CN108344982B - Small unmanned aerial vehicle target radar detection method based on long-time coherent accumulation - Google Patents

Small unmanned aerial vehicle target radar detection method based on long-time coherent accumulation Download PDF

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CN108344982B
CN108344982B CN201810121320.0A CN201810121320A CN108344982B CN 108344982 B CN108344982 B CN 108344982B CN 201810121320 A CN201810121320 A CN 201810121320A CN 108344982 B CN108344982 B CN 108344982B
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CN108344982A (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

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Abstract

The invention belongs to the technical field of radar target detection, and particularly relates to a small sectional motion maneuvering target detection method based on long-time coherent accumulation. According to the technical scheme, the slope distance between the radar and the moving target is modeled mainly by using a piecewise equation. Then, a proposed phase-coherent accumulation method based on RFT is applied and the parameters are searched to realize long-time phase-coherent accumulation of target echo signals, so that the detection probability of the target is improved. The method has the beneficial effect that the method can realize the small-scale target of fast identifying the segmental motion.

Description

Small unmanned aerial vehicle target radar detection method based on long-time coherent accumulation
Technical Field
The invention belongs to the technical field of radar target detection, and particularly relates to a method for detecting a target radar of a small unmanned aerial vehicle based on long-time coherent accumulation.
Background
With the increasing maturity of unmanned aerial vehicle technology and the substantial decline of related product prices, various types of unmanned aerial vehicles have been applied to different fields. However, the unmanned aerial vehicle brings convenience to people and becomes a criminal tool in the hands of lawbreakers. Due to the loss of unmanned aerial vehicle supervision and control measures, the phenomena of abuse and illegal flight of the unmanned aerial vehicle become more and more serious. In the face of the threat of such targets, higher requirements are put on the detection capability of the radar.
Nowadays, many rotor unmanned aerial vehicle not only controls simply, can the VTOL, also can hover in the air easily after taking off, has more possessed the higher advantage of duty nature, when this type unmanned aerial vehicle motor, electronic governor, battery, oar and frame damage promptly, replaces parts etc. very easily, has received more and more consumers' favor. The complex environment of a low-altitude airspace is aggravated due to the fact that the flying height of the small-sized unmanned aerial vehicle is more than or equal to 1000 meters in a low-altitude area, so that a discovery and rapid detection method for the small-sized unmanned aerial vehicle is urgently needed at present, but the following difficulties exist in detecting the targets of the small-sized unmanned aerial vehicle with the low-altitude multiple rotors:
(1) due to the influence of ground object shielding, strong ground clutter interference and earth curvature, the detection capability of the radar on the low-altitude small unmanned aerial vehicle is reduced.
(2) The RCS of the unmanned aerial vehicle target is small, the content of metal in a manufacturing material is low, so that the echo signal of the unmanned aerial vehicle target is weak, and particularly, the target is easily submerged by a large amount of noise under the interference of a complex background in an urban area;
(3) due to the low target speed, there is a severe overlap with clutter in the doppler domain, and it is difficult to suppress clutter through the conventional frequency domain filtering method.
(4) The drone can move and hover in the air at any time and place, and this feature of the rotor can cause segmental motion, thus changing the motion parameters.
(5) The unmanned aerial vehicle has high-order motion parameters, the unmanned aerial vehicle can have an acceleration or deceleration motion state in the process of taking off, stopping and flying, and under the acceleration or deceleration motion state, the unmanned aerial vehicle can have the high-order motion parameters such as acceleration, jerk and the like.
For the above reasons, conventional detection techniques are no longer suitable for detecting low altitude drone targets. At present, a radar detection technology aiming at a target of a small unmanned aerial vehicle is urgently needed to be researched. The long-time coherent accumulation technology is one of research hotspots for improving the radar detection capability. The effective coherent accumulation of the echo pulse train can greatly improve the signal-to-noise ratio and the signal-to-clutter ratio of the target, thereby improving the detection capability of the radar. Therefore, the detection of the small unmanned aerial vehicle target by the radar is realized through a long-time coherent accumulation technology under the condition of considering the target sectional motion.
Disclosure of Invention
Aiming at the problems, the invention provides a radar detection method of a target of a small unmanned aerial vehicle based on long-time coherent accumulation, and solves the problem that the existing low-altitude airspace lacks detection of the small unmanned aerial vehicle.
