CN114415122A - High-speed target accumulation detection method based on frequency domain segmentation processing - Google Patents

High-speed target accumulation detection method based on frequency domain segmentation processing Download PDF

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CN114415122A
CN114415122A CN202210102493.4A CN202210102493A CN114415122A CN 114415122 A CN114415122 A CN 114415122A CN 202210102493 A CN202210102493 A CN 202210102493A CN 114415122 A CN114415122 A CN 114415122A
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CN114415122B (en
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李小龙
王凯瑶
陈海旭
望明星
孙智
崔国龙
孔令讲
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
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    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract

The invention discloses a high-speed target accumulation detection method based on frequency domain segmentation processing, which is applied to the field of radar signal processing and aims at solving the problems of large calculated amount and large loss of accumulation detection performance in the prior art; secondly, applying FBRFT algorithm in the frequency domain to correct the distance walk caused by the target speed in the segment so as to realize coherent accumulation in the segment of the echo signal energy of each segment; then, for FBRFT accumulation output characteristics, an inter-segment accumulation method is proposed to obtain coherent accumulation of multi-segment signal energy. Through the joint processing between the sections in the section, the calculation complexity can be obviously reduced, and the detection performance of the radar is improved.

Description

High-speed target accumulation detection method based on frequency domain segmentation processing
Technical Field
The invention belongs to the field of radar signal processing, and particularly relates to a target detection technology.
Background
In recent years, with the rapid development and progress of aerospace technology, a large number of high-speed maneuvering targets have emerged. These targets are fast and highly mobile. Meanwhile, with the rapid development and application of the stealth technology, the target echo received by the radar is weak, and effective detection is difficult. Maneuvering targets for improving the high-speed detection performance of radar have become hot topics of research in the field of radar.
Multi-pulse accumulation is an effective way to improve the signal-to-noise ratio (SNR) of a target, thereby improving the detection performance of the target. Depending on whether phase information is utilized or not, a distinction can be made between coherent accumulation and non-coherent accumulation. Compared with non-coherent accumulation, the coherent accumulation is adopted to perform phase compensation and distance walk correction on the multi-pulse echo, and the method has higher signal-to-noise ratio gain and better accumulation detection performance. For example, a Moving Target Detection (MTD) method widely used today is to implement coherent accumulation by using a doppler filter bank. However, high speed maneuvering targets have range walk (RM) and Doppler Shift (DS) in short time, causing the conventional coherent accumulation algorithm based on MTD to fail.
In order to solve the RM and DS problems in high maneuvering target accumulation, Perry et al propose to correct the first order RM of synthetic aperture radar imaging results by KT Transform (Keystone Transform). On the basis, Zhang et al apply KT transformation to perform pulse Doppler radar correction first order RM on target detection. However, when the radar is used in a low pulse repetition frequency radar or a high speed target, doppler ambiguity often occurs, which makes the KT algorithm ineffective. Xu et al propose a Radon-Fourier transform (RFT) that achieves coherent accumulation of echoes by searching for radial distance and radial velocity. Thereafter, Oren et al derived a spectral RFT-based method for multi-target detection in high-resolution automotive radar. However, RFT can only correct first order RMs and cannot correct RMs and DS caused by acceleration. Sun et al propose a modified position rotation transform (MLRT) that modifies RM and estimates velocity by searching for rotation angles. Since KT, RFT and MLRT can only correct first order RMs, the detection performance of these algorithms on maneuvering targets is greatly reduced when acceleration-induced second order RMs and DSs occur.
For the problem of second-order RM and DS correction compensation of coherent accumulation, Xu et al propose a generalized rft (grft) to implement coherent integration by parameter search. Chen et al propose Radon fractional Fourier transform (RFRFT) which compensates for RM and uses fractional Fourier transform (FRFT) to accomplish coherent accumulation. Li et al propose a Radon Lv distribution (RLVD) that can eliminate the RM and accumulate energy in the CFCR domain. However, since GRFT, rfrfrft and RLVD are all based on parameter search, they are computationally intensive.
