CN108344982A - Small drone target radar detection method based on long-time phase-coherent accumulation - Google Patents

Small drone target radar detection method based on long-time phase-coherent accumulation Download PDF

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CN108344982A
CN108344982A CN201810121320.0A CN201810121320A CN108344982A CN 108344982 A CN108344982 A CN 108344982A CN 201810121320 A CN201810121320 A CN 201810121320A CN 108344982 A CN108344982 A CN 108344982A
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target
time
signal
echo
movement
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CN108344982B (en
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曹宗杰
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Chengdu Dianke Zhida Technology Co Ltd
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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
    • G01S7/411—Identification of targets based on measurements of radar reflectivity
    • 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
    • G01S7/415—Identification of targets based on measurements of movement associated with the target

Abstract

The invention belongs to Radar Targets'Detection technical fields, particularly relate to a kind of segment movement equipment of small motor object detection method based on long-time phase-coherent accumulation.Technical scheme of the present invention, mainly by with oblique distance of the segmentation equation Modeling between radar and moving target.Then, the long-time phase-coherent accumulation for scanning for realizing target echo signal using a kind of correlative accumulation method based on RFT of proposition and to parameter, improves the detection probability of target.Beneficial effects of the present invention are that method of the invention may be implemented quickly to identify the precision target of segment movement.

