CN117192553A - Multi-navigation SAR three-dimensional imaging self-focusing method - Google Patents

Abstract

The invention relates to a multi-navigation SAR three-dimensional imaging self-focusing method, in particular to a multi-navigation track error estimation and compensation method based on error drop and track-target position joint estimation, which belongs to the technical field of radar three-dimensional imaging residual signal error estimation and compensation, can effectively realize SAR three-dimensional imaging signal data processing, and provides assistance for acquiring high-resolution three-dimensional images of a multi-navigation chromatography SAR system. The method adopts the information of a plurality of point targets, and can effectively estimate and compensate the space change error in a large scene; the method does not depend on the track measured by the inertial navigation system and only depends on echo information acquired by the radar system, so that the method has advantages for the radar system without inertial navigation, and images with good focusing effect can be obtained through estimating and compensating the track error.

Description

A multi-pass SAR three-dimensional imaging self-focusing method

Technical field

The invention relates to a multi-pass SAR three-dimensional imaging self-focusing method, in particular to a multi-pass track error estimation and compensation method based on error dimensionality reduction and track-target position joint estimation, which belongs to radar three-dimensional imaging residual signal error estimation. In the field of compensation technology, it can effectively realize SAR three-dimensional imaging signal data processing and provide help in obtaining high-resolution three-dimensional images of multi-pass tomography SAR systems.

Background technique

Synthetic Aperture Radar (SAR) is a high-resolution microwave imaging radar that can detect the irradiated area all day and all day and obtain high-resolution radar images of the irradiated area. SAR is usually installed on a moving platform. It achieves high range resolution through transmitting and receiving broadband signals and pulse compression, and achieves high azimuth resolution through platform motion and synthetic aperture imaging processing. Therefore, SAR can obtain two-dimensional high-resolution images of the observation scene.

At present, the SAR two-dimensional imaging technology of slow-speed unmanned platforms is relatively mature and has some applications. However, the SAR imaging technology of slow-speed unmanned platforms is still restricted by two aspects. First, the resolution and width of current SAR images are limited: the narrow relative signal bandwidth of the current SAR system limits the range resolution of SAR, especially in lower frequency bands. The narrower relative bandwidth means a narrower signal bandwidth. , that is, lower range resolution; the narrow beam width of the current SAR system results in lower azimuth resolution and narrow imaging width. The second is the limited imaging dimension of current SAR images: SAR two-dimensional images have inherent problems such as overlapping, geometric distortion, and missing dimensions, and cannot accurately reflect the true three-dimensional scattering information of objects, making the images difficult to identify and apply.

Multi-pass tomography SAR forms a third-dimensional aperture by flying multiple passes at different altitudes on the same track to achieve multi-baseline three-dimensional imaging data collection. Its resolution is not limited by the real array and has the advantage of high tomographic resolution. . However, the motion trajectories of each pass of the slow-speed unmanned platform multi-pass tomography SAR are complex, and there is no rigid body constraint relationship between the passes. Therefore, it is not only affected by the complex trajectory errors within the pass, resulting in azimuth defocusing, but also by the flight path. The time-varying baseline error affects the tomographic defocus. Existing self-focusing algorithms cannot sense low-order errors between passes, and therefore cannot effectively process multi-pass tomographic SAR data. Therefore, it is necessary to conduct in-depth research on the self-focusing three-dimensional imaging algorithm of multi-pass tomography SAR on slow-speed unmanned platforms.

Up to now, scholars such as Feng Dong have proposed a three-dimensional self-focusing method for multi-pass tomographic SAR, applying the traditional self-focusing phase error estimation method to the phase error estimation between multi-pass data, realizing the multi-pass SAR of spaceborne multi-pass. Interval phase scaling. The above methods all assume that there are only time-invariant phase errors between passes, and ignore the first-order, second-order and high-order errors that change with azimuth time remaining from the two-dimensional self-focusing, and all consider the errors to be null-invariant. However, in multi-pass tomographic SAR on slow-speed unmanned platforms, there are not only constant-order errors but also time-varying errors between each pass-through 2D image obtained based on the self-focusing of the 2D image, that is, first-order, second-order and higher-order errors, resulting in defocusing of the three-dimensional image. In addition, the impact of track error on the entire scene is spatially variable, especially under wide beams, and this spatial variation cannot be ignored. Therefore, the above method cannot be applied to multi-pass tomography SAR self-focusing processing on slow-speed unmanned platforms. Tebaldini S. and other scholars proposed a tomographic autofocusing method based on phase center dual positioning, which can correct the time-varying vertical heading track error in multi-pass tomographic data. However, this method cannot adapt to the defocus and geometric distortion caused by errors along the heading track. Therefore, this method cannot achieve good three-dimensional focusing for slow-speed unmanned platform multi-pass tomographic SAR. Therefore, the problem of multi-pass tomography SAR self-focusing three-dimensional imaging on slow-speed unmanned platforms still needs to be solved.

