Aerial moving target detection method for dynamic grid imaging along target trajectory

Abstract

The invention provides an aerial moving target detection method for dynamic grid imaging along a target trajectory, and belongs to the field of radar signal processing. According to the method, the motion trail of the aerial moving target is utilized, dynamic grid imaging of the radar on the aerial moving target echo within dozens of seconds is achieved, and accumulation performance reduction caused by motion trail errors is further compensated through a phase gradient self-focusing algorithm. Compared with distance Doppler domain direct coherent accumulation, the method provided by the invention overcomes distance migration and Doppler diffusion caused by relative movement of the platform and the air moving target, brings greater signal-to-noise ratio gain, and further improves the detection probability of the air moving target.

Description

Technical Field

[0001] The invention relates to the field of radar signal processing, and in particular to an aerial moving target detection method using dynamic grid imaging along a target track. Background Art

[0002] High-orbit radar moves in geosynchronous orbit, with a wide visual range and a shorter revisit period for a region of interest, which is conducive to long-term observation of a region. It has great civil and military value and is a direction for future spaceborne radar systems. However, due to the particularity of its location, it also brings many difficulties to high-orbit radar signal processing.

[0003] Since the high-orbit radar has an orbital altitude of about 36,000 km, the slant distance between the satellite platform and the target on the earth's surface is too large, and the signal received by the platform is very weak, making it difficult to detect the target directly from the signal. Under the premise that the radar antenna cannot be adjusted, in order to improve the signal-to-noise ratio of the target signal, the radar echo accumulation time can only be increased. However, it often takes tens of seconds of coherent accumulation time for high-orbit radar to detect moving targets in the air. During such a long period of time, if the traditional two-dimensional coherent accumulation method in the range Doppler domain is still used, the movement of the moving targets in the air and the platform will cause serious range migration and Doppler diffusion, which greatly reduces the accumulation effect. In some cases, the movement trajectory of the moving targets in the air over a period of time is known or predictable, such as the "air corridor" that civil airliners must pass through to land at certain airports. Inputting these known target trajectory information into the signal accumulation process can effectively improve the accumulation effect and increase the detection probability of moving targets in the air. Summary of the invention

[0004] In view of this, in order to solve the technical problem that the echo signal of the high-orbit radar detecting the aerial moving target in the prior art is too weak, the present invention provides an aerial moving target detection method of dynamic grid imaging along the target trajectory, which uses the motion trajectory of the aerial moving target to realize the radar dynamic grid imaging of the aerial moving target echo for tens of seconds, and uses the phase gradient self-focusing algorithm to further compensate for the accumulated performance degradation caused by the motion trajectory error. Compared with the direct coherent accumulation in the range Doppler domain, the method proposed by the present invention will overcome the range migration and Doppler diffusion caused by the relative motion between the platform and the aerial moving target, bring greater signal-to-noise ratio gain, and further improve the detection probability of the aerial moving target.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for detecting aerial moving targets by dynamic grid imaging along the target trajectory utilizes the motion trajectory of the aerial moving target to realize dynamic grid imaging of the radar echo of the aerial moving target for tens of seconds, and uses a phase gradient self-focusing algorithm to further compensate for the accumulated performance degradation caused by the motion trajectory error.

[0007] Preferably, the method comprises the following steps:

[0008] Step (1), constructing a matched filter according to each pulse echo and waveform parameters such as pulse duration and modulation frequency, and performing matched filtering on each pulse to obtain a pulse compressed signal;

[0009] Step (2), according to the system parameters of frequency modulation bandwidth, equivalent synthetic aperture angle, and the ground grazing angle of the beam center, the range and azimuth resolutions are calculated, and the grid size is determined according to the resolution, and the center moment scene imaging grid is set, and then the predicted target position according to the known target trajectory at each pulse moment is injected into the center moment imaging grid to form a dynamic grid at each pulse moment;

[0010] Step (3), according to the antenna pointing, platform position and target position, calculate the beam off-axis angle between the radar and all target points in the dynamic grid at each pulse moment, and determine whether the target point is illuminated by the beam;