In order to solve the contradiction between the detection performance and the calculated amount, Xu et al propose a hybrid accumulation algorithm (HI), which realizes the accumulation of target energy by means of intra-sub-segment coherence and inter-sub-segment non-coherence. However, the gain of the HI algorithm depends on the sub-segment accumulation time. Therefore, when the accumulation time of the subsections is short, the performance loss will be large. Ding et al propose a sub-aperture GRFT (sagrft) accumulation method, in which the GRFT algorithm is used to accumulate the results of MTD between different subsections. However, like HI, SAGRFT has a limited accumulation time and accumulation pulse number in the sub-section, which is disadvantageous for improvement of accumulation performance. In order to improve the detection efficiency, for the HI performance of a high-speed maneuvering target, a spatial hybrid accumulation algorithm (SHI) is proposed, in which a parameter space is divided into a plurality of subspaces, and the parameters are accumulated by using the HI in each subspace. However, the detection performance of SHI is still limited due to non-coherent accumulation between fragments.
In addition to the segment accumulation methods described above, various mixed segment accumulation methods are being studied by many scholars. Chen et al have proposed an RFT-based time-domain segmentation processing algorithm, but the computation is still large and there is a large loss in the accumulated detection performance. Other scholars have carried out some relevant researches, but it should be pointed out that the problems of large calculation amount or poor accumulation performance exist in the conventional segmented accumulation methods, and a good compromise between the accumulation detection performance and the calculation cost is difficult to obtain.
Disclosure of Invention
In order to solve the technical problems, the invention provides a high-speed target accumulation detection method based on frequency domain segmentation processing, which can obviously reduce the calculated amount and improve the detection performance of a radar, thereby obtaining good balance between the accumulation detection performance and the calculation cost.
The technical scheme adopted by the invention is as follows: a high-speed target accumulation detection method based on frequency domain segmentation processing comprises the following steps:
s1, performing pulse compression on the echo signal received by the radar receiver;
s2, according to the segmentation criterion:
Figure BDA0003492731200000021
dividing the frequency domain echo signal after pulse compression into a plurality of sub-segments in a slow time dimension; t issRepresenting the sub-segment pulse accumulation time, fsIs the sampling frequency, λ is the signal wavelength, amaxIs the maximum acceleration, c represents the speed of light;
s3, performing coherent accumulation in each sub-segment;
s4, performing coherent accumulation among each sub-segment;
s5, carrying out IFFT transformation on the coherent accumulation results among the fragments obtained in the step S4 to obtain final coherent accumulation results;
and S6, carrying out target detection according to the final coherent accumulation result.
Step S3 specifically includes: and processing the echo signals corresponding to the sub-segments by using FBRFT (Frequency Bin Radon Fourier Transform, Frequency domain Lato Fourier Transform) based on chirp-z transformation, and realizing coherent accumulation of signal energy in the sub-segments.
Step S4 specifically includes the following substeps:
s41, constructing a frequency domain phase compensation equation corresponding to each sub-segment echo signal;
and S42, performing coherent accumulation between the sub-segments in the frequency domain according to the phase compensation equation to obtain coherent accumulation results between the sub-segments.
Step S3 specifically includes: the velocity interval is searched, and the coherent accumulation in the sub-segment is expressed as:
Figure BDA0003492731200000031
wherein v (q) ═ q Δ v, q ═ 1,2, …, NvΔ v ═ λ/(2T), T is the full coherence accumulation time, NvIs the speed number of the search, NsubRepresenting the number of pulses in each segment, sm(f,tn) For the mth subfragment signal, C ═ exp (j4 π Δ vT)r(f+fc)/c)，TrIs the pulse repetition period, f is the distance frequency corresponding to the distance r, fcFor the carrier frequency, c is the speed of light,
Figure BDA0003492731200000032
performing convolution operation;
target velocity v for subfragmentsmIFFT is carried out after searching to obtain coherent accumulation result G of sub-segmentsm(t,vm)；
Wherein t is a fast time;
the coherent accumulation result G in each subfragment can be obtained from t-2 r/cm(r,vm) Expressed as:
Figure BDA0003492731200000033
where λ is the signal wavelength.
Step S41, constructing frequency domain phase compensation equation H corresponding to each sub-section baseband signalm(f, v, a) the expression is:
Figure BDA0003492731200000041
wherein, i is 1,2, …, NaAnd a (i) is an acceleration searchSequence, NaIs the acceleration search number.
