Accumulation detection method for high-speed targets with range ambiguity based on multi-frequency joint processing

Abstract

The present invention discloses a method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing, which is applied to the field of radar signal processing. In order to solve the problem of poor accumulation performance in the case of range ambiguity in the prior art, the present invention first constructs a radial motion model of the target relative to the radar system under range ambiguity, and establishes a multi-frequency radar high-speed target echo model with range ambiguity. Then, the MRFT algorithm is used to correct the range movement of the ambiguous echo signal to achieve intra-segment coherent accumulation of the signal. Subsequently, based on the intra-segment accumulation peak position transfer relationship, a three-dimensional joint search is performed through the range-speed-range ambiguity, the echo envelopes are aligned, and the phase difference is compensated to achieve coherent accumulation of echoes between multiple segments and target parameter estimation. By means of the joint accumulation processing between segments, the radar accumulation performance can be significantly improved, and the detection power of the system can be enhanced.

Description

Technical Field

[0001] The invention belongs to the field of radar signal processing, and in particular relates to a target accumulation detection technology. Background Art

[0002] With the rapid development of science and technology and the extensive research on stealth materials, a number of high-speed targets with long combat ranges, high flight speeds, and low observability have emerged. Therefore, how to improve radar's detection capabilities for high-speed targets has become a hot topic in the radar field.

[0003] To improve the signal-to-noise ratio (SNR) of echo signals, multi-pulse signal accumulation is often used to focus the target signal energy. However, multi-pulse accumulation of high-speed target echo signals often faces the problem of range walk (RW), resulting in a loss of accumulation performance. Furthermore, in practical applications, high-speed target detection often uses high pulse repetition frequency (HPRF) transmission signals to improve the detection performance of radar systems. However, as the pulse repetition frequency increases, when the target echo delay exceeds the pulse repetition frequency, the target range ambiguity (RA) problem will inevitably occur. Therefore, the conflicting relationship between long-term coherent integration and the range walk and range ambiguity of high-speed target echo signals is a scientific problem that needs to be urgently resolved.

[0004] Scholars at home and abroad have conducted research on high-speed target echo accumulation detection algorithms and range ambiguity resolution algorithms.

[0005] To address the range movement problem of high-speed targets, Xu et al. proposed the Radon-Fourier transform (RFT), which achieves coherent accumulation of echoes by searching radial distance and radial velocity. Xu et al. proposed the MTD-GRT (Moving target detection-generalized Radon transform, MTD-GRT) method, which divides the longer integration time into several sub-apertures and performs coherent accumulation on each sub-aperture through a Doppler filter group. Then, the generalized Radon transform is used to compensate for the high-order RM and DM motion of the echo to achieve signal accumulation between each sub-aperture. Li et al. proposed an improved RFT (Modified Radon Fourier transform) accumulation. For target signals in multiple frames, the traditional RFT is used within the frame, and the coherent accumulation between multiple frame signals is achieved by designing matching criteria between signal segments. However, when range ambiguity occurs, the detection and estimation performance of the above algorithms will drop sharply.

[0006] To address the range ambiguity problem, Li et al. proposed combining the Chinese remainder theorem with traditional detection methods using multi-frequency signals. Through two-stage data fusion, they proposed a joint disambiguation and estimation algorithm based on covariance combination fusion to resolve the range-Doppler ambiguity and ultimately perform target detection on the accumulated results. Wei et al. used multiple pulse repetition frequencies to coherently accumulate echoes in the same beam, and used the multi-model elliptic transform (MM-ERT) between beams to map ambiguity measurements using the ambiguity interval multiple hypothesis method, thereby achieving long-term non-coherent accumulation under range ambiguity. However, the above algorithms did not consider the RM problem existing in the echo signals of high-speed targets and failed to achieve effective coherent accumulation.