[0011] Step (4), for the target point illuminated by the beam, the equivalent slant range and echo delay between the radar and the target point are calculated according to the platform position and the target position;

[0012] Step (5), performing range interpolation processing on the pulse compression signal, and extracting the echo signal corresponding to the target point at the pulse time;

[0013] Step (6), compensating the azimuth phase factor of the echo signal extracted in step (5), and performing coherent superposition processing on each pulse to obtain a preliminary dynamic grid imaging result along the target trajectory;

[0014] Step (7), performing phase gradient self-focusing on the preliminary dynamic grid imaging result along the target trajectory obtained in step (6), further improving the accumulation effect, and obtaining a phase gradient self-focusing result of dynamic grid imaging along the target trajectory.

[0015] Preferably, in step (1), each pulse echo is a matrix of size Na*Nr, where Na is the number of pulses and Nr is the number of sampling points of each pulse;

[0016] The matched filter is 1*N r The matched filtering result is also a matrix of Na*Nr.

[0017] Preferably, in step (2), for the nth pulse, the moving target position predicted based on the prior trajectory information at the pulse moment is relative to the center moment position of the known trajectory, and the position of the entire center moment imaging grid is compensated to obtain a dynamic grid at each pulse moment.

[0018] Preferably, in step (2), the position vector of any grid point at the center moment is expressed as Then at the nth pulse time, the position vector of any grid point is expressed as

[0019] Preferably, in step (7), the specific steps of phase gradient self-focusing include:

[0020] Step (71), finding the position of the strong point target in the image, selecting the range gate where the strong point target is located, and performing a circular shift in the azimuth direction so that the strong point is shifted to the center position in the azimuth direction;

[0021] Step (72), setting a window length of appropriate size for the azimuth of the strong point target so that the main energy of the azimuth of the point target is retained in the window, and obtaining a windowed result;

[0022] Step (73), after azimuth circular shifting and windowing, performing azimuth inverse Fourier transform on the obtained image to obtain range time domain and azimuth frequency domain signals;

[0023] Step (74), estimating the phase error using a linear unbiased minimum estimate;

[0024] Step (75), integrating all azimuth phase errors to obtain the final phase error that needs to be compensated;

[0025] Step (76), multiply each point of the image by the corresponding to perform phase error compensation.

[0026] Preferably, in step (74), in actual applications, the conjugate multiplication between adjacent azimuth directions is directly used, and the phase error is estimated after taking the sum, so that the phase error of the adjacent azimuth directions can be obtained.

[0027] The method for detecting aerial moving targets by dynamic grid imaging along the target trajectory provided by the present invention utilizes the motion trajectory of aerial moving targets to realize dynamic grid imaging of the radar echo of aerial moving targets for tens of seconds, and uses the phase gradient self-focusing algorithm to further compensate for the accumulated performance degradation caused by the motion trajectory error. It is suitable for situations where the motion trajectory of aerial moving targets is known within a period of time, and allows the known trajectory of aerial moving targets to have a very large azimuth position error and a certain distance position error relative to the true trajectory. Compared with the prior art, it has the following beneficial effects:

[0028] (1) Compared with direct coherent integration in the range-Doppler domain, the method proposed in the present invention can overcome the range migration and Doppler diffusion caused by the relative motion between the platform and the aerial moving target, bring greater signal-to-noise ratio gain, and further improve the detection probability of the aerial moving target.

[0029] (2) The present invention combines the prior information of some aerial moving targets into the imaging method, thus realizing the effective coherent accumulation of signals of tens of seconds when the high-orbit radar detects aerial moving targets, effectively overcoming the problems of weak signals of aerial moving targets detected by the high-orbit radar, serious range migration caused by the movement of aerial moving targets in tens of seconds, and difficulty in signal coherent accumulation caused by Doppler diffusion. The detection probability of aerial moving targets by the high-orbit radar is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The present invention is a flowchart of the steps of a method for detecting aerial moving targets by dynamic grid imaging along target trajectories.

[0031] Figure 2 This is the preliminary dynamic grid imaging result along the target trajectory.

[0032] Figure 3 This is the phase gradient self-focusing result after dynamic grid imaging along the target trajectory.