Description

Small drone target radar detection method based on long-time phase-coherent accumulation
Technical field
The invention belongs to Radar Targets'Detection technical field, particularly relate to a kind of based on long-time phase-coherent accumulation Small drone target radar detection method.
Background technology
With reaching its maturity for unmanned air vehicle technique and declining to a great extent for Related product price, all types of unmanned planes are applied In different fields.However while unmanned plane offers convenience for people, the guilty tool in criminal's hand is also become. Since unmanned plane supervises the missing of control measure, unmanned plane abuse and violation Flying are on the rise.In face of such target It threatens, to the detectability of radar, more stringent requirements are proposed.
Nowadays, more rotor small drone not only manipulate simply, can in the air can also be hanged easily after taking off with VTOL Stop, more for the higher advantages of duties, i.e., when the type unmanned plane motor, electron speed regulator, battery, paddle and rack are damaged When, it is easy to replacement part etc. receives the favor of more and more consumers.And because its flying height mostly at 1000 meters it is below Low clearance area but exacerbates the complex environment of low altitude airspace, so being badly in need of seeking a kind of discovery for small drone at present With rapid detection method, but detect the more rotor small drone targets in low latitude there are following difficult points：
(1) since atural object blocks, the influence of the interference of strong land clutter and earth curvature, make radar to low latitude small drone Detectivity decline.
(2) unmanned plane target RCS is small, tenor is low in making material, causes unmanned plane target echo-signal faint, special It is not under the complex background interference of urban area, target is easily flooded by much noise；
(3) since target velocity is low, so it is overlapped in Doppler domain and clutter there are serious, it is difficult to by traditional Frequency domain filtering method clutter reduction.
(4) unmanned plane can be moved and be hovered in the air in any place at any time, this feature of rotor may be led Segment movement is caused, to change kinematic parameter.
(5) have order motion parameter, unmanned plane take off, stop and in flight course can exist accelerate or subtract The motion state of speed, under the motion state for accelerating or slowing down, unmanned plane can have the order motions such as acceleration, jerk ginseng Number.
Traditional detection technique has been no longer appropriate for for detecting low latitude small drone target due to the above reasons,.At present It is badly in need of studying a kind of radar detection technique for small drone target.Long-time phase-coherent accumulation technology is to improve detections of radar One of research hotspot of ability.To echo impulse string do effective correlative accumulation can greatly improve target signal-to-noise ratio and letter it is miscellaneous Than to improve the detectability of radar.Therefore in the case where considering target segment movement, pass through long-time phase-coherent accumulation skill Art realizes detection of the radar to small drone target.
Invention content
It is to be solved by this invention, aiming at the above problem, provide a kind of small-sized nothing based on long-time phase-coherent accumulation The radar detecting method of man-machine target solves existing low altitude airspace and lacks the test problems system for being directed to small drone.
The technical scheme is that a kind of small drone target radar detection side based on long-time phase-coherent accumulation Method detects small drone target using radar, hovers, accelerates or subtracts since unmanned plane is often in flight course The states such as speed, speed are time-varying, and traditional polynomial function is not particularly suited for establishing oblique between radar and unmanned plane target Away from therefore, being modeled using oblique distance of the segment movement equation between radar and unmanned plane target, in observation time Instantaneous oblique distance can be described as：
Wherein, r0It is target and the initial oblique distance of radar, vi, i=0,1 ... M are the radial speed of target i+1 time movement Degree, Ti, i=0,1 ... M are the end times of target i+1 time movement, and total hop count of target segment movement is M+1. tm= MT, m=0,1,2 ... indicate that slow time, T are pulse-recurrence times；
According to model above, then detection method includes the following steps：
S1, radar emission signal be linear frequency modulation (LFM) signal：
Wherein, rect () is a rectangular window function, TrIt is pulse width, γ is chirp rate, f0It is carrier frequency,It is total time,It is time range (the fast time).
S2, in receiving terminal, after coherent demodulation and pulse compression, broad pulse becomes burst pulse, improves distance resolution, because This signal received is：
Using segment movement equation, the echo-signal of unmanned plane target is：
Wherein, c is the light velocity,The wavelength of echo-signal, B=γ TrIt is bandwidth,It is sinc letters Number, A1It is the amplitude that passages through which vital energy circulates rushes compressed echo, and is reduced to a constant.
S3, search polarization distance ρ0, polarisation angles θ and the movement of kth+1 time end time TkAnd to echo-signalThe correlative accumulation based on RFT is done, specially：
The range walk curve of the target of plane is determined by parameter (ρ, θ), wherein polarization distance ρ0∈(-∞,+ ∞), [0, π] polarisation angles θ ∈, the end time T of+1 movement of kthk∈[0,TCPI], k=0,1 ... M-1, TCPI=TMIt is phase Join integration time.
Defining doppler filtering function is：
Consider the correlation of (ρ, θ) and (r, v)：
θi=arccot (- vi)
ρiAnd θiThe respectively polarization distance and polarisation angles of target i+1 time movement.
To echo-signalThe correlative accumulation based on RFT is done, specially：
Wherein ρ0, θiAnd TkIt is search variables, ρ0∈ (- ∞ ,+∞),Tk∈[0,TCPI], i=0,1 ... M, K=0,1 ... M-1, TM=TCPIIt is the correlative accumulation time.
According to the correlation of (ρ, θ) and (r, v), above formula can be rewritten as：
Wherein, r0, viAnd TkIt is r respectively0, viAnd TkSearch variables, r0∈ (- ∞ ,+∞), vi∈ (- ∞ ,+∞), Tk∈ [0,TCPI], realize that correlative accumulation obtains the Integrated peak of echo by search parameter；
S4, the echo Integrated peak after correlative accumulation is carried out using unit average constant false alarm (CA-CFAR) detection technique Decision threshold V is used in detection in a comparatorTJudgement is compared with detected cell signal, unit Y to be detected meets：
