CN109655802A - A kind of multi-objective particle swarm long time integration detection method based on CLEAN algorithm - Google Patents
A kind of multi-objective particle swarm long time integration detection method based on CLEAN algorithm Download PDFInfo
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- CN109655802A CN109655802A CN201811398217.7A CN201811398217A CN109655802A CN 109655802 A CN109655802 A CN 109655802A CN 201811398217 A CN201811398217 A CN 201811398217A CN 109655802 A CN109655802 A CN 109655802A
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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
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- 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 present invention provides a kind of multi-objective particle swarm long time integration detection method based on CLEAN algorithm, after obtaining target radar returns, target echo is segmented, it is ensured that in each split time, the movement of target does not exceed a range-doppler cells；Using segmentation Fourier transform every section of echo data is subjected to correlative accumulation respectively, obtains multistage echo correlative accumulation data；Target detection is carried out using particle swarm algorithm, obtains single goal information；Response of the target in every section of echo correlative accumulation data is rejected using CLEAN algorithm；Target detection is carried out using particle swarm algorithm again；It repeats the above process, until all targets have been detected successfully.The present invention can obtain the correlative accumulation data of multistage echo using segmentation Fourier transform, detect target in multistage echo correlative accumulation data using particle swarm algorithm, detect multiple targets using CLEAN algorithm.
Description
Technical field
The present invention relates to missile-borne pulse Doppler radar fields, and in particular to a kind of multiple target grain based on CLEAN algorithm
Subgroup long time integration detection method.
Background technique
Long time integration is generallyd use when Faint target detection, improves signal-to-noise ratio in the way of the increase target illumination time,
To improve detection performance.But when target has high speed, high maneuvering characteristics simultaneously, it will appear mesh within entire integration time
Mark across distance unit (Across Range Unit, ARU) and across frequency cells (Across Doppler Unit, ADU) phenomenon,
The build-up properties of simple accumulation method are caused to decline, the signal-to-noise ratio of target reduces, and has seriously affected detection performance.Meanwhile target
Often there is order motion characteristic, such as acceleration, acceleration, increase the difficulty and calculation amount of parameter compensation.Population
Algorithm (Particleswarm Optimization, PSO) can be used for Radar Targets'Detection, but common particle swarm algorithm is only
Global optimum can be converged on, cannot achieve multi-target detection.For radar, faint, high speed, high multiple-moving target inspection
Survey is a critical issue.
Patent CN105116395A (" a kind of moving-target long-time phase-coherent accumulation method of space-based laser weapon ") is introduced
A kind of moving-target long-time phase-coherent accumulation method of space-based laser weapon, using in pulse reference compression signal apart from when
Prolong and handled with doppler phase information, progress pulse compression and Keystone, carries out FFT, acquisition side in echo-signal orientation
Position is to correlative accumulation data.This method can reduce the movement bring doppler phase variation of radar platform.But this method
Bistatic radar must be used just to be able to achieve the extraction apart from time delay and doppler phase information, transceiver can not be applied to
Single base radar.
Patent CN103323829A (" is based on Radon-fractional order ambiguity function radar moving targets long-time phase-coherent accumulation
Detection method ") describe a kind of radar moving targets long-time phase-coherent accumulation detection method using RFRAF.By received echo
Accumulation in arteries and veins is carried out, detection parameters are initialized according to the kinetic characteristic of radar system parameters and target, are mended using RFRAF
It repays distance and Doppler walks about and completes correlative accumulation, target information extraction is carried out after CFAR detection.This method can match mesh
It marks the phase change that generates during the motion and compensates, obtain the motion information of target, but the operand of this method
It is huge, real-time processing is only just able to achieve in the case where kinematic parameter is accurately pre-installed.
Patent CN104574442A (" adaptive particle swarm optimization particle filter motion target tracking method ") describes one
Kind adaptive particle swarm optimization particle filter motion target tracking method, is filtered tracking to target using particle swarm algorithm.