[0033] Figure 4 This is the phase gradient self-focusing magnification result after dynamic grid imaging along the target trajectory.

[0034] Figure 5 This is a slice diagram of the phase gradient self-focusing distance after dynamic grid imaging along the target trajectory.

[0035] Figure 6 This is an azimuthal slice of the phase gradient self-focusing after dynamic grid imaging along the target trajectory.

[0036] Figure 7 This is a graph of mixed accumulation results for comparative experiments.

[0037] Figure 8 This is a magnified result diagram of mixed accumulation for comparison experiment.

[0038] Fig. 9 Mixed accumulated distance dimension slices for comparison experiments.

[0039] Fig.10 Slice diagram of the velocity dimension of the mixed accumulation for comparison experiment. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In the task of detecting aerial moving targets by high-orbit radar, the echo energy is extremely weak and needs to be accumulated for a long time. In addition, the aerial moving targets move greatly over a long time span, and traditional methods are difficult to achieve effective accumulation. The present invention provides an aerial moving target detection method using dynamic grid imaging along the target trajectory. The overall flow chart is as follows: Figure 1 As shown, the present invention utilizes the motion trajectory of an aerial moving target to realize dynamic grid imaging of the radar for tens of seconds of the echo of the aerial moving target, and uses a phase gradient self-focusing algorithm to further compensate for the accumulated performance degradation caused by the motion trajectory error.

[0042] In the present invention, the radar system parameters adopt the parameters shown in Table 1, which are as follows:

[0043] Table 1 Radar system parameters

[0044] parameter value Average antenna transmit power 10kw Antenna effective transmit and receive gain 60dB Receiver Noise Temperature 293k Receiver Noise Figure 2dB System loss 10dB Carrier frequency 3GHz bandwidth 6MHz Pulse repetition frequency 200Hz Accumulation time 50s

[0045] The present invention specifically comprises the following steps:

[0046] Step (1), according to each pulse echo Echo and waveform parameter pulse duration T r , frequency modulation K r , construct a matched filter, perform matched filtering on each pulse, and obtain the pulse compressed signal S rc .

[0047] Each pulse echo Echo is preferably a matrix of size Na*Nr, where Na is the number of pulses and Nr is the number of sampling points of each pulse. r The matched filtering result is also a matrix of Na*Nr.

[0048] Among them, the construction steps of the matched filter can be operated by the following formula:

[0049] hrc=(abs(tr)<Tr / 2)*exp(1j*pi*Kr*tr 2 )

[0050] Hrc=conj(fftshift(fft(fftshift(hrc))))

[0051] Where hrc is the time domain matched filter, abs() is the absolute value operation, t r is the pulse fast time one-dimensional vector, T r is the pulse duration, which is 0.5ms in this example. exp() is the natural exponential function, pi is the circumference of a circle, and K r is the linear frequency modulation slope. Hrc is a frequency domain matched filter, conj() is a complex conjugate operation, fft() is a fast Fourier transform operation, and fftshift() is an operation to move the zero-frequency component to the center of the spectrum.

[0052] The matched filtering operation for each pulse can be expressed as:

[0053] srfft=fftshift(fft(fftshift(Echo,2),Nr,2),2)

[0054] src=ifftshift(ifft(ifftshift(srfft*Hrc,2),Nr,2),2)

[0055] Where srfft is the frequency domain form of the pulse compressed signal, src is the time domain form of the pulse compressed signal, ifft() is the inverse fast Fourier transform operation, and ifftshift() is the inverse zero frequency shift operation.

[0056] Step (2), frequency modulation bandwidth B according to system parameters w , equivalent synthetic aperture angle Δi, the ground-grazing angle of the beam center, and the range and azimuth resolution ρ are calculated r , az , and determine the grid size dx and dy according to the resolution, and set the scene imaging grid at the center moment. Then, the predicted target position according to the known target trajectory at all pulse moments is injected into the imaging grid at the center moment to form a dynamic grid at each pulse moment.

