CN114415122B - 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 PDFInfo
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- 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
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- G—PHYSICS
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- 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
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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
- G01S7/41—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
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 technologies, 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 difficult to effectively detect. 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, a distinction can be made between coherent accumulation and non-coherent accumulation. Compared with non-coherent accumulation, 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 coherent accumulation using a doppler filter bank. However, high speed maneuvering targets have range walk (RM) and doppler migration (DS) in short time, causing the conventional coherent accumulation algorithm based on MTD to fail.
In order to solve the problems of RM and DS in high maneuvering target accumulation, perry et al propose KT Transform (Keystone Transform) to correct the first order RM of synthetic aperture radar imaging results. 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 developed 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 a rotation angle. 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 second order RM and DS correction compensation problem of coherent accumulation, xu et al propose a Generalized RFT (GRFT) that implements 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, because GRFT, RFRFT and RLVD are all based on parameter searching, they are computationally expensive.
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 subsegments 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 MTDs between different subsections. However, similarly to HI, SAGRFT has a limited accumulation time and accumulation pulse number within 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 an echo signal received by a radar receiver;
s2, according to a segmentation criterion:
dividing the frequency domain echo signal after pulse compression into a plurality of sub-segments in a slow time dimension; t is s Representing the sub-segment pulse accumulation time, f s Is to adoptSample frequency, λ is the signal wavelength, a max Is the maximum acceleration, c represents the speed of light;
s3, performing coherent accumulation in each sub-segment;
s4, performing coherent accumulation among all the sub-segments;
s5, performing IFFT (inverse fast Fourier transform) on the coherent accumulation results among the segments obtained in the step S4 to obtain final coherent accumulation results;
and S6, carrying out target detection according to the final coherent accumulation result.
The step S3 specifically includes: and (3) processing the echo signals corresponding to the sub-segments by using FBRFT (Frequency Bin Radon Fourier Transform, frequency domain Landong Fourier Transform) based on chirp-z transformation, and realizing coherent accumulation of signal energy in the sub-segments.
Step S4 specifically includes 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 a phase compensation equation to obtain coherent accumulation results between the sub-segments.
The step S3 specifically comprises the following steps: the velocity interval is searched, and the coherent accumulation in the sub-segment is expressed as:
wherein v (q) = q Δ v, q =1,2, \8230, N v Δ v = λ/(2T), T is the full coherent integration time, N v Is the speed number of the search, N sub Indicates the number of pulses in each segment, s m (f,t n ) For the signal of the mth subfragment, C = exp (j 4 π Δ vT) r (f+f c )/c)，T r Is the pulse repetition period, f is the distance frequency corresponding to the distance r, f c Is the carrier frequency, c is the speed of light,
performing convolution operation;
of subfragmentsTarget velocity v m IFFT is carried out after searching to obtain coherent accumulation result G of sub-segments m (t,v m )；
Wherein t is a fast time;
the coherent accumulation result G in each subfragment can be obtained from t =2r/c m (r,v m ) Expressed as:
where λ is the signal wavelength.
Step S41 constructs frequency domain phase compensation equation H corresponding to baseband signals of each sub-segment m (f, v, a) the expression is:
wherein i =1,2, \ 8230;, N a A (i) is an acceleration search sequence, N a Is 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) = v 1 ，a(i)＝a 1 When, G pro (f, v, a) is represented by
For G pro (f, v, a) performing an IFFT transform, and obtaining a final coherent accumulation result G from t =2r/c pro (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.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 shows the 10 th echo pulse range walk result;
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 the detection of different accumulation algorithms.
Detailed Description
In order to facilitate 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 totally receives N during observation p The echo pulse signal is the base band signal of the p pulse received by the radar receiver is s r (t,t p ) Wherein p =1,2,. Cndot., N p ，t p Is a slow time, t p ＝(p-1)T r And 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 f c =0.15GHz, bandwidth B =10MHz, sampling frequency f s =10MHz, pulse repetition period T r =2ms, pulse duration T p =20us, number of accumulated pulses N p =600, signal-to-noise ratio SNR =10dB. The target parameters are set as: initial target distance r 1 =200km (100 th range bin), radial velocity v 1 =450m/s, radial acceleration a 1 ＝200m/s 2 。
Step 2, for each baseband echo signal s r (t,t p ) Performing pulse compression to obtain signal s pc (t,t p ) Wherein the target instantaneous distance is
Echo time delay tau (t) p )＝2R(t p ) C, c is the speed of light; then, the pulse-pressed signal s is processed pc (t,t p ) 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 s pc (f,t p ) Where f is the distance frequency corresponding to the distance r.
The invention proceeds with N p Sub N r Point FFT, one FFT having a computational complexity of
Wherein, I cm And I ca Representing the computational complexity of complex multiplication and complex addition, respectively. Thus, N p Sub N r The computational complexity of the FFT of a point is N p I FFT (N r )，N r Is the number of distance units.
Step 3, according to the segmentation criterion
The frequency domain echo signal s after pulse compression is processed in the slow time dimension pc (f,t p ) Is divided into N c Segments, each segment having N sub One pulse, the mth segment signal being s m (f,t n ). Wherein, T s Is the sub-segment pulse accumulation time, f s Is the sampling frequency, λ is the signal wavelength, a max Is the maximum acceleration, N c ＝N p /N sub ，m＝1,2,...,N c ，t n Denotes a segmented pulse, N =1,2 sub 。
As shown in fig. 2, which is a distance trace of the 10 th segment after the echo signal is segmented, it can be seen that the distance trace caused by the acceleration is approximately negligible.