Adjusted in real time using quantity of the global optimum to particle in an iterative process, using particle swarm algorithm to particle state into
Row updates.The algorithm complexity of this method is low, and calculation amount is small, but can only realize the filter tracking of single goal.
" a kind of improved Keystone transformation algorithm of document disclosed in Wang Juan in 2011 the 5th phase " airborne weapon " magazines
And its application in Detection of Weak Signals " Keystone transform method is improved, Keystone is carried out to echo data first
Then data progress IFFT is transformed into time domain in frequency domain, finally carries out FFT in slow time-domain and complete correlative accumulation, most by transformation
Moving-target detection is realized eventually.This method is combined for the range walk phenomenon occurred during weak target long time integration
Chirp-Z transformation and Keystone method, can overcome range walk phenomenon, while reducing computation complexity.But this method
It is only applicable to the target of uniform motion, when there is target order motion parameter to lead to range migration and Doppler's migration, the party
The build-up properties of method will decline.
Document " airborne radar high speed aerial maneuvering target disclosed in Wu Ren young tiger in 2013 the 1st phase " electronic letters, vol " magazines
New detecting method " benefit is proposed aiming at the problem that range walk and Doppler that high-speed maneuver target occurs during the motion are walked about
The method combined with Keystone transformation and Wigner-Hough transformation.First to echo data carry out clutter recognition, then into
Row Keystone correction distance is walked about, and Wigner-Hough transformation estimated acceleration is carried out after the Wave beam forming of airspace and is mended
It repays, finally carries out space-time two-dimensional Wave beam forming and complete accumulation.This method can overcome range walk and Doppler to walk about, and improve product
Tired signal-to-noise ratio, but when target velocity is larger, the algorithm need to carry out the fuzziness of Doppler traversal search, calculation amount compared with
Greatly, it is unfavorable for handling in real time.
" the quick long time integration based on frequency-domain correction disclosed in Wang Ze jade in 2016 the 2nd phase " airborne weapon " magazines
Algorithm " for range walk and Doppler's walk problem propose a kind of quick long time integration algorithm, this method first with
Radon-Ambiguity is scanned on acceleration, phase compensation is carried out using estimated acceleration, by the correlation between echo
Function is scanned for as cost function, is finally obtained accurate kinematic parameter and is compensated.This method can be effective
Realize envelope cancellation and Doppler effect correction, but this method needs to tie up in acceleration and carries out linear search, when priori acceleration model
When enclosing larger, the time of search is longer, influences the real-time of algorithm.
Above method cannot achieve in the case where long-time is irradiated, and faint, high speed, high multiple-moving target detection improves inspection
Survey the desired effect of performance.
Summary of the invention
The present invention relates to pulse Doppler radar field, provide a kind of multi-objective particle swarm based on CLEAN algorithm it is long when
Between integration detection method, can using segmentation Fourier transform obtain multistage echo correlative accumulation data, be calculated using population
Method detects target in multistage echo correlative accumulation data, detects multiple targets using CLEAN algorithm.
In order to achieve the above object, the invention adopts the following technical scheme:
It, will after obtaining target radar returns in multi-objective particle swarm long time integration detection method based on CLEAN algorithm
Target echo is segmented, it is ensured that in each split time, the movement of target does not exceed a range-doppler cells；Benefit
With segmentation Fourier transform every section of echo data is subjected to correlative accumulation respectively, obtains multistage echo correlative accumulation data；It utilizes
Particle swarm algorithm carries out target detection, obtains single goal information；The target is rejected in every section of echo coherent product using CLEAN algorithm
Response in tired data；Target detection is carried out using particle swarm algorithm again；Repeat the above process, until all targets by
It detects successfully.