[0057] In step (2), for the nth pulse, the moving target position predicted based on the prior trajectory information at the time of the pulse is The center position relative to the known trajectory The relative displacement The position of the imaging grid at the entire central moment is compensated to obtain the dynamic grid at each pulse moment.

[0058] If the position vector of any grid point at the central moment can be expressed as Then at the nth pulse time, the position vector of any grid point can be expressed as

[0059] In the present invention, the beam irradiation center at an altitude of 10,000 m is taken as the scene center, the north-south direction is the distance direction, the positive direction of the x-axis of the scene coordinate system is set to the due north direction, the east-west direction is the azimuth direction, the y-axis direction of the scene coordinate axis is set to the due east direction, and according to the right-hand coordinate system, the positive direction of the z-axis is close to the center of the earth. According to theoretical calculations, the range resolution of the slant range plane of the imaging processing can be expressed as:

[0060]

[0061] Where c is the speed of light and B is the radar signal bandwidth.

[0062] Considering that the ground plane and the slant range plane do not coincide, the resolutions of the two planes cannot be equal. The range resolution of the ground range plane can be expressed as:

[0063]

[0064] The angle γ is the angle between the beam line of sight and the ground plane.

[0065] There is no precise expression for the azimuth resolution of a high-orbit radar. Due to its high orbital altitude, the influence of the earth's rotation cannot be ignored, and the calculation methods of the Doppler bandwidth and equivalent velocity of the airborne model and the low-orbit model cannot be used directly. Using the generalized fuzzy function derivation, the azimuth resolution can be expressed as:

[0066]

[0067] Where λ is the wavelength of the radar signal, ω Σ is the relative angular velocity between the platform and the target point around the center of the earth, T c is the effective synthetic aperture time, then θ is the total rotation angle of the platform relative to the target point around the center of the earth. Due to the rotation of the earth and the movement of the satellite platform, the direction of the azimuth resolution is always along ω Σ Direction of angular velocity In fact, for a fixed point on the ground Since the synthetic aperture angle is very small, if the position vector of the satellite platform at the beginning and end of the synthetic aperture time is known, the total rotation angle θ can be approximated as:

[0068]

[0069] in, is the starting time of synthetic aperture, the satellite platform and the target point The unit vector between is the synthetic aperture end time, the satellite platform and the target point The unit vector between . Here All vectors are calculated in the Earth-centered Earth-fixed coordinate system (ECEF).

[0070] It should also be noted that since the target point to be imaged is a moving target, the movement of the moving target itself will also provide a component for the relative angular velocity between the platform and the target point around the center of the earth, so the movement of the target point itself will also affect the azimuth resolution of the imaging result. In addition, the phase gradient autofocusing algorithm will also improve the azimuth resolution to a certain extent.

[0071] In the present invention, the ground-to-ground resolution in the range direction is calculated to be 95.9 m, and the ground-to-ground resolution in the azimuth direction considering the motion of the moving target is calculated to be 75.6 m. Due to the phase gradient self-focusing, the actual value may be smaller. When setting the scene grid size, the embodiment selects a grid size of 50 m in the range direction and a grid size of 50 m in the azimuth direction.

[0072] Then determine the center coordinate ImageCenter in the scene coordinate system, the longitude, latitude, and altitude of the coordinate are Lmbda0, Theta0, and Elevation0. Then perform grid division. The x-axis and y-axis coordinates of each grid scene coordinate system can be expressed as:

[0073] BPImagePx=(-0.5*Nx:1:0.5*Nx-1)*dx+BPImageCenter(1)

[0074] BPImagePy=(-0.5*Ny:1:0.5*Ny-1)*dy+BPImageCenter(2)

[0075] Among them, Nx is the number of grid points in the distance direction, dx is the grid size in the distance direction, BPImageCenter(1) is the x-axis coordinate of the center coordinate; Ny is the number of grid points in the azimuth direction, dy is the grid size in the azimuth direction, and BPImageCenter(2) is the y-axis coordinate of the center coordinate. The conversion from the scene coordinate system to the latitude and longitude coordinate system can be expressed as:

[0076] GridElevation=Elevation0

[0077] GridLambdat=Lambda0+BPImagePy / (Re_center+GridElevation)

[0078] GridThetat=Theta0+BPImagePx / (Re_center+GridElevation)

[0079] Where Re_center is the equivalent earth radius at the center coordinate.