[0007] In addition to the above-mentioned accumulation methods, other scholars have also conducted some related research. However, it should be pointed out that these current accumulation methods all have the problem of insufficient accumulation performance when the target distance is blurred, resulting in a large performance loss. Summary of the Invention

[0008] To solve the above technical problems, the present invention proposes a range-ambiguous high-speed target accumulation detection method based on multi-frequency joint processing, which significantly improves the detection performance of radar through intra-segment accumulation and inter-segment accumulation of signals.

[0009] The technical solution adopted by the present invention is: a range fuzzy high-speed target accumulation detection method based on multi-frequency joint processing, comprising:

[0010] S1. The radar uses multiple sets of repetitive linear frequency modulation signals as the transmission signal;

[0011] S2. Considering the ranging ambiguity corresponding to each group of repetitive linear frequency modulation signals, the radar echo signal obtained is subjected to down-conversion processing and pulse compression processing in sequence to obtain a pulse compression echo signal;

[0012] S3. Determine a search interval and a search step size of the intra-segment search parameter, obtain an intra-segment search parameter combination according to the search interval and the search step size of the intra-segment search parameter, and determine an extracted intra-segment search trajectory based on the intra-segment search parameter combination;

[0013] S4, extracting the pulse pressure echo signal in step S2 according to the intra-segment search trajectory and performing intra-segment coherent accumulation;

[0014] S5. Determine a search interval and a search step size of the inter-segment search parameter, obtain an inter-segment search parameter combination according to the search interval and the search step size of the inter-segment search parameter, and determine an extracted inter-segment search trajectory based on the inter-segment search parameter combination;

[0015] S6. Extract the echo signal after intra-segment coherent accumulation in step S4 according to the inter-segment search trajectory and perform inter-segment coherent accumulation;

[0016] S7, performing constant false alarm target detection according to the inter-segment coherent accumulation result obtained in step S6;

[0017] S8. Estimate the motion parameters of the target based on the peak position corresponding to the target detection result of step S7.

[0018] The beneficial effects of the present invention are as follows: The present invention discloses an accumulation detection and estimation algorithm for range-ambiguous high-speed targets based on multi-frequency joint processing. The algorithm studies the coherent accumulation processing method for the echo signals of range-ambiguous high-speed targets using a multi-frequency radar. First, a radial motion model of the range-ambiguous high-speed target relative to the radar system is established. Based on this model, a received signal model of the range-ambiguous high-speed target under the multi-frequency radar is constructed. Next, for echo signals under the same PRF, MRFT is used to perform intra-segment coherent accumulation of the signal to correct for range shifts in the echo. Subsequently, based on the inter-segment peak transfer relationship of each signal segment, a three-dimensional joint search of range, speed, and range ambiguity is performed to align the echo envelopes and compensate for phase differences between signals, thereby achieving inter-segment coherent accumulation of the signal. This significantly improves the output signal-to-noise ratio and enables accurate estimation of the target's true motion parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of an embodiment of the present invention;

[0020] Figure 2 This is the result graph of distance walking after pulse pressure;

[0021] Figure 3 This is the coherent accumulation result of the proposed algorithm on the range-range ambiguity plane when the signal-to-noise ratio is -15dB;

[0022] Figure 4 This is the coherent accumulation result of the proposed algorithm in the range-velocity plane when the signal-to-noise ratio is -15dB;

[0023] Figure 5 This is the coherent accumulation result of the improved RFT algorithm when the signal-to-noise ratio is -15dB;

[0024] Figure 6 This is the coherent accumulation result of the RFT algorithm when the signal-to-noise ratio is -15dB;

[0025] Figure 7 This is the coherent accumulation result of the MTD-GRT algorithm when the signal-to-noise ratio is -15dB;

[0026] Figure 8 This is the coherent accumulation result of the MTD algorithm when the signal-to-noise ratio is -15dB;

[0027] Figure 9 Detection performance curves of different accumulation algorithms. DETAILED DESCRIPTION

[0028] To facilitate those skilled in the art to understand the technical content of the present invention, the present invention is further explained below with reference to the accompanying drawings.

[0029] The present invention is verified by Matlab simulation experiment method, and the correctness and effectiveness of the present invention are verified on the scientific computing software Matlab R2021b. The embodiments of the present invention are further described below with reference to the accompanying drawings.