The method includes the steps of:
(1) radar successively emits N number of pulse pulse, carrier frequency f0, the pulse signal expression of transmitting is Aexp (j2 π
f0It t), is R by distance0Place's relative velocity is v0Target reflection after, the target echo that receives is Aexp { j2 π f0[t-2
(R0-v0t)/c]}。
(2) by echo-signal and local oscillation signal exp (j2 π f0T) it is mixed, the mixed frequency signal obtained after down coversion is
A’·exp{j[2πf0(-2R0/c+2v0t/c)]}。
(3) utilize priori knowledge by entire correlative accumulation time ttotalInterior echo is divided into M sections, and every section has L pulse, really
Protect less than one distance unit of ranging offset of target in every section.
(4) every section of target echo is subjected to FFT according to range gate, obtains the time-frequency correlative accumulation data of target in this section.
(5) the N number of particle for initializing particle swarm algorithm, makes the initial value of particle be randomly dispersed in the priori range of target
[Rmin~Rmax,vmin~vmax,amin~amax] in, population the number of iterations k=1.
(6) when kth time iteration, range walk and speed of the particle i between M sections of correlative accumulation data is calculated and is walked about, it is assumed that
The initial distance of particle, velocity and acceleration are [Ri,k,vi,k,ai,k], then distance and speed of the particle in m sections are respectively
Ri,k-vi,ktm-1/2ai,ktm 2、vi,k-ai,ktm, wherein tm=m (ttotal/ M) indicate m segment data and the first segment data interval
Time.Its distance unit and frequency cells in m sections of time-frequency correlative accumulation data is obtained using the distance and speed of particle,
And carry out phase compensation.
(7) the FFT modulus value of respective distances doppler cells in M sections of time-frequency correlative accumulation data is added and is used as particle i kth
The fitness A of secondary iterationi,k.Using the highest particle of particle i fitness in preceding k iteration as the individual optimal value of particle i
[RPbesti,vPbesti,aPbesti], using the highest particle of fitness in N number of particle individual optimal value as population group
Optimal value [RGbestk,vGbestk,aGbestk]。
(8) judgement it can happen that:
Situation one: if current iteration number k >=kmin, and kth time iterates to kth-kminGroup between secondary iteration is optimal
Value variance is less than threshold value and then goes to step (10)；
Situation two: if current iteration number k >=kmin, and kth time iterates to kth-kminGroup between secondary iteration is optimal
Value variance is greater than threshold value and then goes to step (9)；
Situation three: if k < kmin, go to step (9)；
Situation four: if k >=kmax, then (10) are gone to step.
kminIndicate minimum the number of iterations, kmaxIndicate maximum number of iterations.
(9) particle is updated, the distance of particle i more new increment is Δ Ri,k+1=ω Δ Ri,k+C1·rand·
(RPbesti-Ri,k)+C2·rand·(RGbestk-Ri,k), distance is updated to Ri,k+1=Ri,k+ΔRi,k+1；Speed more new increment
For Δ vi,k+1=ω Δ vi,k+C1·rand·(vPbesti-vi,k)+C2·rand·(vGbestk-vi,k), speed is updated to
vi,k+1=vi,k+Δvi,k+1；Acceleration more new increment is Δ ai,k+1=ω Δ ai,k+C1·rand·(aPbesti-ai,k)+C2·
rand·(aGbestk-ai,k), acceleration is updated to ai,k+1=ai,k+Δai,k+1。C1And C2It indicates to update coefficient, rand indicates 0
Random quantity between~1.Go to step (6).
(10) it using population group optimal value as target value, has detected target number and has added 1.
Situation one: meeting the requirements if having detected target number, completes detection；
Situation two: it is required if having detected target number and being less than, goes to step (11).