[0080] The conversion relationship from the latitude and longitude coordinate system to the ECEF coordinate system can be expressed as:

[0081] Gridxgt=(Re*Rp / sqrt(Rp 2 *cos 2 (GridThetat)+Re 2 *sin 2 (GridThetat))+GridElevation)*cos(GridThetat)*cos(GridLambdat)

[0082] Gridygt=(Re*Rp / sqrt(Rp 2 *cos(GridThetat) 2 +Re 2 *sin(GridThetat) 2 )+GridElevation)*cos(GridThetat)*sin(GridLambdat)

[0083] Among them, Re is the equatorial radius of the earth, Rp is the polar radius of the earth, sqrt() is the square root operation, GridThetat is the latitude of the target point, GridLambdat is the longitude of the target point, GridElevation is the altitude of the target point, and the coordinate posg_Grid in the ECEF coordinate system is (Gridxgt, Gridygt, Gridzgt).

[0084] In the present invention, the moving target point T is set, and its RCS is 10m 2 The position of the center moment is [0m, 0m, -10000m] at the center of the scene, and its true speed is [0m / s, -340m / s, 0m / s]. That is, at an altitude of 10000m, it moves at a constant speed of 340m / s in the due west direction.

[0085] The known moving target trajectory of the present invention is considered to have a moving target velocity of [0m / s, -300m / s, 0m / s], that is, the target trajectory injected along the target trajectory dynamic grid imaging has an azimuth velocity error of 40m / s with the actual target motion trajectory. In the present invention, the injection of the known moving target trajectory is performed directly in the ECEF coordinate system. For each pulse moment, the operation can be expressed as:

[0086] posg_Grid_movinng=posg_Grid+posg_target_moving

[0087] Among them, posg_Grid_movinng is the dynamic grid position vector along the target trajectory that needs to be used for imaging accumulation after each pulse moment, and posg_target_moving is the relative displacement vector of the position obtained at the pulse moment according to the known moving target trajectory in the ECEF coordinate system relative to the position obtained at the center moment according to the known moving target trajectory.

[0088] In the invention, since the trajectory of the known moving target in the air is set as a relatively simple uniform linear motion model, posg_target_moving can be simply obtained by multiplying the estimated speed by time, that is:

[0089] posg_target_moving=v_track*(t-t_center)

[0090] Among them, v_track is the velocity vector of the known target trajectory, t is the time variable of the current pulse moment, and t_center is the time variable of the center moment. If the known target trajectory has a more complex motion model, it can also be injected into the center moment grid to construct the dynamic grid of each pulse moment.

[0091] Step (3), according to the antenna pointing Platform location and target location Calculate the beam off-axis angle θ between the radar and all target points at the current azimuth moment a , to determine whether the target point is illuminated by the beam. In the present invention, the antenna diameter is set to 50m, the carrier frequency is 3GHz, and the half-beam width of the antenna in the range and azimuth directions is about 0.05°. The calculation of the beam off-axis angle is usually carried out in the antenna coordinate system. The coordinate origin of the antenna coordinate system is the phase center of the antenna, the x-axis points to the true flight direction of the satellite, the y-axis is along the antenna sighting line, and the direction pointing to the earth is positive. The z-axis is pointed out by the right-hand rule, so that the coordinate system constitutes a right-hand rectangular coordinate system. In other words, the direction vector pointed by the beam can be expressed as (0,1,0). If the position of the target point is converted to the coordinates in the antenna coordinate system as (posa_target_x,posa_target_y,posa_target_z), then the size of the off-axis angle can be expressed as:

[0092]

[0093] Step (4): For the target point illuminated by the beam, according to the platform position and target location Calculate the equivalent slant range R and two-way delay τ between the radar and the target point.

[0094] In addition, it should be noted that no matter the coordinate solution of the satellite platform or the ground target point, it should finally be converted to the inertial coordinate system, usually the Earth-centered inertial coordinate system (ECI), to avoid coordinate and slant range solution errors caused by changes in other coordinate systems at different time points.