The technical scheme of the invention is that a radar is adopted to detect the target of the small unmanned aerial vehicle, the unmanned aerial vehicle is in hovering, accelerating or decelerating states in the flight process, the speed is time-varying, and the traditional polynomial function is not suitable for establishing the slant distance between the radar and the target of the unmanned aerial vehicle, so that the slant distance between the radar and the target of the unmanned aerial vehicle is modeled by adopting a piecewise motion equation, and the instantaneous slant distance in the observation time can be described as follows:
Figure GDA0003120827980000021
Figure GDA0003120827980000022
wherein r is0Is the initial slant distance, v, of the target from the radariI is 0,1, … M is the radial velocity of the target i +1 th motion, TiI is 0,1, … M is the end time of the i +1 th motion of the target, and the total number of segments of the segmented motion of the target is M + 1. t is tmmT, m 0,1,2, … denotes slow time, T is pulse repetition time;
according to the above model, the detection method comprises the following steps:
s1, the signal transmitted by the radar is a Linear Frequency Modulation (LFM) signal:
Figure GDA0003120827980000023
where rect () is a rectangular window function, TrIs the pulse width, gamma is the chirp rate, f0Is the carrier frequency of the carrier wave,
Figure GDA0003120827980000031
is the total time of day that is,
Figure GDA0003120827980000032
is the time range (fast time).
S2, at the receiving end, after coherent demodulation and pulse compression, the wide pulse is changed into a narrow pulse, so as to improve the range resolution, and thus the received signal is:
Figure GDA0003120827980000033
by using a piecewise equation of motion, the echo signal of the drone target is:
Figure GDA0003120827980000034
where, c is the speed of light,
Figure GDA0003120827980000035
wavelength of echo signal, B ═ y TrIs the bandwidth of the communication channel, the bandwidth,
Figure GDA0003120827980000036
is a sinc function, A1Is the amplitude of the echo after pulse compression and reduces it to a constant.
S3, searching for polarization distance rho0Polarization angle theta and end time T of the k +1 th motionkAnd to echo signals
Figure GDA0003120827980000037
Performing coherent accumulation based on RFT, specifically:
Figure GDA0003120827980000038
the range walk curve of a planar object is determined by parameters (rho, theta), wherein the polarization distance rho0E (- ∞, + ∞), polarization angle theta e 0, pi]End time T of the k +1 st movementk∈[0,TCPI]，k＝0,1,…M-1，TCPI＝TMIs the phase-coherent integration time.
The doppler filter function is defined as:
Figure GDA0003120827980000041
consider the correlation of (ρ, θ) and (r, v):
Figure GDA0003120827980000042
θi＝arc cot(-vi)
ρiand thetaiThe polarization distance and the polarization angle of the i +1 th motion of the target are respectively.
Figure GDA0003120827980000045
For echo signal
Figure GDA0003120827980000043
Performing coherent accumulation based on RFT, specifically:
Figure GDA0003120827980000044
where ρ is0，θiAnd TkIs the search variable, p0∈(-∞,+∞)，θi∈[0,π]，Tk∈[0,TCPI]，i＝0,1,…M,k＝0,1,…M-1,TM＝TCPIIs the phase-coherent integration time.
Depending on the correlation between (ρ, θ) and (r, v), the above equation can be rewritten as:
Figure GDA0003120827980000051
wherein r is0，viAnd TkAre respectively r0，viAnd TkSearch variable of r0∈(-∞,+∞)，vi∈(-∞,+∞)，Tk∈[0,TCPI]Realizing coherent accumulation by searching parameters to obtain an integral peak value of the echo;
s4, adopting cell average constant false alarm rate (CA-CFAR) detection technology to carry out phase comparisonDetecting the echo integral peak value after the accumulation, and using a decision threshold V in a comparatorTAnd comparing and judging with the detected unit signal, wherein the detected unit Y meets the following conditions:
|Y|>VT
the target is decided to exist.
Wherein, the decision threshold VT＝ZTa，
Figure GDA0003120827980000052
Is all reference cell signals x in a cell-averaged constant false alarm detectoriI is the mean of the sum of 1,2, …,2n, the threshold weighting factor
Figure GDA0003120827980000053
PfaIs a false alarm.
The method has the beneficial effect that the method can realize the small-scale target of fast identifying the segmental motion.
Drawings
FIG. 1 is a CA-CFAR detector;
FIG. 2 is a sectional motion trace of an object;
FIG. 3 shows the result of distance compression of the target motion trajectory;
FIG. 4 shows the coherent integration results of the method of the present invention;
FIG. 5 is a comparison of the detection performance of the method of the present invention and the RFT method.
Detailed Description
The technical scheme of the invention is described in detail in the following with the accompanying drawings:
the signal transmitted by the radar is a linear frequency modulation signal:
Figure GDA0003120827980000061
where rect (-) is a rectangular window function, TrIs the pulse width, gamma is the chirp rate, f0Is the carrier frequency of the carrier wave,
Figure GDA0003120827980000062
is the total time of day that is,
Figure GDA0003120827980000063
is the time range (fast time), tmmT, m is 0,1,2, … denotes slow time, T is pulse repetition time.
Using a piecewise function to model the unmanned aerial vehicle target motion process, the instantaneous slope distance over the observation time can be described as:
Figure GDA0003120827980000064
Figure GDA0003120827980000065
wherein r is0Is the initial slant distance, v, of the target from the radariI is 0,1, … M is the radial velocity of the target i +1 th motion, TiI is 0,1, … M is the end time of the i +1 th motion of the target, and the total number of segments of the segmented motion of the target is M + 1.