|Y|>VT
Then adjudicate target presence.
Wherein, decision threshold VT=ZT,It is all reference unit signal x in CA-CFAR detectorsi, i=1, The mean value of the sum of 2 ..., 2n, thresholding weighting coefficientPfaFor false-alarm probability.
Beneficial effects of the present invention are that method of the invention may be implemented quickly to identify the precision target of segment movement.
Description of the drawings
Fig. 1 CA-CFAR detectors；
Fig. 2 target segment movement locus；
Result of Fig. 3 target trajectories after Range compress；
The coherent integration result of Fig. 4 institute's extracting methods of the present invention；
Fig. 5 institute's extracting methods of the present invention are compared with RFT method detection performances.
Specific implementation mode
Below in conjunction with the accompanying drawings, detailed description of the present invention technical solution：
The signal of radar emission is linear FM signal in the present invention：
Wherein, rect () is a rectangular window function, TrIt is pulse width, γ is chirp rate, f0It is carrier frequency,It is total time,It is time range (fast time), tm=mT, m=0,1,2 ... indicate that slow time, T are that pulse repeats Time.
Unmanned plane target motion process is modeled using piecewise function, the instantaneous oblique distance in observation time can be by It is described as：
Wherein, r0It is target and the initial oblique distance of radar, vi, i=0,1 ... M are the radial speed of target i+1 time movement Degree, Ti, i=0,1 ... M are the end times of target i+1 time movement, and total hop count of target segment movement is M+1.
Then, pulse compression is carried out to linear FM signal, the signal received is：
Wherein, c=3 × 108It is the light velocity,The wavelength of echo-signal, B=γ TrIt is bandwidth,It is Sinc functions, A1It is the amplitude that passages through which vital energy circulates rushes compressed echo, and is reduced to a constant.
It utilizesFormula 4 can be written as with formula 2：
As can be seen from the above equation, echo of the maneuvering target after Range compress is probably distributed inThe a plurality of straight line of plane On, Fig. 1 gives the segment movement of the target with different radial velocities, it can be seen that segment movement can lead to range walk (RM) and Doppler frequency shift (DFM).Range walk and Doppler frequency shift can all cause serious correlative accumulation to be lost.Therefore, it is Realization long-time phase-coherent accumulation, range walk and Doppler frequency shift caused by needing to being moved due to target shift speed are mended It repays.
Based on the above issues, it proposes a kind of to realize the correlative accumulation to segment movement target based on the method for RFT. The range walk curve of the target of plane is by parameter (ρ, θ) decision, polarization distance ρ0∈ (- ∞ ,+∞) is defined as range walk With
Minimum range between the origin of plane, θ ∈ [0, π] are defined as polarization range line to tmIt is inverse between axis Hour hands angle.Consider the correlation of (ρ, θ) and (r, v), we obtain：
θi=arccot (- vi) (formula 7)
ρiAnd θiThe respectively polarization distance and polarisation angles of target i+1 time movement.
Doppler filtering function is：
By ρiAnd θiBringing doppler filtering function into can obtain：
To echo-signalThe correlative accumulation based on RFT is done, specially：
Wushu 5 and 9 substitution formula 10 of formula can obtain：
It can be seen that all echoes are all correlative accumulations.In practical applications, due in target detection and parameter Estimation The distance that polarizes before ρ0, polarisation angles θi, target i+1 time movement end time TiAll it is unknown, by searching for polarizing angle It spends and the end time of every section of movement is to realize correlative accumulation, specially：
Wherein ρ0, θiAnd TkIt is search variables, ρ0∈ (- ∞ ,+∞), θi∈ (0, π), Tk∈[0,TCPI], i=0,1 ... M, K=0,1 ... M-1, TM=TCPIIt is the correlative accumulation time.
According to the correlation of (ρ, θ) and (r, v), above formula can be rewritten as：
r0∈ (- ∞ ,+∞), vi∈ (- ∞ ,+∞), Tk∈[0,TCPI], r0, viAnd TkIt is r respectively0, viAnd TkSearch become Amount realizes that correlative accumulation obtains the Integrated peak of echo by search parameter.
The unit to be detected after correlative accumulation is detected using unit average constant false alarm (CA-CFAR) detection technique, CA-CFAR detectors structure is as shown in Figure 1, it is 2n that the cell signal after quadratic detection enters a length in a serial fashion + 1 shift register, preceding n and rear n unit come from reference window in register, and intermediate 1 window is to be detected unit, 2n windows units are at a distance from adjacent with detected unit or speed channels.Z is all reference unit signal xi, i=1, 2 ..., the mean value of the sum of 2n：
Wherein, thresholding weighting coefficient T is determined by following formula：
Wherein, PfaEstimated value Z is multiplied by threshold coefficient T, obtains decision threshold V in multiplier for false-alarm probabilityT= ZT.V in a comparatorTIt is compared judgement with detected cell signal.
The validity of technical solution of the present invention, simulation parameter such as the following table 1 of radar and target are proved below by emulation：
The simulation parameter of table 1 radar and moving target
Carrier frequency 5.8GHz Initial oblique distance 200m
Bandwidth 80MHz Radial velocity 0 80m/s
Sample frequency 160MHz Radial velocity 1 0m/s
Pulse-recurrence time 1ms Radial velocity 2 -100m/s
Pulse duration 10us Integration time 1.024s
The result difference of result and this paper institutes extracting method after maneuvering target Range compress is as shown in Figure 3 and Figure 4, by Gauss Noise is added in target echo and input signal-to-noise ratio is 0dB after Range compress, the track of moving target as shown in Fig. 2, It can be seen that target has three sections of movements.Figure shows the coherent integration result of this paper institutes extracting method, it can be seen that the simulation result Middle target energy concentrates on a peak value, if peak value is more than given threshold value, can detect target.
The detection performance of this paper institute's extracting method and RFT methods is compared by Monte Carlo Experiment, as shown in Figure 5.Through away from Input signal-to-noise ratio is step-length from -30dB to 0dB using 1dB after tripping contracting.T0And T0Equal to 0.4s and 0.7s.For given noise Than carrying out 100 Monte Carlo Experiments.False-alarm probability is set as steady state value Pfa=10-6, from fig. 5, it can be seen that this method exists Preferable detection performance can be obtained under low signal-to-noise ratio.