And 4, in each subsection, quickly realizing coherent accumulation of signal energy in the subsection by using FBRFT (fast Fourier transform) processing based on chirp-z transformation. For the frequency domain signal s of each segment by using chirp-z transform m (f,t n ) Implementing FBRFT algorithm, searching speed interval, and expressing coherent accumulation in subsegment as
Wherein v (q) = q Δ v, q =1,2, \8230, N v ，Δv＝λ/(2T)，T＝N c T s Is the full coherent integration time, N v Is the speed number of the search, C = exp (j 4 π Δ vT) r (f+f c )/c)，
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 subsegment m (t,v m ) T is the fast time, v m Is the target speed within the sub-segment. Obtaining coherent accumulation result G in each subsection from t =2r/c m (r,v m ) Can be expressed as
Wherein A is pc Representing 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 above.
This step significantly reduces the computational complexity. Due to the division into N c Segments, and each sub-segment contains N sub For each pulse, the computational complexity of the sub-segment after Chirp-z transformation is:
I CZT (J)＝(2J+Jlog 2 (J))I cm +2N r log 2 (J)I ca
wherein J = N v +N sub ，N v Is the search point number of the speed. In all, N is carried out r N c sub-Chirp-z transform operation, the computational complexity of the step is N r N c I CZT (J)。
Wherein a (i) = i Δ a, i =1,2, \8230, N a Is an acceleration search sequence, Δ a = λ/(N) c T s 2 ) Is the acceleration search interval, N a Is the acceleration search number.
Step 6, according to the phase compensation equation corresponding to each frame baseband signal, N is performed in the frequency domain c Carrying out coherent accumulation between the segments to obtain coherent accumulation result G between the segments pro (r,v,a)。
When searching among subsections, the signal can be represented as
Wherein, A' pc Is the signal amplitude and B is the signal bandwidth. Traversing the speeds searched out in the subsections and searching the accelerated speeds. 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) = v 1 ，a(i)＝a 1 When, G pro (f, v, a) may be represented as
For G pro (f, v, a) is IFFT-transformed, and the final coherent accumulation result G can be obtained from t =2r/c pro (r, v, a); obviously, when r = r 1 ,v＝v 1 ,a＝a 1 Then, an accumulated peak can be obtained at the target location:
the final coherent accumulation output is shown in fig. 4.
The phase compensation and the distance walk compensation between the subsegments of the frequency domain of the step need to be carried out by N r N v N a N c Second order complex multiplication and N r N v N a (N c -1) complex additions of numbers, N a Is the number of acceleration points searched, and therefore the calculation complexity is N r N v N a N c I cm +N r N v N a (N c -1)I ca 。
Combining the step 2 and the step 4, the calculation complexity of the algorithm is
I proposed ＝(N p +N v N a )I FFT (N r )+N r N c I CZT (J)+N r N v N a N c I cm +N r N v N a (N c -1)I ca
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 the 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 | G pro (r,v,a)| 2 Above a threshold value
Then the target is determined to be present. Threshold value->
Is shown as
Wherein L is the number of the detection reference units,
for the noise power estimated by the reference unit, P FA Is 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 (3)
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 an echo signal received by a radar receiver;
s2, according to a segmentation criterion:
dividing the frequency domain echo signal after pulse compression into a plurality of sub-segments in a slow time dimension; t is a unit of s Representing the sub-segment pulse accumulation time, f s Is the sampling frequency, λ is the signal wavelength, a max Is the maximum acceleration, c represents the speed of light;
s3, performing coherent accumulation in each sub-segment;
s4, performing coherent accumulation between each sub-segment; step S4 specifically includes the following sub-steps:
s41, constructing a frequency domain phase compensation equation corresponding to each sub-segment echo signal; step S41 constructs frequency domain phase compensation equation H corresponding to baseband signals of each sub-segment m (f, v, a) the expression is:
wherein i =1,2, \8230, N a A (i) is an acceleration search sequence, N a Is the number of acceleration searches, v (q) = qDeltav, q =1,2, \ 8230;, N v ，N v Is the speed number searched, Δ v = λ/(2T), T is the full coherent integration time, m =1,2 c ，N c Representing the total number of sub-segments, f is the distance frequency corresponding to the distance r, f c Is the carrier frequency;
s42, performing coherent accumulation between the sub-segments in the frequency domain according to a phase compensation equation to obtain coherent accumulation results between the sub-segments; step S42 further includes: the method comprises the following specific steps: traversing the speeds searched in the subsections, and searching the acceleration; when the traversing speed is equal to the real speed of the target and the searching acceleration is equal to the targetAt true acceleration, i.e. v (q) = v 1 ，a(i)＝a 1 Then obtaining the coherent accumulation result G between the sub-segments pro (f, v, a), the expression is:
A' pc is the amplitude of the signal corresponding to the frequency domain, N sub Indicates the number of pulses in each segment, r 1 Representing the target initial distance, B representing the bandwidth;
s5, performing IFFT (inverse fast Fourier transform) on the coherent accumulation results among the segments obtained in the step S4 to obtain final coherent accumulation results;
and S6, performing 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: 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:
wherein A is pc Representing the amplitude, s, of the pulse-compressed signal m (f,t n ) For the signal of the mth subfragment, C = exp (j 4 π Δ vT) r (f+f c )/c)，T r Is a pulse repetition period of the time period of the pulse,
performing convolution operation;
pair subfragmentsTarget velocity v of m IFFT is carried out after searching to obtain coherent accumulation result G of sub-segments m (t,v m )；
Wherein t is a fast time;
the coherent accumulation result G in each subfragment can be obtained from t =2r/c m (r,v m ) Expressed as:
where λ is the signal wavelength.
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