-RFT algorithm; (d) is the velocity error histogram of the DP-RFT algorithm;

[0059] Figure 4 Comparison of the one-dimensional high-resolution range image of the target of range gating of the present invention: (a) is the range gating statistical histogram; (b) is the single range gating comparison diagram;

[0060] Figure 5 Schematic diagram of the long-range, high-speed, and faint target detection device based on DP-RFT of the present invention. DETAILED DESCRIPTION

[0061] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0062] The present invention provides a long-range, high-speed, and faint target detection method based on DP-RFT. The basic idea is to use DP-optimized Radon Fourier transform to perform coherent accumulation to obtain target motion parameters, and perform high-resolution one-dimensional range image synthesis on the target echo after motion parameter compensation. This scheme can estimate the target motion parameters and synthesize the target high-resolution one-dimensional range image under range ambiguity, avoid blind speed sidelobes, reduce the amount of calculation, and thus achieve stable detection of long-range, high-speed, and faint targets; the present invention also establishes a long-range, high-speed, and faint target echo model under range gating to verify the impact of errors on target detection under both gating matching and gating mismatching conditions, and optimizes the design of the phase encoding of the transmitted signal to maximize the error caused by phase decoding when the gating is mismatched, thereby minimizing the range gating amplitude peak when the gating is mismatched, reducing or eliminating the misjudgment of the detected target, and further improving the stability of long-range, high-speed, and faint target detection.

[0063] like Figure 1 As shown, the long-range, high-speed, and faint target detection method based on DP-RFT of the present invention comprises the following steps:

[0064] Step 1: Establish a long-range, high-speed, and weak target echo model under range gating.

[0065] The radar transmits a frequency-stepped signal composed of M sub-pulses, and a multi-channel receiver simultaneously down-converts and receives the M sub-pulses at their carrier frequencies. Each channel in the multi-channel receiver can only match and resolve target echoes corresponding to the carrier frequency. The filter only generates an output if a target is within the assumed range; if no target is within the assumed range, the filter generates no output.

[0066] The expression of the stepped frequency simple pulse transmission signal s(t,m) is as follows:

[0067]

[0068] Among them, rect() represents the rectangular function, and the sub-pulse width is T p , t is the fast time, exp() represents the exponential function, f0 is the signal carrier frequency, m is the sub-pulse sequence, M is the total number of sub-pulses, Δf is the frequency hopping interval, d s (m) represents the frequency sequence of the transmitted signal, more precisely the frequency of the mth sub-pulse, represents the phase coding sequence of the transmitted signal, more precisely, the phase coding corresponding to the mth sub-pulse.

[0069] Considering a single target scene, assuming that the motion parameters of each order of the target center are α M =[α0,α1,α2,…α K-1] represents, where α0, α1, α2 represent the target initial radial distance R0, initial velocity v0, initial acceleration a0, and so on. K represents the highest order of target motion. At the mth sub-pulse, the instantaneous radial distance R m It can be expressed as:

[0070]

[0071] Among them, T r is the PRT (Pulse Repetition Time) of the sub-pulse.

[0072] Substituting equation (2) into equation (1), and taking into account the delay of the transmitted signal when the target echoes, the target echo expression can be obtained:

[0073]

[0074] In the above formula, c is the speed of light.

[0075] Based on Equation (3), range gating filtering is performed. Range gating involves filtering the received signal at the receiving end using a filter at the corresponding frequency hopping point. The filter outputs only when a target exists within the range matched by the gating. If no target exists within the range matched by the gating, the filter outputs nothing.

[0076] The echo expression after range gating is as follows:

[0077]

[0078] Among them, A r is the target complex amplitude, d r (m) is the echo frequency sequence, and ε(t,m) is the error term.

[0079] When the gate is matched, the filter gate frequency and the echo frequency correspond one to one, that is, d r (m)d=m(), d(m) is the filter selection frequency sequence; phase encoding and decoding are error-free, the error term ε(t,m) is 0, and the corresponding target echo after down-conversion to baseband is:

[0080]

[0081] When the gating is mismatched, there is a deviation between the filter gating frequency and the echo frequency, there is an error in the phase decoding, and ε(t,m) is not 0. The echo expression is as follows:

[0082]

[0083] in, is the error term, d′(m) is the misplaced echo frequency, d(m) is the gated frequency sequence, is the phase-encoded sequence of the echo; It is the phase coding sequence of the transmitted signal and also the phase coding sequence of the pass filter.