Contents of the invention

The technical problem solved by the present invention is to overcome the shortcomings of the existing technology and propose a multi-pass SAR three-dimensional imaging self-focusing method, especially a multi-pass track error based on error dimensionality reduction and track-target position joint estimation. The estimation and compensation method belongs to the technical field of radar three-dimensional imaging residual signal error estimation and compensation. It can effectively realize SAR three-dimensional imaging signal data processing; provide help for obtaining high-resolution three-dimensional images of multi-pass tomography SAR systems.

The method of the present invention is realized through the following technical solutions:

A multi-pass SAR three-dimensional imaging self-focusing method. The steps of the method include:

Step 1: Use the two-dimensional self-focusing track inversion method to estimate and compensate for the track error of each track, specifically as follows:

Assume that the coordinate position of any ground point target within the radar beam coverage is (x ₀ , y ₀ ), then the slant range error when the azimuth time is t _a is ΔR (t _a ; x ₀ , y ₀ ), and the azimuth time is t The track error at time _a is ΔT(t _a )=[Δx(t _a ), Δy(t _a ), Δz(t _a )] ^T , where Δx(t _a ) is the track error in the azimuth direction, Δy (t _a ) is the track error in the ground direction, Δz(t _a ) is the track error in the sky direction, and the relationship between the slant range error and the track error is:

_{ΔR (} t _a ; x _{0 , y 0 )} = _- cosβ _{( t a ;} y ₀ )Δy(t _a )+sinβ(t _a ;x ₀ ,y ₀ )·sinθ(t _a ;x ₀ ,y ₀ )Δz(t _a ) (1)

Among them _{, β ( t a ;}

Suppose there are M ground point targets within the radar beam coverage, and the position vector of the mth ground point target is P _m =[x _m ,y _m ,z _m ] ^T , then the slant range error of the M ground point targets is related to the navigation The relationship between trace errors is expressed as:

R _E (t _a )=M(t _a )·ΔT _c (t _a ) (2)

Among them, R _E (t _a ) is a vector composed of slant range errors of M ground point targets, and M (t _a ) is the line of sight vector of M ground point targets;

Substitute formula (1) into formula (2) to get:

R _E (t _a )=[ΔR (t _a ; x ₁ , y ₁ ) ΔR (t _a ; x ₂ , y ₂ ) … ΔR (t _a ; x _M , y _M )] ^T (3)

The weighted least squares method is used to solve the three-dimensional track error ΔT _c (t _a ) at the orientation time t _a , and the track error estimation and compensation of each orbit are completed.

Step 2: Jointly estimate and compensate the low-order error of the residual track and the target position. The specific method is:

Assume that the unmanned platform has flown a total of N tracks, n=1, 2, 3,...,N, then the nth track is:

Among them, T _i,n (t _a ) is the ideal track of the radar flight nth track; ΔT _c,n (t _a ) is the compensation result of the track error ΔT (t _a ) of the nth track, ΔΔT _cl,n (t _a ) is the current value of the low-order track error of the nth orbit, expressed as A _n is a constant term, B _n is a linear term coefficient, and C _n is a quadratic term coefficient. Then, the echo of the mth ground point target after deskewing is expressed as:

Among them, c is the speed of light, f _c is the bandwidth, f _a is the azimuth Doppler frequency, f _r is the range frequency, B _r is the bandwidth, is the deskew function,/> is the current value of the slope distance history;

Obtain a three-dimensional image based on the deskewed echo When the correct target point position and the correct low-order track error of the track are used to construct the slant range history, the three-dimensional image I _m can obtain the maximum coherent superposition effect, that is, the point target energy accumulation reaches the maximum value/> for:

Therefore, the joint estimation problem is expressed as

In order to achieve a faster solution to the optimization problem, the particle swarm optimization algorithm (PSO) is used. At the same time, PSO jumps out of the local minimum point and obtains the global optimal solution. The fast solution method for this optimization problem is:

Step 1: Select the local image where the feature points are located and obtain the pulse pressure echoes of all feature points;

Step 2: Construct the deskew function H _deramp,m based on the current values of A _all , B _all , C _all , and P _all . After deskewing, the deskewed echo S _deramp,m is obtained;

Step 3: According to equation (7), obtain the amplitude accumulation value I _p,m at each target point, thereby obtaining the energy accumulation index.