(11) assume that target range, velocity and acceleration are [RT,vT,aT], then the target is in m sections of coherent data
Distance RT,m=RT-vTtm-1/2aTtm 2, speed vT,m=vT-aTtm, Doppler fd=2vT,m/ λ, distance unit GR=mod (RT,m/
DR), dR indicates distance resolution.In distance unit GRMiddle reconstruction echo signal is α sin [π (l-fd/df)]/[π(l-fd/
Df)], l=0,1,2 ..., L-1, α indicate the amplitude correction factor, and df indicates frequency resolution.It is weeded out from correlative accumulation data
Reconstruction signal.Return step (5).
For highly maneuvering target, realize that long time integration is more tired in the way of parameter compensation and correlative accumulation
It is difficult.In contrast, the present invention bring it is following the utility model has the advantages that
The present invention is applied to pulse Doppler radar field, and a kind of method for being capable of providing long time integration detection utilizes
It is segmented Fourier transform and improves signal-to-noise ratio, using the heuristic detection target of particle swarm algorithm, solve population using CLEAN algorithm
Algorithm can only detect the problem of single goal, target can be overcome to lead across distance unit and across doppler cells in long time integration
The phenomenon that causing detection build-up properties decline, improves detection performance, obtains the information of multiple detection targets.
Detailed description of the invention
Fig. 1: the multi-objective particle swarm long time integration detection method flow chart based on CLEAN algorithm；
Fig. 2: across the range gate phenomenon of correlative accumulation；
Fig. 3: segmentation correlative accumulation data；
Fig. 4: the 1st iteration particle distribution figure of target 1；
Fig. 5: the 21st iteration particle distribution figure of target 1；
Fig. 6: the 201st iteration particle distribution figure of target 1；
Fig. 7: 1 reconstructed image of target；
Fig. 8 a, Fig. 8 b: target 1 rejects front and back correlative accumulation data；
Fig. 9: the 201st iteration particle distribution figure of target 2.
Specific embodiment
Invention is further described in detail with reference to the accompanying drawings and examples.
As shown in Figure 1, the present invention provides a kind of multi-objective particle swarm long time integration detection side based on CLEAN algorithm
Target echo is segmented, it is ensured that in each split time, the movement of target will not by method after obtaining target radar returns
More than one range-doppler cells；Using segmentation Fourier transform every section of echo data is subjected to correlative accumulation respectively, obtained
Multistage echo correlative accumulation data；Target detection is carried out using particle swarm algorithm, obtains single goal information；Utilize CLEAN algorithm
Reject response of the target in every section of echo correlative accumulation data；Target detection is carried out using particle swarm algorithm again；It repeats
The above process, until all targets have been detected successfully.
(1) detection scene is set as Bi-objective scene, distance, the velocity and acceleration of two targets be set as [550m,
1245m/s,100m/s2]、[700m,1220m/s,100m/s2].Radar transmitted pulse signal, pulse repetition period Tr=5.5 μ
S, pulse width τ=0.5 μ s, integration time ttotal=0.1s, entire echo is as shown in Fig. 2, two targets are long-pending in entire coherent
Across range gate phenomenon all has occurred in for a long time.Directly carrying out accumulation will lead to accumulation signal-to-noise ratio is too low is difficult to detect.
(2) entire echo data is divided into M=10 sections, every section has L=1818 pulse.Target is apart from priori range
375m~750m, speed priori range are 1200m/s~1260m/s, and acceleration priori range is -10m/s2~10m/s2.Segmentation
Time is 1818 × Tr=0.01s, maximum displacement is 1260m/s × 0.01s=12.6m in section, is less than range gate width 75m,
Across range gate phenomenon will not occur according to target in priori knowledge 0.01s.
(3) correlative accumulation is carried out according to range gate to every section of 1818 pulses, the section FFT result of acquisition is as shown in Figure 3.
Theoretically two targets should be located at the 8th range gate and the 10th range gate, and the 8th distance of 10 sections of pulses is illustrated in figure
Door FFT's as a result, not across range gate, echo impulse completes correlative accumulation.