[0030] See also Figure 1 The present invention proposes an algorithm for detecting and estimating high-speed targets with range ambiguity based on multi-frequency joint processing, which is specifically implemented through the following process:

[0031] Step 1: The radar uses a multi-frequency linear frequency modulation signal as the transmitting signal s n (t,t p,n ), assuming that the pulse repetition period of the nth segment signal can be expressed as T n , n=1,…,N, then the nth segment transmission signal can be expressed as

[0032]

[0033] in, t is fast time, t p,n =pT n , p=0,1,…,N p -1 is the slow time of the nth group of repetition signals. p is the pulse duration, μ=B / T p is the frequency modulation slope, B is the signal bandwidth, f c is the radar carrier frequency.

[0034] The radar parameters used in this example are set as follows: carrier frequency f c =3GHz, bandwidth B = 30MHz, sampling frequency f s =60MHz, pulse repetition frequency 1 is f r1 =20kHz, pulse repetition frequency 2 is f r2 =30kHz, pulse repetition frequency 3 is f r3 =50kHz, each pulse repetition frequency corresponds to a "segment" of the multi-frequency signal, and the maximum unambiguous distance of each segment of the corresponding signal is Pulse duration T p =5μs, each segment of the multi-frequency signal contains 1000 pulses. The total number of accumulated pulses of the entire multi-frequency signal is N p= 3000. In practical applications, the appropriate transmission signal pulse repetition frequency can be selected according to the system's requirements for the maximum unambiguous distance and maximum unambiguous speed.

[0035] The signal-to-noise ratio after pulse compression is SNR = -15dB. The target parameters are set as follows: the initial target distance is r 0,1 =13km, radial velocity v 0，1 = 2700m / s. Assume that the radial distance and speed of the target relative to the radar at the beginning of the nth segment signal are r 0,n 、v 0,n , then at t p,n At time , the instantaneous radial distance of the target can be expressed as

[0036] R(t p,n )=r 0,n +v 0,n t p,n

[0037] At the same time, considering the ranging ambiguity, the instantaneous distance of the target can also be expressed as

[0038] R(t p,n )=R A (t p,n )+m p,n R max,n

[0039] Among them, R A (tp,n) is the fuzzy distance of R(tp,n), m p,n The maximum ambiguity distance of the nth segment signal is

[0040] After down-conversion processing of the radar echo signal, the radar baseband echo signal can be obtained as

[0041]

[0042] Among them, A r represents the complex phase of the echo signal, c is the speed of light, λ=cf c is the wavelength of the radar, χ(t p,n )=2R A (t p,n ) / c is the echo delay.

[0043] Step 2: For the echo signal s r (t,t p,n ) to perform pulse compression processing to obtain the pulse compression echo signal s pc,n (t,t p,n )

[0044]

[0045] Among them, A pc is the complex amplitude of the signal after pulse compression, and B is the signal bandwidth.

[0046] Figure 2 This is a schematic diagram of the pulse compression echo signal in a noise-free scenario. It can be seen that the range ambiguity causes the target echo trajectory to fold.

[0047] Step 3: Determine the search interval and search step size of the search parameters within the segment. Based on the prior information such as the radar detection area and the speed range of high-speed targets, determine the distance search interval [0, R max,n ] and speed search interval [-v max ,v max ], where v max is the maximum possible target speed. Based on the radar system parameters, the search steps of the initial radial distance and radial speed are determined as △r=c2f s , △v=λ2T, where is the total accumulation time. From this, we can get that the number of search units of the distance is N r,n =ceil(R max,n △r), the number of speed searches is N v =ceil((2v max )△v), where ceil(·) represents the rounding up operation.