Step 4: Estimate the low-order track error parameters. The specific method is:

For the nth track among N tracks, use PSO to estimate the optimal low-order track errors A _n , B _n , C _n and update H _deramp,m , S _deramp,m and

Step 5: Estimate the target position. The specific method is:

For the m-th target point among M ground point targets, use PSO to estimate the position of the target point. And update H _deramp,m , S _deramp,m and/>

Step 6: Repeat steps 2 to 5 until the change in the energy accumulation index is less than the threshold, and obtain the estimation results of the low-order track error ΔΔT _cl,n (t _a ) and the target position.

Step 7. Estimation results of low-order track error ΔΔT _cl,n (t _a ) and target position estimation results Perform joint solution to complete the low-order error and target position estimation and compensation of each residual track, and obtain the compensated multi-pass tomographic SAR data and target position estimation results/>

Step 3: Estimate and compensate the high-order error of the residual track. The specific method is:

After the compensation in step 2, the estimated results of the M point targets are The high-order error of the residual track when the azimuth time of the N tracks is t _a is ΔΔT _ch,n (t _a ), and the slant range error of the N tracks when the azimuth time is t _a is The relationship between slant range error and track error is

R _ch,E (t _a )=M _ch (t _a )·ΔΔT _ch,n (t _a ) (9)

Among them, R _ch,E (t _a ) is a vector composed of slant range errors of M point targets, and M _ch (t _a ) is the line of sight vector of M point targets;

Put formula (1) into formula (9) to get

in, For instant mopping,/> is the instantaneous forward oblique angle, and the two constitute the line of sight direction of the radar observation ground point target;

Use the weighted least squares method to solve the high-order error of the residual track at azimuth time t _a as ΔΔT _ch,n (t _a ), and complete the estimation and compensation of the high-order error of the residual track;

In order to ensure that the residual error is negligible, iteration is carried out based on the method of steps 2 and 3. The number of iterations is 1 to 3 times to obtain the high-order estimation result of the residual track error and the compensated multi-pass tomography SAR data;

Step 4: Imaging the multi-pass tomographic SAR data after compensation in step 3;

Use the obtained track for three-dimensional imaging to obtain good three-dimensional imaging effects. Finally, use the complete track estimation results Carry out three-dimensional precision imaging and use three-dimensional BP algorithm imaging to observe the self-focusing imaging effect.

beneficial effects

Compared with existing UAV SAR three-dimensional imaging methods, this method uses information from multiple point targets and can effectively estimate and compensate for spatial variation errors in large scenes; and this method does not rely on the track measured by the inertial navigation system. Relying only on the echo information collected by the radar system, this has advantages for radar systems without inertial navigation. Through the estimation and compensation of track errors by this method, an image with good focusing effect can be obtained.

Description of the drawings

Figure 1 is the overall flow chart of the multi-pass tomography SAR self-focusing imaging method;

Figure 2 shows the relative track error of each orbit in the simulation experiment. The relative track error of each orbit in the simulation experiment (excluding constant and linear term errors, for the convenience of observation, only the 1st, 11th, 21st and 30th orbits are shown);

Figure 3 shows the three-axis track error of each flight in the simulation experiment;

Figure 4 shows the lattice three-dimensional imaging results, point cloud image;

Figure 5 shows the experimental scene diagram;

Figure 6 shows the three-dimensional imaging results of the measured data, colored by the relative elevation within the scene.

Detailed ways

The implementation of the method of the present invention will be described in detail below with reference to the drawings and examples.

Multi-pass tomography SAR self-focusing imaging method, the steps include:

1. Two-dimensional self-focusing track inversion estimates and compensates the track error of each track;

2. Joint estimation and compensation of low-order error of residual track and target position;

3. Residual track high-order error estimation results and compensation;

4. Imaging multi-pass tomographic SAR data.

Example

Through scene simulation, this section first compares the channel error estimation results in the presence of motion errors and compensated motion errors, and then compares the three-dimensional imaging before self-focusing, two-dimensional self-focusing track inversion and multi-pass tomographic SAR track inversion. result.

Table 2 Azimuth narrow beam distributed SAR simulation system parameters

In the simulation, the track interval between each pass of the ideal track is 3m, and the ideal track is a uniform straight line track with a speed of 8m/s in the positive direction of the Y-axis. On this basis, constant, first-order, and high-order track errors are added to each track. Unknown random constant order track errors and three-axis speed errors (primary track errors) are added to each track. Among them, the actual collected UAV tracks are used as the true relative track errors of each track (excluding constant and linear term errors), as shown in Figure 2. It can be seen that the relative track errors of each flight are random. The three-axis position error (constant order error) is a random number evenly distributed between -0.8~0.8m, while the three-axis speed error is a random number evenly distributed between -0.05~0.05m/s. The added three-axis position error and the three-axis velocity error are shown in the blue square in Figure 3. During the simulation process, only the ideal trajectory is considered known. The three-axis constants, first-order and higher-order errors of each trajectory are all unknown quantities. It can be seen that there are unknown complex time-varying track errors in the multi-pass data under this track.