[0095] Step (5): compress the pulse signal S rc Perform distance interpolation to obtain S rcup , and extract the echo signal sig corresponding to the target point at the pulse time temp .

[0096] The specific operations can be:

[0097] The result obtained after the pulse pressure signal is Fourier transformed is padded with zeros at both ends, and then the result after zero padding is inverse Fourier transformed. The upsampling multiple RangeInterpolation in the present invention is 4 times, that is, after the zero padding operation, the total number of signal points is four times the original. A pulse signal with Nr original sampling points will become Nrup = RangeInterpolation * Nr points. The specific operation can be expressed as:

[0098] srcfftup=zeros(Na,Nrup)

[0099] srcfftup(:,floor((Nrup-Nr) / 2)+(1:Nr))=srfft*Hrc

[0100] srcup=ifftshift(ifft(ifftshift(srfftup,2),Nrup,2),2)

[0101] Where Nrup is the number of signal points after upsampling, srcfftup is the frequency domain signal after zero padding, srcup is the pulse compression signal after upsampling, and step (6) is used to extract the echo signal sig from step (5). temp , to compensate for its azimuth phase factor Each pulse is coherently superimposed to obtain the preliminary dynamic grid imaging result Image along the target trajectory. The specific operation process is that for each target point at each moment, there is a calculated instantaneous slant distance R, and then the distance unit corresponding to the slant distance is selected from the signal at the corresponding moment. However, the instantaneous slant distance is often not exactly equal to the value of the distance unit, but is often between two distance units. Assume R min is the distance represented by the minimum distance unit that the signal can detect, and dr is the size of each distance unit. Then the following variables can be calculated:

[0102] N=(RR min ) / dr

[0103] N int =floor(N)

[0104] coe=NN int

[0105] In the above formula, N is the non-integer number of distance units corresponding to the slant distance R, N int The floor() operation is the rounding operation for the distance unit where the slope distance R is located. That is, R is in the Nth int and Nth int +1 distance unit. coe can be understood as R to Nth int The distance unit represents the degree of distance. When the coherent energy of the target point is accumulated at this moment, the following operations can be performed:

[0106] sig temp =(1-coe)*srcup(N)+coe*srcup(N+1)

[0107] Where srcup is the signal after four-fold upsampling of pulse compression. temp It is the temporary value of the energy accumulated at the target point at that moment.

[0108] Then directly to sig temp Phase compensation can be performed directly through sig temp *exp(j4πR / λ) is calculated for compensation. Each target will calculate the phase compensation sig at each moment temp The final value of the target point is obtained by adding the values. The initial imaging result along the target trajectory is as follows: Figure 2 As shown in the figure, under the effect of imaging along the moving target trajectory, the energy of the moving target in the air is focused to a certain extent, but it is still severely defocused, forming a straight line. At this time, the signal-to-noise ratio is very low and effective detection cannot be performed.

[0109] Step (7), performing phase gradient autofocusing (PGA) on the preliminary dynamic grid imaging result Image along the target trajectory obtained in step (6), further improving the accumulation effect, and obtaining the dynamic grid imaging phase gradient autofocusing result Image_PGA along the target trajectory.

[0110] The main principle of the PGA algorithm is to estimate the phase information in the azimuth direction according to the defocusing of the prominent point, and use the iterative idea to continuously estimate and compensate the phase error until the phase error value meets the requirements.

[0111] In the step (7), the specific steps of phase gradient autofocusing (PGA) include:

[0112] Step (71), find the strong point target position of the image Image, select the range gate m where the strong point target is located, and perform a circular shift in the azimuth direction so that the strong point is shifted to the azimuth center position M / 2+1, where M is the total number of points in the azimuth direction.

[0113] The specific operation can be expressed as:

[0114] Scene_temp(:,i)=circshift(image_temp(:,i),[-m(i)+M / 2+1 0])

[0115] Wherein, image_temp is the imaging result of step six. For the i-th range gate, in the first dimension of the image, that is, the azimuth direction, the cyclic shift size is -m(i)+M / 2+1, m(i) is the brightest point position of the range gate, and M is the number of points in the image azimuth direction, which is 200 in the present invention.