[0048] Step 4: According to the search parameter combination (r(l n ),v(q n )) Determine the search trajectory within the extracted segment, that is, calculate the distance addressing variable ζ within the segment n (r(l n ),v(q n )):

[0049] ζ n (r(l n ),v(q n ))=mod(r(l n )+v(q n )t p,n ,R max,n )

[0050] The range of values ​​of each search parameter is as follows: distance search variable r(l n )∈[0,R max,n ],l=0,…,N r,n -1, speed search variable v(q n )∈[-v max ,v max ],q n=0,…,N v -1. The value rules of each search parameter are as follows: n Indicates the corresponding position of the searched radial distance in the searched radial distance sequence, and the searched radial distance is n The relationship is r(l n )=l n △r,l n =0,1,…,N r,n -1,q n Indicates the corresponding position of the searched radial velocity in the searched radial velocity sequence, and the searched radial velocity is related to q n The relationship is v(q n )=-v max +q n △v,q n =0,1,...,N v ;

[0051] Step 5: The echo signal s extracted in step 4 is pc,n (ζ n (r(l n ),v(q n )),t p,n ), through the phase compensation function H(v(q n ),t p,n ) compensates for the phase shift of the echo and accumulates the phase-aligned signal. The expression of the phase compensation function is

[0052]

[0053] When the search distance and speed are r(l n )、v(q n ), the accumulated result of the echo can be expressed as

[0054]

[0055] Among them, A n =A pc T n is the accumulated amplitude of the nth segment, is the distance resolution, is the fuzzy distance of the target under the nth repetition frequency, and v0 is the real moving speed of the target.

[0056] When satisfied And v(q n )=v0, that is, the searched motion parameters match the target fuzzy motion parameters, and the position of the accumulation plane can be The cumulative peak results within the segment are obtained above.

[0057]

[0058] Step 6, traverse all search parameter combinations (r(l n ),v(q n )), we can get the intra-segment coherent accumulation results based on the Modulo Generalized Radon Fourier transform (MGRFT) algorithm. n (r(l n ),v(q n )).

[0059] Step 7: Determine the search interval and search step of the inter-segment search parameters. The target distance search interval is [0, R max,N ], the speed search range is [-v max ,v max ], where R max,N Indicates the maximum unambiguous distance of the Nth segment signal, v max is the maximum possible target speed. Based on the radar system parameters, the initial radial range, radial speed, and search step size of the range ambiguity are determined as △r = c2f s , △v=λ2T, △m=1. From this we can get that the number of search units of the distance is N r,N =ceil(R max,N / △r), the number of speed searches is N v =ceil((2v max ) / △v), the number of searches for the range ambiguity is N m,N =ceil(R max / R max,N ).

[0060] Step 8: Combine r(l N ),v(q N ),m(k N ) determines the extracted inter-segment search trajectory, that is, calculates the inter-segment distance addressing variable ξ(r(l N ),v(q N ),m(k N )):

[0061]

[0062] Among them, the distance segment transfer relationship The range of values ​​for each search parameter is as follows: distance search variable r(l N )∈[0,R max,N ],l N =0,…,N r,N-1, speed search variable v(q N )∈[-v max ,v max ],q N =0,…,N v -1, distance ambiguity search variable m(k N )∈[0,ceil(R max / R max,N )],k N =0,1,...,N m,N -1. The value rules of each search parameter are as follows: N Indicates the corresponding position of the searched radial distance in the searched radial distance sequence, and the searched radial distance is N The relationship is r(l N )=l N △r,l N =0,1,...,N r,N -1,q N Indicates the corresponding position of the searched radial velocity in the searched radial velocity sequence, and the searched radial velocity is related to q N The relationship is v(q N )=-v max +q N △v,q N =0,1,...,N v , k N Indicates the corresponding position of the searched range ambiguity in the searched range ambiguity sequence, and the searched range ambiguity is related to k N The relationship is m(k N )=k N △m,k N =0,1,...,N m,N -1;

[0063] Step 9: The echo signal S extracted in step 8 is n (ξ(r(l N ),v(q N ),m(k N )),v(q N )), through the inter-segment phase compensation function H n (v(q N )) Compensate for the phase shift of the echo. n (v(q N The mathematical expression of )) is

[0064]