Then, pulse compression is carried out on the linear frequency modulation signal, and the received signal is as follows:
Figure GDA0003120827980000066
wherein c is 3 × 108It is the speed of light that is,
Figure GDA0003120827980000067
wavelength of echo signal, B ═ y TrIs the bandwidth of the communication channel, the bandwidth,
Figure GDA0003120827980000068
is a sinc function, A1Is the amplitude of the echo after pulse compression and reduces it to a constant.
By using
Figure GDA0003120827980000069
And equation 2 may write equation 4 as:
Figure GDA00031208279800000610
as can be seen from the above formula, the range-compressed echoes of the maneuvering target are approximately distributed in
Figure GDA0003120827980000071
On the multiple straight lines of the plane, fig. 1 shows the segmental motion of objects with different radial velocities, which can be seen to result in range walk (RM) and doppler shift (DFM). Both range walk and doppler shift can result in severe coherent accumulation loss. Therefore, in order to realize the long-time coherent accumulation, it is necessary to compensate for the range walk and the doppler shift due to the variable speed motion of the target.
Based on the above problems, an RFT-based method is proposed to realize coherent accumulation of segmented moving targets.
Figure GDA0003120827980000072
The range walk curve of a planar target is determined by the parameters (rho, theta), the polarization distance rho0E (- ∞, + ∞) is defined as the sum of distance walking
Figure GDA0003120827980000073
Minimum distance between the origins of the planes, θ ∈ [0, π ∈ >]Is defined as the line of polarization distance to tmThe counterclockwise angle between the axes.
Considering the correlation of (ρ, θ) and (r, v), we find:
Figure GDA0003120827980000074
θi＝arc cot(-vi) (formula 7)
ρiAnd thetaiThe polarization distance and the polarization angle of the i +1 th motion of the target are respectively.
The doppler filter function is:
Figure GDA0003120827980000075
will rhoiAnd thetaiThe doppler filter function is taken to obtain:
Figure GDA0003120827980000081
for echo signal
Figure GDA0003120827980000082
Performing coherent accumulation based on RFT, specifically:
Figure GDA0003120827980000083
substitution of formulae 5 and 9 for formula 10 can result:
Figure GDA0003120827980000084
it can be seen that all echoes are coherently accumulated. In practical applications, the distance p is polarized before target detection and parameter estimation0Angle of polarization thetaiEnd time T of i +1 th movement of targetiAll unknown, the coherent accumulation is realized by searching the polarization angle and the end time of each motion, specifically:
Figure GDA0003120827980000085
where ρ is0，θiAnd TkIs the search variable, p0∈(-∞,+∞)，θi∈[0,π]，Tk∈[0，TCPI]，i＝0,1,…M,k＝0,1,…M-1,TM＝TCPIIs the phase-coherent integration time.
Depending on the correlation between (ρ, θ) and (r, v), the above equation can be rewritten as:
Figure GDA0003120827980000091
r0∈(-∞,+∞)，vi∈(-∞,+∞)，Tk∈[0,TCPI]，r0，viand TkAre respectively r0，viAnd TkThe integral peak value of the echo is obtained by realizing coherent accumulation through searching parameters.
The detection method comprises the steps of detecting a unit to be detected after coherent accumulation by adopting a unit average constant false alarm rate (CA-CFAR) detection technology, wherein the structure of a CA-CFAR detector is shown in figure 1, unit signals after square law detection enter a shift register with the length of 2n +1 in a serial mode, the front n units and the rear n units in the register come from reference windows, the middle 1 window is a unit to be detected, and the 2n window units come from a distance or speed channel adjacent to the unit to be detected. Z is all reference cell signal xiI-mean of the sum of 1,2, …,2 n:
Figure GDA0003120827980000092
wherein, the threshold weighting coefficient TaIs determined by the following formula:
Figure GDA0003120827980000093
wherein, PfaFor false alarm probability, the estimated value Z is multiplied by a threshold coefficient T in a multiplieraTo obtain a decision threshold VT＝ZTa. In the comparator VTAnd comparing and judging with the detected unit signals.
The effectiveness of the technical scheme of the invention is proved by simulation below, and the simulation parameters of the radar and the target are as follows in table 1:
TABLE 1 simulation parameters for radar and moving target
Figure GDA0003120827980000094
Figure GDA0003120827980000101
The distance compression result of the maneuvering target and the result of the method provided by the invention are respectively shown in fig. 3 and fig. 4, gaussian noise is added into the target echo, the input signal-to-noise ratio is 0dB after the distance compression, the track of the moving target is shown in fig. 2, and the target can be seen to have three-stage motion. The graph shows the coherent accumulation results of the method proposed herein, and it can be seen that the target energy is concentrated in one peak in the simulation results, and if the peak is larger than a given threshold, the target can be detected.
The detection performance of the method proposed herein and the RFT method was compared by monte carlo experiments as shown in fig. 5. The input signal-to-noise ratio after distance compression is from-30 dB to 0dB in steps of 1 dB. T is0And T0Equal to 0.4s and 0.7 s. For a given signal-to-noise ratio, 100 monte carlo experiments were performed. The false alarm probability is set to a constant value Pfa＝10-6As can be seen from fig. 5, the method can obtain better detection performance at low signal-to-noise ratio.