Step S42 further includes: when the traversed speed is equal to the target real speed and the searched acceleration is equal to the target real acceleration, i.e., v (q) ═ v1，a(i)＝a1When, Gpro(f, v, a) is represented by
Figure BDA0003492731200000042
For Gpro(f, v, a) performing IFFT, and obtaining a final coherent accumulation result G from t being 2r/cpro(r,v,a)。
The invention has the beneficial effects that: the invention relates to a high-speed target accumulation detection method based on frequency domain segmentation processing, which researches a combined long-time coherent accumulation method between intra-segment segments of target echo signals under distance walking; firstly, a reasonable sub-segment segmentation criterion is provided, and echo signals are segmented to reduce the computational complexity; secondly, applying FBRFT algorithm in the frequency domain to correct the distance walk caused by the target speed in the segment so as to realize coherent accumulation in the segment of the echo signal energy of each segment; then, for FBRFT accumulation output characteristics, an inter-segment accumulation method is proposed to obtain coherent accumulation of multi-segment signal energy. Through the joint processing between the sections in the section, the calculation complexity can be obviously reduced, and the detection performance of the radar is improved.
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FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 shows the range walk result of the 10 th echo pulse;
FIG. 3 shows the result of the 10 th echo pulse FBRFT;
FIG. 4 is the final intra-segment inter-segment joint coherent accumulation result;
FIG. 5 is a comparison of computational complexity for different accumulation algorithms;
fig. 6 is a graph of detection curves for different accumulation algorithms.
Detailed Description
In order to facilitate the understanding of the technical contents of the present invention by those skilled in the art, the present invention will be further explained with reference to the accompanying drawings.
The method is verified by adopting a Matlab simulation experiment method, and the correctness and the effectiveness of the method are verified on scientific computing software Matlab R2019 a. The embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, the present invention provides a high-speed target accumulation detection method based on frequency domain segmentation processing, which is specifically implemented by the following processes:
step 1, a radar adopts a linear frequency modulation signal as a transmitting signal, and a radar receiver receives N in total during observationpThe echo pulse signal is the base band signal of the p pulse received by the radar receiver is sr(t,tp) Wherein, p is 1,2p，tpIs a slow time, tp＝(p-1)TrAnd t is a fast time. Those skilled in the art will note that r in the subscript, which is an abbreviation for receive, means here differently than r, which is later denoted as distance.
The radar parameters used in this example are set to: carrier frequency fc0.15GHz, bandwidth B10 MHz, sampling frequency fs10MHz, pulse repetition period Tr2ms, pulse duration Tp20us, number of accumulated pulses NpThe SNR is 10dB for 600. The target parameters are set as: initial target distance r1200km (100 th range unit), radial velocity v1450m/s, radial acceleration a1＝200m/s2。
Step 2, for each base band echo signal sr(t,tp) Performing pulse compression to obtain signal spc(t,tp) Wherein the target instantaneous distance is
Figure BDA0003492731200000051
Echo time delay tau (t)p)＝2R(tp) C, c is the speed of light; then, the pulse-pressed signal s is processedpc(t,tp) Fourier transform is carried out along the direction of the distance r (namely the fast time dimension) to obtain a frequency domain pulse pressure signal spc(f,tp) Whereinf is the distance frequency corresponding to the distance r.
The invention proceeds with NpSub NrPoint FFT, one FFT having a computational complexity of
Figure BDA0003492731200000052
Wherein, IcmAnd IcaRepresenting the computational complexity of complex multiplication and complex addition, respectively. Thus, NpSub NrThe computational complexity of the FFT of a point is NpIFFT(Nr)，NrIs the number of distance units.
Step 3, according to the segmentation criterion
Figure BDA0003492731200000053
The frequency domain echo signal s after pulse pressure is processed in the slow time dimensionpc(f,tp) Is divided into NcSegments, each segment having NsubOne pulse, the signal of the m-th segment being sm(f,tn). Wherein, TsIs the sub-segment pulse accumulation time, fsIs the sampling frequency, λ is the signal wavelength, amaxIs the maximum acceleration, Nc＝Np/Nsub，m＝1,2,...,Nc，tnDenotes the pulse after segmentation, N1, 2sub。
As shown in fig. 2, which is a distance moving graph of the 10 th segment after the echo signal is segmented, it can be seen that the distance moving graph caused by the acceleration is approximately negligible.