(4) particle apart from initial range is set as 375m~750m, speed initial range be set as 1200m/s~
1260m/s, acceleration initial range are set as -10m/s2~10m/s2, particle, particle number setting are initialized in the range
It is N=300.Assuming that the initial value of i-th of particle is [Ri,0,vi,0,ai,0]。
(5) assume to be currently kth time iteration, calculate range Doppler index of the particle i in 10 sections of correlative accumulation data.
The range walk of particle i is represented by m sections
In formula: tmIndicate the interval time of m sections of pulses and paragraph 1 pulse, herein tm=LTr=0.1s.Speed is walked about can
It is expressed as
vroute,m=vi,k+ai,ktm
Utilize Rroute,mAnd vroute,mIt calculates distance of the particle i in m sections of correlative accumulation data and indexes GRWith Doppler's rope
Draw Gf, expression formula is as follows
GRm=mod (Rroute/dR)
Gfm=mod (2vroute/λ/df)
In formula: dR indicates distance resolution, is herein dR=c τ/2=75m；Df indicates frequency resolution, herein df=1/
(LTr)=100Hz；λ indicates wavelength.
(6) the M section correlative accumulation data of particle i are subjected to phase compensation, the phase compensation of m sections of correlative accumulation data because
Sub- Hi,mIt is represented by
In formula: f0Indicate carrier frequency.The FFT modulus value for indexing respective distance Doppler in M sections of correlative accumulation data after compensation
It is added the fitness A for obtaining particle ii,k。
(7) when carrying out successive ignition, using the corresponding particle value of maximum adaptation degree in successive ignition as the individual of particle i
Optimal value [RPbesti,vPbesti,aPbesti].Using the highest particle value of fitness in individual optimal value as population group
Optimal value [RGbestk,vGbestk,aGbestk]。
(8) assume minimum the number of iterations kmin=200, maximum number of iterations kmax=3000, iteration ends thresholding kThreshold
=10-5。
Situation one: if current iteration number k >=kmin, and kth time iterates to kth-kminGroup between secondary iteration is optimal
Value variance is less than threshold value and then goes to step (10)；
Situation two: if current iteration number k >=kmin, and kth time iterates to kth-kminGroup between secondary iteration is optimal
Value variance is greater than threshold value and then goes to step (9)；
Situation three: if k < kmin, go to step (9)；
Situation four: if k >=kmax, then (10) are gone to step.
(9) particle is updated, the distance of particle i updates increment Delta Ri,k+1, speed update increment Delta vi,k+1, accelerate
Degree more new increment is Δ ai,k+1It is represented by
ΔRi,k+1=ω Δ Ri,k+C1·rand·(RPBesti-Ri,k)+C2·rand·(RGBest-Ri,k)
Δvi,k+1=ω Δ vi,k+C1·rand·(vPBesti-vi,k)+C2·rand·(vGBest-vi,k)
Δai,k+1=ω Δ Ri,k+C1·rand·(aPBesti-ai,k)+C2·rand·(aGBest-ai,k)
In formula: C1It indicates individual optimal value impact factor, takes 1.8；C2It indicates group's optimal value impact factor, takes 1.8；
Rand indicates the random quantity between 0~1, and w is indicated to update coefficient, is represented by
In formula: ωminIt indicates minimum and updates coefficient, be taken as 0.1；ωmaxIt indicates maximum and updates coefficient, be taken as 0.8.It has updated
Return step (5) later.
(10) by population group optimal value [RGbestk,vGbestk,aGbestk] it is used as target value, by population group
Optimal value has detected target number and has added 1 as target value.
Situation one: meeting the requirements if having detected target number, completes detection；
Situation two: it is required if having detected target number and being less than, goes to step (11).