[0065] Then, the echo signal after phase complementation is accumulated between segments. When the search distance, speed, and range ambiguity are r(lN )、v(q N )、m(k N ), the accumulation result of the echo under the current trajectory can be expressed as

[0066]

[0067] in, is the fuzzy distance of the target under the Nth repetition frequency, v0 is the real moving speed of the target, m 0,N is the range ambiguity of the target under the Nth segment repetition frequency. When the paths between segments match, the search parameters meet v(q N )=v0,m(k N )=m 0,N When the reference segment plane position There will be an accumulation peak, and the peak position satisfies

[0068]

[0069] Figure 3 、 Figure 4 These are the coherent accumulation results of the proposed algorithm in the range-range ambiguity plane and the range-velocity plane, respectively. Figure 5 、 Figure 6 、 Figure 7 、 Figure 8 The accumulation results of the improved RFT algorithm, RFT algorithm, MTD-GRT algorithm, and MTD algorithm under the same signal-to-noise ratio are given respectively. The accumulation results show that under the same input signal-to-noise ratio, the method of the present invention has a higher accumulation gain than the existing technology and can achieve accurate estimation of target motion parameters.

[0070] Step 10: Perform constant false alarm target detection based on the accumulated results. N ),v(q N ),m(k N ))Above the threshold When the target is determined to exist. Expressed as

[0071]

[0072] Where L is the number of detection reference units, is the noise power estimated by the reference unit, P FA is the false alarm rate.

[0073] like Figure 9The figure shows the detection performance curves of the relevant algorithms. It can be seen that the method of the present invention has better detection performance than the improved RFT algorithm, RFT algorithm, MTD-GRT algorithm and MTD algorithm when the signal-to-noise ratio is low.

[0074] Step 11: Estimate the target's motion parameters based on the peak position corresponding to the target detection result. First, extract the peak position of the detection result.

[0075]

[0076] Then, the target initial radial velocity v is derived from the reference segment inversion. 0,1 The initial radial distance r 0,1 The estimated results They are

[0077]

[0078] In summary, the method of the present invention can eliminate the influence of range movement and range ambiguity, achieve effective detection of high-speed targets in a low signal-to-noise ratio environment, and achieve accurate estimation of the target's unambiguous motion parameters.

[0079] Those skilled in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the principles of the present invention, and it should be understood that the scope of the present invention is not limited to such specific descriptions and embodiments. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims.

Claims

1. A range-ambiguous high-speed target accumulation detection method based on multi-frequency joint processing, characterized in that: include: S1. The radar uses multiple sets of repetitive linear frequency modulation signals as the transmission signal; S2. Considering the ranging ambiguity corresponding to each set of repetitive linear frequency modulation signals, the radar echo signal obtained is subjected to down-conversion processing and pulse compression processing in sequence; Obtaining pulse pressure echo signal; S3. Determine a search interval and a search step size of the intra-segment search parameter, obtain an intra-segment search parameter combination according to the search interval and the search step size of the intra-segment search parameter, and determine an extracted intra-segment search trajectory based on the intra-segment search parameter combination; S4, extracting the pulse pressure echo signal in step S2 according to the intra-segment search trajectory and performing intra-segment coherent accumulation; S5. Determine a search interval and a search step size of the inter-segment search parameter, obtain an inter-segment search parameter combination according to the search interval and the search step size of the inter-segment search parameter, and determine an extracted inter-segment search trajectory based on the inter-segment search parameter combination; S6. Extract the echo signal after the intra-segment coherent accumulation in step S4 according to the inter-segment search trajectory and perform inter-segment coherent accumulation; S7, performing constant false alarm target detection according to the inter-segment coherent accumulation result obtained in step S6; S8. Estimate the motion parameters of the target based on the peak position corresponding to the target detection result of step S7.