Claims (1)

1. The method for detecting the target radar of the small unmanned aerial vehicle based on the long-time coherent accumulation is characterized in that the slope distance between the radar and the target is modeled by a piecewise function, and the instantaneous slope distance in the observation time is described as a piecewise motion equation:
Figure FDA0003120827970000011
Figure FDA0003120827970000012
wherein r is0Is the initial slant distance, v, of the target from the radariI is 0,1, … M is the radial velocity of the target i +1 th motion, TiI is 0,1, … M is the end time of the i +1 th motion of the target, and the total segment number of the segmented motion of the target is M + 1; t is tmmT, m 0,1,2, … denotes slow time, T is pulse repetition time;
the detection method comprises the following steps:
s1, transmitting signals:
setting the signal transmitted by the radar as a chirp signal:
Figure FDA0003120827970000013
where rect (-) is a rectangular window function, TrIs the pulse width, gamma is the chirp rate, f0Is the carrier frequency of the carrier wave,
Figure FDA0003120827970000014
is the total time of day that is,
Figure FDA0003120827970000015
is a time range;
s2, receiving signal:
and performing pulse compression on the linear frequency modulation signal, wherein the received signal is as follows:
Figure FDA0003120827970000016
according to
Figure FDA0003120827970000017
And a piecewise motion equation, wherein the echo signal of the unmanned aerial vehicle target is:
Figure FDA0003120827970000021
where, c is the speed of light,
Figure FDA0003120827970000022
wavelength of echo signal, B ═ y TrIs the bandwidth of the communication channel, the bandwidth,
Figure FDA0003120827970000023
is a sinc function, A1Is the amplitude of the echo after pulse compression and simplifies it to a constant;
s3, searching for polarization distance rho0Polarization angle theta and end time T of the k +1 th motionkAnd to echo signals
Figure FDA0003120827970000024
Performing coherent accumulation based on RFT, specifically:
Figure FDA0003120827970000025
the range walk curve of a planar object is determined by parameters (rho, theta), wherein the polarization distance rho0E (- ∞, + ∞), polarization angle theta e 0, pi]End time T of the k +1 st movementk∈[0,TCPI]，k＝0,1,…M-1，TCPI＝TMIs the phase-coherent integration time;
the doppler filter function is defined as:
Figure FDA0003120827970000026
consider the correlation of (ρ, θ) and (r, v):
Figure FDA0003120827970000027
θi＝arccot(-vi)
where ρ isiAnd thetaiAre respectively the targetThe polarization distance and polarization angle of the (i + 1) th motion;
Figure FDA0003120827970000031
for echo signal
Figure FDA0003120827970000032
Performing coherent accumulation based on RFT, specifically:
Figure FDA0003120827970000033
where ρ is0，θiAnd TkIs the search variable, p0∈(-∞,+∞)，θi∈[0,π]，Tk∈[0,TCPI]，i＝0,1,…M,k＝0,1,…M-1,TM＝TCPIIs the phase-coherent integration time;
coherent integration result J based on the correlation of (ρ, θ) and (r, v)rvComprises the following steps:
Figure FDA0003120827970000034
wherein r is0，viAnd TkAre respectively r0，viAnd TkSearch variable of r0∈(-∞,+∞)，vi∈(-∞,+∞)，Tk∈[0,TCPI]Realizing coherent accumulation by searching parameters to obtain an integral peak value of the echo;
s4, detecting the echo integral peak value after coherent accumulation by using a unit average constant false alarm detection technology, and using a decision threshold V in a comparatorTAnd comparing and judging with the detected unit signal, wherein the detected unit Y meets the following conditions:
|Y|>VT
judging that the target exists;
wherein, the decision threshold VT＝ZTa，
Figure FDA0003120827970000041
Is all reference cell signals x in a cell-averaged constant false alarm detectoriI is the mean of the sum of 1,2, …,2n, the threshold weighting factor
Figure FDA0003120827970000042
PfaIs the false alarm probability.
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