And 4, in each subsection, rapidly realizing coherent accumulation of signal energy in the subsection by using FBRFT (fiber Bragg Reflector) processing based on chirp-z conversion. For the frequency domain signal s of each segment by using chirp-z transformm(f,tn) Implementing FBRFT algorithm, searching speed interval, and expressing coherent accumulation in subsegment as
Figure BDA0003492731200000061
Wherein v (q) ═ q Δ v, q ═ 1,2, …, Nv，Δv＝λ/(2T)，T＝NcTsIs the total coherent accumulation time, NvIs the speed number searched, C ═ exp (j4 π Δ vT)r(f+fc)/c)，
Figure BDA0003492731200000062
Is a convolution operation. Searching the target speed of the subsegment, and then carrying out IFFT to obtain a coherent accumulation result G of the subsegmentm(t,vm) T is the fast time, vmIs the target speed within the sub-segment. Obtaining coherent accumulation result G in each subsection from t-2 r/cm(r,vm) Can be expressed as
Figure BDA0003492731200000063
Wherein A ispcRepresenting the amplitude of the signal after pulse compression.
Fig. 3 shows the coherent FBRFT output result of the 10 th echo signal in the time domain.
The speed search interval in step 4 is set according to the prior information, namely the known possible speed range of the target. And the subsequent value range of v (q) is the search interval. In the subsequent content, the setting range of the acceleration search interval is the same as the following.
This step significantly reduces the computational complexity. Due to the division into NcSegments, and each sub-segment contains NsubFor each pulse, the computational complexity of the sub-segment after Chirp-z transformation is:
ICZT(J)＝(2J+Jlog2(J))Icm+2Nrlog2(J)Ica
wherein J is Nv+Nsub，NvIs the search point number of the speed. In all, N is carried outrNcsub-Chirp-z transform operation, the computational complexity of this step is NrNcICZT(J)。
Step 5, constructing a frequency domain phase compensation equation H corresponding to each sub-section baseband signalm(f,v,a)
Figure BDA0003492731200000071
Wherein a (i) ═ i Δ a, i ═ 1,2, …, NaIs an acceleration search sequence, Δ a ═ λ/(N)cTs 2) Is the acceleration search interval, NaIs the acceleration search number.
Step 6, according to the phase compensation equation corresponding to each frame baseband signal, N is performed in the frequency domaincCarrying out coherent accumulation between the segments to obtain coherent accumulation result G between the segmentspro(r,v,a)。
When searching among subsections, the signal can be represented as
Figure BDA0003492731200000072
Wherein, A'pcIs the signal amplitude and B is the signal bandwidth. Traversing the speeds searched in the subsections, and searching the acceleration. When the traversed speed is equal to the target real speed and the searched acceleration is equal to the target real acceleration, i.e., v (q) ═ v1，a(i)＝a1When, Gpro(f, v, a) may be represented as
Figure BDA0003492731200000081
For Gpro(f, v, a) is subjected to IFFT, and the final coherent accumulation result G can be obtained by changing t to 2r/cpro(r, v, a); obviously, when r ═ r1,v＝v1,a＝a1Then, an accumulated peak can be obtained at the target location:
Figure BDA0003492731200000082
the final coherent accumulation output is shown in fig. 4.
A sub-frequency domain of this stepThe phase compensation and the distance walk compensation between the segments need to be carried out by NrNvNaNcSecond order complex multiplication and NrNvNa(Nc-1) complex additions of numbers, NaIs the number of acceleration points searched, and therefore has a computational complexity of NrNvNaNcIcm+NrNvNa(Nc-1)Ica。
Combining the step 2 and the step 4, the calculation complexity of the algorithm is
Iproposed＝(Np+NvNa)IFFT(Nr)+NrNcICZT(J)+NrNvNaNcIcm+NrNvNa(Nc-1)Ica
Fig. 5 shows a comparison of the computational complexity of the correlation algorithm.
In conclusion, the method can eliminate the influence caused by distance walking, Doppler walking and low signal-to-noise ratio environment, realize effective detection of the high-speed target and greatly reduce the calculation complexity of the algorithm.
Step 7, carrying out target detection according to the obtained coherent accumulation result, and detecting the target energy | Gpro(r,v,a)|2Above a threshold value
Figure BDA0003492731200000083
Then the target is determined to be present. Threshold value
Figure BDA0003492731200000084
Is shown as
Figure BDA0003492731200000085
Wherein L is the number of the detection reference units,
Figure BDA0003492731200000086
for the noise power estimated by the reference unit,PFAis the false alarm rate.