2. The method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing according to claim 1, characterized in that: The pulse pressure echo signal in step S2 is expressed as: Among them, s pc,n (t,t p,n ) is the pulse pressure echo signal, t is the fast time, t p,n is the slow time of the nth group of repetition frequency signals, n=1,…,N, N represents the total number of groups of repetition frequency signals, A pc is the complex amplitude of the signal after pulse compression, B is the signal bandwidth, χ(t p,n ) is the echo delay, R(t p,n ) is the time t when considering ranging ambiguity p,n The instantaneous radial distance of the target at time λ is the wavelength of the radar.

3. The method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing according to claim 2, characterized in that: The search parameter combination within the segment is (r(l n ),v(q n )), r(l n ) represents the distance search variable within the segment, v(q n ) represents the speed search variable within the segment, l n Indicates the corresponding position of the searched radial distance in the search radial distance sequence within the segment, q n Indicates the corresponding position sequence of the radial velocity searched within the segment in the search radial velocity sequence.

4. The method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing according to claim 3, characterized in that: The intra-segment search trajectory extracted in step S3 is expressed as: ζ n (r(l n ),v(q n ))=mod(r(l n )+v(q n )t p,n ,R max,n ) Among them, R max,n Indicates the maximum unambiguous distance of the nth group of repeated frequency signals.

5. The method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing according to claim 4, characterized in that: Step S4 is specifically as follows: S41, constructing the intra-segment phase compensation function H(v(q n ),t p,n ), H(v(q n ),t p,n ) is: S42, extracting the pulse pressure echo signal in step S2 based on the intra-segment search trajectory; S43, using the intra-segment phase compensation function constructed in step S41, coherently accumulate the echo signal extracted in step S42; specifically: when the search distance and speed are r(l n )、v(q n ), the accumulation result of the echo is expressed as: in, is the fuzzy distance of the target under the nth group of repetitive frequency signals, r 0,n It represents the radial distance of the target relative to the radar at the beginning of the nth segment signal, v0 is the actual moving speed of the target, A n is the accumulated amplitude of the nth group of repetition frequency signals, ρ r is the distance resolution, T n is the pulse repetition period of the nth group of repetition frequency signals; S44, traverse all search parameter combinations (r(l n ),v(q n )) to obtain the intra-segment coherent accumulation result S n (r(l n ),v(q n )).

6. The method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing according to claim 5, characterized in that: In step S5, the inter-segment search parameter combination is r(l N ),v(q N ),m(k N ), r(l N ) is the inter-segment distance search variable, l N Indicates the corresponding position of the radial distance searched between segments in the search radial distance sequence, v(q N ) is the inter-segment speed search variable, q N Indicates the corresponding position of the radial velocity searched between segments in the search radial velocity sequence, m(k N ) represents the inter-segment distance fuzzy search variable, k N Indicates the corresponding position sequence of the inter-segment searched distance ambiguity in the search distance ambiguity sequence.

7. The method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing according to claim 6, characterized in that: The inter-segment search trajectory extracted in step S5 is expressed as: Among them, R max,N Indicates the maximum unambiguous distance of the Nth group of repeated frequency signals, It is the transfer relationship between distance segments.

8. The method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing according to claim 7, characterized in that: Step S6 is specifically as follows: S61, constructing the inter-segment phase compensation function H n (v(q N )), H n (v(q N )) is expressed as: S62, extracting the echo signal after intra-segment coherent accumulation in step S4 based on the inter-segment search trajectory; S63, using the inter-segment phase compensation function constructed in step S61, coherently accumulate the echo signal extracted in step S62; specifically: when the search distance, speed, and range ambiguity are r(l N )、v(q N )、m(k N ), the accumulation result of the echo under the current trajectory is expressed as in, is the fuzzy distance of the target under the Nth group of repetitive frequency signals, and v0 is the real moving speed of the target; S64, traverse all search parameter combinations r(l N ),v(q N ),m(k N ) to obtain the inter-segment coherent accumulation result.

9. The method for detecting high-speed targets with range ambiguity based on multi-frequency joint processing according to claim 8, characterized in that: Step S8 is specifically as follows: When the paths between segments match, an accumulation peak appears at the reference segment plane position; when the accumulation peak is higher than the threshold value, it is determined that the target exists.

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