[0084] In order to maximize the error caused by phase decoding when the gate does not match, the peak value of the range gate amplitude is minimized when the gate does not match (such as Figure 4 The peak value of the red signal in Figure (b) is the smallest. To avoid misjudgment of the target, phase encoding needs to be optimized. For a set of phase encoding sets, its autocorrelation and cross-correlation are important indicators for measuring performance. The calculation method is as follows:

[0085]

[0086]

[0087] in, Indicates the pth group code The autocorrelation function at the i-th side lobe in , i = 0 represents the autocorrelation main lobe, i ≠ 0 represents the autocorrelation side lobe; represents the cross-correlation function between the pth group code and the qth group code. n is the sequence number in the phase encoding, and N is the phase encoding length.

[0088] ASP represents the autocorrelation sidelobe peak, and CP represents the cross-correlation peak, that is:

[0089]

[0090] Among them, max means taking the maximum value.

[0091] The present invention adopts an inter-pulse orthogonal phase coding design algorithm based on a genetic algorithm, which minimizes the autocorrelation sidelobe peak value I s As a fitness function, the minimum autocorrelation sidelobe peak indicates the maximum error term when the gate mismatch occurs, and its expression is as follows:

[0092]

[0093] in, represents the phase encoding designed by the algorithm, Represents the autocorrelation function of the group of codes.

[0094] The optimized phase coding is used to adjust the transmission signal so that after the echo is range-gated, in the case of gating mismatch, an excessively large range gating amplitude peak is not generated, thus avoiding false target detection.

[0095] Step 2: Perform range gating filtering on the radar echo.

[0096] Step 3: Target motion parameter estimation and compensation are performed based on Radon Fourier transform (RFT) optimized by dynamic programming (DP).

[0097] High-speed targets move quickly, and the distance variation between step-rate pulses cannot be ignored, so motion parameter estimation and compensation are necessary. However, the signal-to-noise ratio of target echoes in long-range detection environments is low, requiring long-term pulse accumulation to improve processing gain. Furthermore, the high speed of targets makes velocity ambiguity a problem.

[0098] There are many traditional long-term target accumulation algorithms. Among them, the Radon Fourier Transform (RFT) algorithm achieves long-term coherent target accumulation and motion parameter estimation by searching in two dimensions: distance and velocity. Its theoretical accumulation gain is optimal. However, for targets with weak echo motion and high-speed motion, using RFT for target motion parameter estimation presents problems such as blind velocity sidelobes and high computational complexity.

[0099] Therefore, the present invention improves RFT and forms a Radon Fourier Transform (RFT) parameter estimation method based on dynamic programming (DP) optimization, which is called DP-RFT algorithm. The DP algorithm preferably adopts DP-TBD. First, the DP-TBD algorithm is used to quickly obtain the low signal-to-noise ratio target trajectory, thereby avoiding the influence of the speed ambiguity problem on the accumulation algorithm; then, a rough estimate of the target motion parameters is made based on the target motion trajectory obtained by DP-TBD, and the rough estimate is used as the prior information of the RFT algorithm. By reasonably setting the parameter search range, the RFT calculation amount is reduced while solving the RFT blind speed sidelobe problem; finally, the high accumulation gain and high search accuracy of the RFT algorithm are used to accurately estimate the target motion parameters.

[0100] The present invention adopts the pre-placed DP algorithm. The purpose is not to simply add one search, but to use the DP algorithm to help the RFT algorithm avoid blind speed sidelobes, while reducing the amount of calculation and solving the speed ambiguity problem.