(11) distance R of the target in m sections of coherent dataT,m=RGbestk-vGbestk tm-1/2aGbestk tm 2,
Speed vT,m=vGbestk-aGbestk tm, Doppler fd=2vT,m/ λ, distance unit GR=mod (RT,m/dR).In distance unit
GRMiddle reconstruction echo signal is α sin [π (l-fd/df)]/[π(l-fd/ df)], l=0,1,2 ..., L-1, α indicate that amplitude is repaired
Positive divisor, df indicate frequency resolution.Reconstruction signal is weeded out from correlative accumulation data.
(12) when first time population detection algorithm the 1st iteration and the 21st iteration of population population distribution map
Respectively as shown in Figure 4 and Figure 5, entire population is to optimal value direction change.Population judges to terminate before the 201st iteration
Condition, the variance of the 1st time to the 200th time iteration is -1.4681 × 10 at this time-6, it is less than iteration ends thresholding kThreshold=10-5, the final iteration result of first time target detection as shown in fig. 6, distance, the velocity and acceleration of target be respectively 585m,
1245m/s、100m/s2。
(13) detection target number is 1, less than requiring number 2, it is therefore desirable to carry out second and detect.It is calculated using CLEAN
It is as shown in Figure 7 that method reconstructs target image.First segment correlative accumulation data reject the image of front and back as shown in Fig. 8 a, Fig. 8 b, and first
Response of a target in correlative accumulation data has been removed.
(14) in second of detection, population judges termination condition before the 201st iteration, the 1st time to the 200th at this time
The variance of secondary iteration is -5.2381 × 10-6, it is less than iteration ends thresholding kThreshold=10-5, second target detection be final
Iteration result is as shown in figure 9, distance, the velocity and acceleration of target are respectively 730m, 1219m/s, 100m/s2。
(15) detection target number is 1, meets the requirements number 2, detection terminates.
In conclusion the present invention realizes long time integration using section FFT, target is solved across distance unit and across frequency list
The phenomenon of member；The detection that target is realized using particle swarm algorithm reduces meaningless searching route, improves detection performance, shortens inspection
Survey the time；Response of the target in correlative accumulation data has been detected using the rejecting of CLEAN algorithm, has realized multi-target detection.
It is discussed in detail although the contents of the present invention have passed through above preferred embodiment, but it should be appreciated that above-mentioned
Description is not considered as limitation of the present invention.After those skilled in the art have read above content, for of the invention
A variety of modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (2)
1. a kind of multi-objective particle swarm long time integration detection method based on CLEAN algorithm, it is characterised in that: obtain target thunder
Up to after echo, target echo is segmented, it is ensured that in each split time, it is more that the movement of target does not exceed a distance
General Le unit；Using segmentation Fourier transform every section of echo data is subjected to correlative accumulation respectively, obtains multistage echo coherent product
Tired data；Target detection is carried out using particle swarm algorithm, obtains single goal information；The target is rejected every using CLEAN algorithm
Response in Duan Huibo correlative accumulation data；Target detection is carried out using particle swarm algorithm again；It repeats the above process, Zhi Daosuo
Some targets have been detected successfully.