As shown in fig. 6, which is a detection performance graph of the correlation algorithm, it can be seen that the method of the present invention has better detection performance than the time domain segmentation detection algorithm, the SHI algorithm, and the HI algorithm under the condition of low signal-to-noise ratio, and the performance is also better than the RFT coherent accumulation algorithm.
In conclusion, the method can eliminate the influence caused by distance walking, Doppler walking and low signal-to-noise ratio environment, realize the effective detection of the high-speed target, greatly reduce the calculation complexity of echo accumulation and improve the detection performance of the radar.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (6)

1. A high-speed target accumulation detection method based on frequency domain segmentation processing is characterized by comprising the following steps:
s1, performing pulse compression on the echo signal received by the radar receiver;
s2, according to the segmentation criterion:
Figure FDA0003492731190000011
dividing the frequency domain echo signal after pulse compression into a plurality of sub-segments in a slow time dimension; t issRepresenting the sub-segment pulse accumulation time, fsIs the sampling frequency, λ is the signal wavelength, amaxIs the maximum acceleration, c represents the speed of light;
s3, performing coherent accumulation in each sub-segment;
s4, performing coherent accumulation among each sub-segment;
s5, carrying out IFFT transformation on the coherent accumulation results among the fragments obtained in the step S4 to obtain final coherent accumulation results;
and S6, carrying out target detection according to the final coherent accumulation result.
2. The method for detecting the accumulation of the high-speed target based on the frequency domain segmentation processing as claimed in claim 1, wherein the step S3 is specifically as follows: and processing the echo signals corresponding to the sub-segments by using the FBRFT based on chirp-z transformation to realize coherent accumulation of signal energy in the sub-segments.
3. The method for detecting the accumulation of the target at high speed based on the frequency domain segmentation processing as claimed in claim 2, wherein the step S3 is implemented as follows: the velocity interval is searched, and the coherent accumulation in the sub-segment is expressed as:
Figure FDA0003492731190000012
wherein v (q) ═ q Δ v, q ═ 1,2, …, NvΔ v ═ λ/(2T), T is the full coherence accumulation time, NvIs the speed number of the search, NsubRepresenting the number of pulses in each segment, sm(f,tn) For the mth subfragment signal, C ═ exp (j4 π Δ vT)r(f+fc)/c)，TrIs the pulse repetition period, f is the distance frequency corresponding to the distance r, fcFor the carrier frequency, c is the speed of light,
Figure FDA0003492731190000013
performing convolution operation;
target velocity v for subfragmentsmIFFT is carried out after searching to obtain coherent accumulation result G of sub-segmentsm(t,vm)；
Wherein t is a fast time;
the coherent accumulation result G in each subfragment can be obtained from t-2 r/cm(r,vm) Expressed as:
Figure FDA0003492731190000021
where λ is the signal wavelength and B represents the bandwidth.
4. The method for detecting the accumulation of the high-speed target based on the frequency domain segmentation processing as claimed in claim 3, wherein the step S4 specifically comprises the following sub-steps:
s41, constructing a frequency domain phase compensation equation corresponding to each sub-segment echo signal;
and S42, performing coherent accumulation between the sub-segments in the frequency domain according to the phase compensation equation to obtain coherent accumulation results between the sub-segments.
5. The high-speed target accumulation detection method based on frequency domain segmentation processing as claimed in claim 4, wherein step S41 is to construct a frequency domain phase compensation equation H corresponding to each sub-segment baseband signalm(f, v, a) the expression is:
Figure FDA0003492731190000022
wherein, i is 1,2, …, NaA (i) is an acceleration search sequence, NaIs the acceleration search number.
6. The method for detecting the accumulation of the target at high speed based on the frequency domain segmentation processing as claimed in claim 3, wherein the step S42 further comprises: the method specifically comprises the following steps: traversing the speeds searched in the subsections, and searching the acceleration; when the traversed speed is equal to the target real speed and the searched acceleration is equal to the target real acceleration, i.e., v (q) ═ v1，a(i)＝a1Then obtaining the coherent accumulation result G between the sub-segmentspro(f, v, a), the expression is:
Figure FDA0003492731190000023
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