[0101] Specifically, assuming that the nth distance sampling unit of the kth PRT is The corresponding value function is The observed value is The state transition range in the k-1th PRT corresponding to the distance sampling unit is The state transition matrix is State transfer range The state information is used to predict the possible states of the target. The state transition matrix records the state correspondence between adjacent PRTs. The DP algorithm process is as follows:

[0102] Initialize the value function and state transfer matrix as follows:

[0103]

[0104]

[0105] in, is the value function of the nth distance sampling unit of the first pulse; is the observation value corresponding to the nth range sampling unit of the first pulse;

[0106] After initialization is completed, the recursive accumulation value function and the update state transfer matrix are started as follows:

[0107]

[0108]

[0109] After completing the accumulation of K PRTs, the accumulated and the detection threshold V T Compare and find all states greater than the threshold, recorded as

[0110]

[0111] By backtracking the state transfer matrix, the complete track can be detected Dynamic programming can quickly obtain the status of each stage of potential targets, and By performing the least squares estimation, a rough estimate of the target's initial radial distance and velocity can be obtained.

[0112] Target motion parameters obtained by DP algorithm As the center, the parameter estimation algorithm based on RFT is used to further improve the parameter estimation accuracy. According to the phase form of (5), RFT is used to achieve coherent accumulation. The specific form is as follows:

[0113]

[0114] Among them, (r, v) is the search motion parameter, r is the distance, v is the speed, p is the sub-pulse number to be searched; H(p, r, v) is the RFT filter bank, f p=(f0+d(p)Δf) is the sub-pulse frequency. By judging the accumulated peak value of the two-dimensional function RFT(r,v), if the search parameters match the target's true motion parameters, the output value of RFT reaches the maximum and a peak is generated on the two-dimensional plane, such as Figure 4 The peak shown in Figure (b) can be used to estimate the parameters of the target echo signal.

[0115] Step 4: synthesize the target high-resolution one-dimensional range image of the target echo after motion parameter compensation.

[0116] By determining within which assumed range the target can obtain a high-resolution range profile (HRRP), the target's true range ambiguity can be determined, thereby enabling detection of long-range, high-speed targets. After target motion parameter compensation, its echo expression is as follows:

[0117]

[0118] Based on formula (17), a high-resolution one-dimensional range image of the target can be synthesized. Only when the gating is matched and the motion parameters are estimated accurately can a high-resolution one-dimensional range image of the target be obtained.

[0119] In order to verify the target parameter estimation accuracy and range gating performance of the DP-RFT-based long-range, high-speed, and faint target detection method described above, a simulation experiment was conducted. The radar system parameters and target motion parameters are as follows:

[0120] Table 1 Radar system parameters and target motion parameters

[0121] Parameter properties Parameter value Carrier center frequency 10GHz Frequency hopping points 128 Frequency hopping interval 20MHz Pulse repetition time (PRT) 50us Sub-pulse width 50ns Distance sampling unit width 0.9375m Target initial slant range 460km speed 1500m / s

[0122] Based on the system parameters, the single pulse echo signal-to-noise ratio can be calculated by the radar equation. The radar equation parameters are as follows:

[0123] Table 2 Radar equation parameters

[0124] Parameter properties Parameter value <![CDATA[Peak power P t > 1GW RCSσ <![CDATA[0.1m 2 ]]> <![CDATA[Transmit antenna gain G t > 48.19dB <![CDATA[Received antenna gain G r > 48.19dB <![CDATA[System noise temperature T s > 627.06K Sub-pulse width B 50ns Wavelength λ 3cm <![CDATA[System loss L s > 10dB Boltzmann constant k 1.38*10^(-23)

[0125] The radar equation is:

[0126]

[0127] Among them, R max is the maximum detection distance.

[0128] From the radar equation, we know that the SNR of a single pulse corresponding to a target at 460 km is 4.05 dB, so the echo signal-to-noise ratio is set to 4 dB.

[0129] On this basis, the performance of the proposed DP-RFT-based maneuvering target detection algorithm is verified from the aspects of parameter estimation accuracy and precision, and compared with the traditional DP-TBD.

[0130] The error calculation method is as follows:

[0131]

[0132] Among them, ΔR and Δv represent the distance error and speed error respectively, R and v represent the estimated distance and speed respectively, the superscript i represents the result of the i-th experiment, R real With v real Represents the true value of distance and true value of speed.