[0116] Step (72), set a window length [winL, winR] of appropriate size for the azimuth of the strong point target, so that the main energy of the point target azimuth is retained in the window, and the result after windowing is g n This operation can reduce the influence of other point targets and clutter on the accuracy of phase error estimation. In this embodiment, the setting threshold of the window length is 0.05 of the maximum energy point size of each range gate.

[0117] Step (73), after azimuth circular shift and windowing, the resulting image is represented by g n Indicates that for g n Perform inverse Fourier transform and get G n , the specific operation can be expressed as the following form:

[0118] G n (m)=|G n (m)|exp{j[φ α (m)+θ n (m)]}

[0119] In the formula, m is the image azimuth and n is the image distance. n (m)| is the amplitude. φ α (m) is the phase error, θ n (m) is the error-free phase value.

[0120] Step (74) uses linear unbiased minimum estimation to estimate the phase error. In practical applications, the phase error is estimated by directly using the conjugate multiplication between adjacent azimuth directions and taking the sum. The phase error of the adjacent azimuth directions can be obtained. The specific operation can be expressed as:

[0121]

[0122] Step (75), then calculate all azimuth phase errors By integrating, we can get the final phase error that needs to be compensated. This step can be expressed as:

[0123]

[0124] Step (76), for image G n Multiply each point by the corresponding Phase error compensation is performed. The phase error is determined during the iteration process. If the phase error is less than π / 4, the image is considered to be well focused and no compensation is required. Generally, the requirement of a phase error less than π / 4 can be achieved within 10 iterations.

[0125] The imaging result along the moving target trajectory after self-focusing is as follows Figure 3 To better observe the results, the enlarged diagram is shown in Figure 4 As shown, the range and azimuth slices are as follows Figure 5 , Figure 6 shown.

[0126] In order to verify the effectiveness of the present invention, CFAR detection is performed on the final image domain signal to realize aerial moving target detection. In this embodiment, CAFR detection uses the CA-CFAR method, the size of the protection unit in two directions is set to 3, the size of the training unit in two directions is set to 2, and the false alarm rate is set to 10 -5 CFAR is implemented using the two-dimensional CFAR detector of the phased toolbox in Matlab. The construction of the detector can be expressed as:

[0127] detector=phased.CFARDetector2D('TrainingBandSize',[3,3],'ThresholdFactor','Auto','GuardBandSize',[2,2],'ProbabilityFalseAlarm',1e-5,'Method','CA')

[0128] After 1000 Monte Carlo experiments, the final detection probability was 91.2%.

[0129] In order to further verify the effectiveness of the method proposed in the present invention, a comparative experiment was carried out.

[0130] The commonly used range Doppler domain accumulation method: hybrid accumulation method is used to accumulate the same echo. Similarly, the known trajectory of the moving target in the air is used to phase compensate the echo and then accumulate it to achieve the effect of controlling the variable.

[0131] The accumulated results are as follows Figure 7 As shown. In addition, the accumulation result interpolation amplification result is as follows Figure 8 As shown, the distance dimension and velocity dimension slicing results are as follows Fig. 9 , Fig.10 As shown. It can be seen that the signal-to-noise ratio of the accumulation result of the comparative experiment is very different from that of the accumulation result of the present invention, which is much less than the 13dB required for reliable detection. Using the CFAR detector with the same configuration to conduct 1000 Monte Carlo experiments, the final detection probability is 10.7%.

[0132] By comparison, it can be verified that the present invention has a significant improvement in the accumulation effect compared with the existing method, and the effectiveness of the method is verified. It has a significant improvement compared with the commonly used range Doppler domain accumulation method.

[0133] The method for detecting aerial moving targets by dynamic grid imaging along the target trajectory proposed by the present invention injects the motion trajectory of aerial moving targets for a period of time into the static imaging grid to obtain a dynamic grid along the target trajectory, then uses an imaging algorithm to accumulate energy, and further focuses using a phase gradient self-focusing method. The above results show that the present invention can effectively accumulate the energy of aerial moving targets, verifying the effectiveness of the present invention.