Claims (1)

1. the small drone target radar detection method based on long-time phase-coherent accumulation, which is characterized in that by radar and target Between oblique distance modeled with piecewise function, the instantaneous oblique distance in observation time is described as segment movement equation：
Wherein, r0It is target and the initial oblique distance of radar, vi, i=0,1 ... M are the radial velocity of target i+1 time movement, Ti, I=0,1 ... M are the end times of target i+1 time movement, and total hop count of target segment movement is M+1；tm=mT, m=0, 1,2 ... indicates that slow time, T are pulse-recurrence times；
It is then described that detection method includes the following steps：
S1, transmitting signal：
The signal of radar emission is set as linear FM signal：
Wherein, rect () is a rectangular window function, TrIt is pulse width, γ is chirp rate, f0It is carrier frequency,It is total time,It is time range；
S2, signal is received：
Pulse compression is carried out to linear FM signal, then the signal received is：
According to segment movement equation, the echo-signal of unmanned plane target is：
Wherein, c is the light velocity,The wavelength of echo-signal, B=γ TrIt is bandwidth,It is sinc functions, A1It is Passages through which vital energy circulates rushes the amplitude of compressed echo, and is reduced to a constant；
S3, search polarization distance ρ0, polarisation angles θ and the movement of kth+1 time end time TkAnd to echo-signalIt does Correlative accumulation based on RFT, specially：
The range walk curve of the target of plane is determined by parameter (ρ, θ), wherein polarization distance ρ0∈ (- ∞ ,+∞), pole Change angle, θ ∈ [0, π], the end time T of+1 movement of kthk∈[0,TCPI], k=0,1 ... M-1, TCPI=TMIt is correlative accumulation Time；
Defining doppler filtering function is：
Consider the correlation of (ρ, θ) and (r, v)：
θi=arccot (- vi)
Wherein, ρiAnd θiThe respectively polarization distance and polarisation angles of target i+1 time movement；
To echo-signalThe correlative accumulation based on RFT is done, specially：
Wherein ρ0, θiAnd TkIt is search variables, ρ0∈ (- ∞ ,+∞),Tk∈[0,TCPI], i=0,1 ... M, k= 0,1,…M-1,TM=TCPIIt is the correlative accumulation time；
According to the correlation of (ρ, θ) and (r, v), coherent integration result JrvFor：
Wherein, r0, viAnd TkIt is r respectively0, viAnd TkSearch variables, r0∈ (- ∞ ,+∞), vi∈ (- ∞ ,+∞), Tk∈[0, TCPI], realize that correlative accumulation obtains the Integrated peak of echo by search parameter；
S4, the echo Integrated peak after correlative accumulation is detected using unit average constant false alarm detection technique, in comparator Middle decision threshold VTJudgement is compared with detected cell signal, unit Y to be detected meets：
|Y|>VT
Then adjudicate target presence；
Wherein, decision threshold VT=ZT,It is all reference unit signal x in unit average constant false alarm detectori, i= The mean value of the sum of 1,2 ..., 2n, thresholding weighting coefficientPfaFor false-alarm probability.
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