[0133] Assume N is the number of Monte Carlo simulations, and calculate the statistical mean and statistical error according to the following formula:

[0134]

[0135] Among them, x mean represents the statistical mean, x std represents the sample mean square error of the statistic. x represents the data sample, and x represents the mean of the data sample. The statistical results are as follows:

[0136] Table 3 Statistical results of target motion parameter estimation errors

[0137] Parameter properties DP-TBD algorithm DP-RFT algorithm of the present invention Mean speed error 16.7574m / s 0.4776m / s velocity error mean square error 243.7526m / s 100.7277m / s Mean distance error 0.5363m <![CDATA[-6*10 -4 m]]> distance error mean square error 0.8094m 0.4273m

[0138] like Figure 3 As shown in the figure, after 1000 Monte Carlo simulations, the statistical histograms of the distance error and speed error of the two methods can be obtained. It can be seen that compared with the traditional DP-based detection-before-tracking algorithm, the speed estimation accuracy of the maneuvering target detection algorithm based on DP-RFT of the present invention can be reduced to below 0.5m / s, the accuracy is improved by 2 times, and the distance error accuracy is reduced to 10 -4 The accuracy is increased by nearly 2 times.

[0139] Subsequently, the range gating capability was verified, using the ratio of the peak power of the next-dimensional range image between the gated match and gated non-match conditions. "Gate match" refers to the presence of the target within the gated range segment, while "gate non-match" refers to the absence of the target within the gated range segment.

[0140] Assume that the peak power of the one-dimensional range image of the gated matching target is P m , the peak power of the one-dimensional range image of the non-matching target is P n , and the ratio of the two is used as a measure of range gating capability, as shown below:

[0141]

[0142] Among them, Q mf It is a measure of the range gating capability, indicating the magnitude of the peak power drop when gating a mismatched target compared to a gating a matched target. mf The smaller the value, the stronger the range gating capability.

[0143] like Figure 4 As shown, after 1000 Monte Carlo simulations, the range gating capability Q can be obtained. mf The statistical histogram of . The average value of the statistical results is taken as the final result, as shown below:

[0144]

[0145] Among them, mean means finding the mean.

[0146] The result of formula (22) is -12.9017dB. Figure 4 It can be seen that compared with the gated matched target, the peak power of the one-dimensional range image of the gated non-matching target decreases by an average of 13 dB, which can distinguish the gated matched target from the gated non-matching target. Therefore, this method has the ability of range gating and can achieve stable detection of long-range, high-speed and weak targets.

[0147] Based on the above method, the present invention also provides a long-range, high-speed, and faint target detection device based on DP-RFT, such as Figure 5 As shown, it includes a range gating module, a motion parameter estimation and compensation module and a one-dimensional range image synthesis module which are connected in sequence.

[0148] Range gating module, used to perform range gating filtering on radar echoes;

[0149] The motion parameter estimation and compensation module is used to estimate the target motion parameters using the dynamic programming (DP) algorithm. The DP parameter estimation results are used as the prior information for the Radon Fourier transform (RFT) algorithm to solve the blind velocity sidelobe problem accumulated over a long period of time by the RFT algorithm. With the target motion parameters obtained by the DP algorithm as the center, the RFT algorithm is used for coherent accumulation to obtain the target motion parameter estimation value for compensation. The RFT algorithm is shown in the above formula (16).

[0150] The one-dimensional range image synthesis module is used to synthesize the target high-resolution one-dimensional range image of the target echo after motion parameter compensation.

[0151] The device also includes a phase encoding optimization module. Based on the construction and analysis of the long-range, high-speed, and weak target echo model under range gating, it determines the factors that may cause target misjudgment in the case of a gating mismatch and optimizes the phase encoding of the transmitted signal to maximize the error caused by phase decoding when the gating mismatch occurs. This minimizes the peak value of the range gating amplitude when the gating mismatch occurs, thus preventing misjudgment of the target. The long-range, high-speed, and weak target echo model s(t,m) under range gating constructed by the phase encoding optimization module is shown in formulas (4)-(6) above.