[0134] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for detecting aerial moving targets by dynamic grid imaging along target trajectories, characterized in that: By utilizing the motion trajectory of moving targets in the air, the radar can realize dynamic grid imaging of the echo of moving targets in the air for tens of seconds, and use the phase gradient self-focusing algorithm to further compensate for the accumulated performance degradation caused by the motion trajectory error.

2. The method for detecting aerial moving targets by dynamic grid imaging along target trajectory according to claim 1, characterized in that: The steps include: Step (1), constructing a matched filter according to each pulse echo and waveform parameters such as pulse duration and modulation frequency, and performing matched filtering on each pulse to obtain a pulse compressed signal; Step (2), according to the system parameters of frequency modulation bandwidth, equivalent synthetic aperture angle, and the ground grazing angle of the beam center, the range and azimuth resolutions are calculated, and the grid size is determined according to the resolution, and the center moment scene imaging grid is set, and then the predicted target position according to the known target trajectory at each pulse moment is injected into the center moment imaging grid to form a dynamic grid at each pulse moment; Step (3), according to the antenna pointing, platform position and target position, calculate the beam off-axis angle between the radar and all target points in the dynamic grid at each pulse moment, and determine whether the target point is illuminated by the beam; Step (4), for the target point illuminated by the beam, the equivalent slant range and echo delay between the radar and the target point are calculated according to the platform position and the target position; Step (5), performing range interpolation processing on the pulse compression signal, and extracting the echo signal corresponding to the target point at the pulse time; Step (6), compensating the azimuth phase factor of the echo signal extracted in step (5), and performing coherent superposition processing on each pulse to obtain a preliminary dynamic grid imaging result along the target trajectory; Step (7), performing phase gradient self-focusing on the preliminary dynamic grid imaging result along the target trajectory obtained in step (6), further improving the accumulation effect, and obtaining a phase gradient self-focusing result of dynamic grid imaging along the target trajectory.

3. The method for detecting aerial moving targets by dynamic grid imaging along target trajectory according to claim 2, characterized in that: In step (1), each pulse echo is a Na*Nr The matrix of Na is the number of pulses, Nr is the number of sampling points for each pulse; The matched filter is a 1*Nr The size of the vector, the matched filtering result is also Na*Nr The matrix of .

4. The method for detecting aerial moving targets by dynamic grid imaging along target trajectory according to claim 2, characterized in that: In step (2), for n A pulse is generated, and the moving target position predicted according to the prior trajectory information at the pulse moment is compensated for the position of the entire center moment imaging grid by the relative displacement of the center moment position of the known trajectory to obtain the dynamic grid at each pulse moment.

5. The method for detecting aerial moving targets by dynamic grid imaging along target trajectory according to claim 4, characterized in that: In step (2), the position vector of any grid point at the central moment is expressed as Then n At the pulse moment, the position vector of any grid point is expressed as 6. The method for detecting aerial moving targets by dynamic grid imaging along target trajectory according to claim 2, characterized in that: In step (7), the specific steps of phase gradient self-focusing include: Step (71), finding the position of the strong point target in the image, selecting the range gate where the strong point target is located, and performing a circular shift in the azimuth direction so that the strong point is shifted to the center position in the azimuth direction; Step (72), setting a window length of appropriate size for the azimuth of the strong point target so that the main energy of the point target azimuth is retained in the window, and obtaining a windowed result; Step (73), after azimuth circular shifting and windowing, performing azimuth inverse Fourier transform on the obtained image to obtain range time domain and azimuth frequency domain signals; Step (74), estimating the phase error using a linear unbiased minimum estimate; Step (75), integrating all azimuth phase errors to obtain the final phase error that needs to be compensated; Step (76), multiply each point of the image by the corresponding to perform phase error compensation.

7. The method for detecting aerial moving targets by dynamic grid imaging along target trajectory according to claim 6, characterized in that: In step (74), in actual applications, the conjugate multiplication between adjacent azimuth directions is directly used, and the phase error is estimated after taking the sum, so that the phase error of the adjacent azimuth directions can be obtained.

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