2. the multi-objective particle swarm long time integration detection method based on CLEAN algorithm, feature exist as described in claim 1
In comprising the steps of:
(1) radar successively emits N number of pulse pulse, carrier frequency f0, the pulse signal expression of transmitting is Aexp (j2 π f0T),
It is R by distance0Place's relative velocity is v0Target reflection after, the target echo that receives is Aexp { j2 π f0[t-2(R0-
v0t)/c]}；
(2) by echo-signal and local oscillation signal exp (j2 π f0T) it is mixed, the mixed frequency signal obtained after down coversion is A ' exp
{j[2πf0(-2R0/c+2v0t/c)]}；
(3) utilize priori knowledge by entire correlative accumulation time ttotalInterior echo is divided into M sections, and every section has L pulse, it is ensured that every
Less than one distance unit of ranging offset of target in section；
(4) every section of target echo is subjected to FFT according to range gate, obtains the time-frequency correlative accumulation data of target in this section；
(5) the N number of particle for initializing particle swarm algorithm, makes the initial value of particle be randomly dispersed in the priori range [R of targetmin~
Rmax,vmin~vmax,amin~amax] in, population the number of iterations k=1；
(6) when kth time iteration, range walk and speed of the particle i between M sections of correlative accumulation data is calculated and is walked about, it is assumed that particle
Initial distance, velocity and acceleration be [Ri,k,vi,k,ai,k], then distance and speed of the particle in m sections are respectively Ri,k-
vi,ktm-1/2ai,ktm 2、vi,k-ai,ktm, wherein tm=m (ttotal/ M) when indicating the interval of m segment data and the first segment data
Between；Its distance unit and frequency cells in m sections of time-frequency correlative accumulation data is obtained using the distance and speed of particle, and
Carry out phase compensation；
(7) the FFT modulus value of respective distances doppler cells in M sections of time-frequency correlative accumulation data is added as particle i kth time repeatedly
The fitness A in generationi,k；Using the highest particle of particle i fitness in preceding k iteration as the individual optimal value of particle i
[RPbesti,vPbesti,aPbesti], using the highest particle of fitness in N number of particle individual optimal value as population group
Optimal value [RGbestk,vGbestk,aGbestk]；
(8) judgement it can happen that:
Situation one: if current iteration number k >=kmin, and kth time iterates to kth-kminOptimal value side, group between secondary iteration
Difference is less than threshold value and then goes to step (10)；
Situation two: if current iteration number k >=kmin, and kth time iterates to kth-kminOptimal value side, group between secondary iteration
Difference is greater than threshold value and then goes to step (9)；
Situation three: if k < kmin, go to step (9)；
Situation four: if k >=kmax, then (10) are gone to step；
kminIndicate minimum the number of iterations, kmaxIndicate maximum number of iterations；
(9) particle is updated, the distance of particle i more new increment is
ΔRi,k+1=ω Δ Ri,k+C1·rand·(RPbesti-Ri,k)+C2·rand·(RGbestk-Ri,k),
Distance is updated to Ri,k+1=Ri,k+ΔRi,k+1；
Speed more new increment is
Δvi,k+1=ω Δ vi,k+C1·rand·(vPbesti-vi,k)+C2·rand·(vGbestk-vi,k),
Speed is updated to vi,k+1=vi,k+Δvi,k+1；
Acceleration more new increment is
Δai,k+1=ω Δ ai,k+C1·rand·(aPbesti-ai,k)+C2·rand·(aGbestk-ai,k),
Acceleration is updated to ai,k+1=ai,k+Δai,k+1；
C1And C2It indicates to update coefficient, rand indicates the random quantity between 0~1；Go to step (6)；
(10) it using population group optimal value as target value, has detected target number and has added 1；
Situation one: meeting the requirements if having detected target number, completes detection；
Situation two: it is required if having detected target number and being less than, goes to step (11)；
(11) assume that target range, velocity and acceleration are [RT,vT,aT], then distance of the target in m sections of coherent data
RT,m=RT-vTtm-1/2aTtm 2, speed vT,m=vT-aTtm,
Doppler fd=2vT,m/ λ, distance unit GR=mod (RT,m/ dR), dR indicates distance resolution；
In distance unit GRMiddle reconstruction echo signal is α sin [π (l-fd/df)]/[π(l-fd/ df)], l=0,1,2 ..., L-
1, α indicates the amplitude correction factor, and df indicates frequency resolution；
Reconstruction signal is weeded out from correlative accumulation data；Return step (5).
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| CN114384484A (en) * | 2022-01-24 | 2022-04-22 | 电子科技大学 | Segmentation processing-based rapid coherent accumulation method for uniform accelerated motion target |
| CN114384484B (en) * | 2022-01-24 | 2023-01-24 | 电子科技大学 | Segmentation processing-based rapid coherent accumulation method for uniform accelerated motion target |
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