[0152] Among them, the phase decoding optimization can adopt a genetic algorithm; the genetic algorithm takes the minimum autocorrelation sidelobe peak as the fitness function; the minimum autocorrelation sidelobe peak indicates that the error term is the largest when the gating is mismatched.

[0153] The above specific embodiments merely illustrate the design principles of the present invention. The shapes and names of the components described herein may vary and are not limiting. Therefore, those skilled in the art may modify or substitute equivalents for the technical solutions described in the above embodiments. Such modifications and substitutions, without departing from the inventive spirit and technical solutions of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A long-range, high-speed, and faint target detection method based on DP-RFT, characterized in that: The following steps are involved: Step 1: Perform range gating filtering on the radar echo; Step 2: Motion parameter estimation and compensation: The dynamic programming (DP) algorithm is used to estimate the target motion parameters. The DP algorithm parameter estimation results serve as prior information to solve the problem of long-term accumulation of blind velocity sidelobes in the Radon Fourier transform (RFT) algorithm. With the target motion parameters obtained by the DP algorithm as the center, the RFT algorithm is used for coherent accumulation to obtain the target motion parameter estimation value and perform compensation. Step 3: synthesize the target one-dimensional range image of the target echo after the target motion parameter compensation.

2. The long-range, high-speed, faint target detection method based on DP-RFT as claimed in claim 1, characterized in that: The method further includes: constructing a long-range, high-speed, weak target echo model s(t,m) under range gating as: Where t is the fast time, m is the sub-pulse sequence number, A r is the target complex amplitude, rect() represents the rectangular function, R m is the instantaneous radial distance of the target center at the mth sub-pulse, T p is the sub-pulse width, exp() represents the exponential function, f0 is the signal carrier frequency, Δf is the frequency hopping interval, d r (m) is the echo frequency sequence, c is the speed of light; When the gate frequency is matched, the filter gate frequency and the echo frequency correspond one to one, then d(m) = d r (m), d(m) filter selection frequency sequence, phase encoding decoding error-free, the error term ε(t,m) is 0, the corresponding target echo after down-conversion to baseband is: When the gating is mismatched, there is a deviation between the filter gating frequency and the echo frequency, there is an error in the phase decoding, and ε(t,m) is not 0. The corresponding target echo after down-conversion to baseband is: Among them, the error term d′(m) is the offset echo frequency, d(m) is the gated frequency sequence, is the phase-coded sequence of the echo, is the phase coding sequence of the transmitted signal; The existence of error terms affects the estimation of target motion parameters and the synthesis of one-dimensional range images; The phase encoding of the transmitted signal is optimized to maximize the error caused by phase decoding when the gating does not match, thereby minimizing the range gating amplitude peak when the gating does not match, and preventing misjudgment of the target detection.

3. The long-range, high-speed, faint target detection method based on DP-RFT as claimed in claim 2, characterized in that: The optimization of phase encoding is achieved using genetic algorithm.

4. The long-range, high-speed, faint target detection method based on DP-RFT as claimed in claim 3, characterized in that: The genetic algorithm uses the minimum autocorrelation sidelobe peak as a fitness function to optimize phase coding; the minimum autocorrelation sidelobe peak indicates that the error term is maximum when the gating is mismatched; in, represents the pth group of phase coding designed by the genetic algorithm The autocorrelation function value at the i-th side lobe in is: in,(·) * represents conjugation, n is the serial number in the phase encoding, and N is the phase encoding length; Then the autocorrelation sidelobe peak is:

5. The method for detecting long-range, high-speed, and faint targets based on DP-RFT as claimed in claim 2, wherein: In the step 2, the coherent accumulation using the RFT algorithm is as follows: Among them, RFT(r,v) is the coherent accumulation result; (r,v) is the target motion parameter to be searched, r is the distance, v is the speed, and the target motion parameter to be searched is centered on the distance and speed estimates obtained by the DP algorithm; p is the sub-pulse number to be searched, M is the total number of sub-pulses, and T r is the PRT of the sub-pulse, H(p,r,v) is the RFT filter bank; f p is the sub-pulse frequency; c is the speed of light; s() is the long-range high-speed weak target echo model when the gate matching is shown in formula (II); When the searched target motion parameters match the target's true motion parameters, the output value of RFT(r,v) reaches the maximum.

6. A long-range, high-speed, and faint target detection device based on DP-RFT, characterized in that: It includes a range gating module, a motion parameter estimation and compensation module, and a one-dimensional range image synthesis module which are connected in sequence; The range gating module is used to perform range gating filtering on the radar echo; The motion parameter estimation and compensation module is used to estimate the target motion parameters using a dynamic programming (DP) algorithm. The DP algorithm parameter estimation results are used as prior information to solve the problem of long-term accumulation of blind velocity sidelobes in the Radon Fourier transform (RFT) algorithm. With the target motion parameters obtained by the DP algorithm as the center, the RFT algorithm is used for coherent accumulation to obtain the target motion parameter estimation value and perform compensation. The one-dimensional range image synthesis module is used to synthesize the target one-dimensional range image of the target echo after the target motion parameter is compensated.

7. The DP-RFT-based long-range, high-speed, faint target detection device according to claim 6, characterized in that: The device further includes a phase coding optimization module for determining the target misjudgment factors in the case of gating mismatch based on the construction and analysis of the long-distance, high-speed and weak target echo model under range gating, and optimizing the phase coding of the transmitted signal to maximize the error caused by phase decoding in the case of gating mismatch, thereby minimizing the range gating amplitude peak in the case of gating mismatch and preventing misjudgment of the detected target.

8. The DP-RFT-based long-range, high-speed, faint target detection device according to claim 7, characterized in that: The long-range, high-speed, and weak target echo model s(t,m) under range gating constructed by the phase encoding optimization module is: Where t is the fast time, m is the sub-pulse sequence number, A r is the target complex amplitude, rect() represents the rectangular function, R m is the instantaneous radial distance of the target center at the mth sub-pulse, T p is the sub-pulse width, exp() represents the exponential function, f0 is the signal carrier frequency, Δf is the frequency hopping interval, d r (m) is the echo frequency sequence, c is the speed of light; When the gate frequency is matched, the filter gate frequency and the echo frequency correspond one to one, then d(m) = d r (m), d(m) filter selection frequency sequence, phase encoding decoding error-free, the error term ε(t,m) is 0, the corresponding target echo after down-conversion to baseband is: When the gating is mismatched, there is a deviation between the filter gating frequency and the echo frequency, there is an error in the phase decoding, and ε(t,m) is not 0. The corresponding target echo after down-conversion to baseband is: Among them, the error term d′(m) is the offset echo frequency, d(m) is the gated frequency sequence, is the phase-coded sequence of the echo, is the phase coding sequence of the transmitted signal; The existence of error terms affects the target motion parameter estimation and one-dimensional range image synthesis.

9. The DP-RFT-based long-range, high-speed, faint target detection device according to claim 6, wherein: The phase coding optimization module uses a genetic algorithm to optimize the phase coding of the transmission signal; the genetic algorithm uses the minimum autocorrelation sidelobe peak as the fitness function; the minimum autocorrelation sidelobe peak indicates that the error term is maximum when the gating is mismatched.

10. The DP-RFT-based long-range, high-speed, faint target detection device according to claim 6, characterized in that: The motion parameter estimation and compensation module uses the RFT algorithm to perform coherent accumulation as follows: Among them, RFT(r,v) is the coherent accumulation result; (r,v) is the target motion parameter to be searched, r is the distance, v is the speed, and the target motion parameter to be searched is centered on the distance and speed estimates obtained by the DP algorithm; p is the sub-pulse number to be searched, M is the total number of sub-pulses, and T r is the PRT of the sub-pulse; H(p,r,v) is the RFT filter bank; f p is the sub-pulse frequency; c is the speed of light; s() is the long-range high-speed weak target echo model when the gate matching is shown in formula (II); When the searched target motion parameters match the target's true motion parameters, the output value of RFT(r,v) reaches the maximum.

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