Since the track is fully sampled and ideally has a uniform baseline, the 3D BP algorithm can be used for 3D imaging processing of the entire scene. The red asterisk in Figure 3 is the error estimation result of the proposed algorithm. It can be seen that the algorithm can correctly invert the track error of each order. The three-dimensional imaging results are shown below. Figure 4(a)~(c) are point cloud images of the three-dimensional imaging results of non-self-focusing, two-dimensional self-focusing track inversion and the proposed algorithm respectively. It can be seen that the processing results of non-self-focusing and two-dimensional self-focusing track inversion are obviously defocused, while the three-dimensional focusing effect of the proposed algorithm is good after self-focusing.

In order to further verify the effectiveness of the multi-pass tomography SAR self-focusing imaging algorithm, an equivalent verification experiment was carried out in a place in Chongqing using a self-developed UAV-borne ultra-wideband/ultra-wide beam SAR system to image architectural scenes, Figure 5 Optical diagram of the experimental scene. Figure 6 shows the point cloud image of the three-dimensional imaging results of each method, which is highly colored with scattering points. Figure 6(a) shows the imaging results before self-focusing, and Figure 6(b) shows the imaging results after using traditional two-dimensional self-focusing processing. Figure 6(c) shows the imaging result after processing by the proposed algorithm. It can be seen from the figure that before self-focusing, the orientation dimension and height dimension are both significantly defocused. After the two-dimensional self-focusing track inversion, the height dimension is still significantly defocused. After self-focusing by the proposed algorithm, the three-dimensional focusing effect of the entire scene is good.

In addition, the three-dimensional image entropy of the full-scene three-dimensional imaging results was evaluated. Before self-focusing, the full-scene three-dimensional image entropy was 20.6988. After using traditional two-dimensional self-focusing, the full-scene three-dimensional image entropy was improved to 20.3046. After using the proposed algorithm, the full-scene three-dimensional image entropy was improved to 20.3046. The scene image entropy was further improved to 18.4591, which shows that the proposed algorithm has the optimal image entropy index, which proves the effectiveness of the proposed method.

To sum up, the above are only preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims (7)

1. A multi-pass SAR three-dimensional imaging self-focusing method, characterized in that the steps of the method include: Step 1: Use the two-dimensional self-focusing track inversion method to estimate and compensate for the track error of each track; Step 2: Jointly estimate and compensate the low-order error of the residual track and the target position; Step 3: Estimate and compensate the high-order error of the residual track to obtain the compensated multi-pass tomographic SAR data; Step 4: Perform three-dimensional imaging on the multi-pass tomographic SAR data compensated in step 3. 2. A multi-pass SAR three-dimensional imaging self-focusing method according to claim 1, the steps of the method include: In the first step, the specific method of estimating and compensating the track error of each orbit using the two-dimensional self-focusing track inversion method is: Assume that the coordinate position of any ground point target within the radar beam coverage is (x ₀ , y ₀ ), then the slant range error when the azimuth time is t _a is ΔR (t _a ; x ₀ , y ₀ ), and the azimuth time is t The track error at time _a is ΔT(t _a )=[Δx(t _a ), Δy(t _a ), Δz(t _a )] ^T , where Δx(t _a ) is the track error in the azimuth direction, Δy (t _a ) is the track error in the ground direction, Δz(t _a ) is the track error in the sky direction, and the relationship between the slant range error and the track error is: _{ΔR (} t _a ; x _{0 , y 0 )} = _- cosβ _{( t a ;} y ₀ )Δy(t _a )+sinβ(t _a ;x ₀ ,y ₀ )·sinθ(t _a ;x ₀ ,y ₀ )Δz(t _a ) (1) Among them _{, β ( t a ;} Suppose there are M ground point targets within the radar beam coverage, and the position vector of the mth ground point target is P _m =[x _m ,y _m ,z _m ] ^T , then the slant range error of the M ground point targets is related to the navigation The relationship between trace errors is expressed as: R _E (t _a )=M(t _a )·ΔT _c (t _a ) (2) Among them, R _E (t _a ) is a vector composed of slant range errors of M ground point targets, and M (t _a ) is the line of sight vector of M ground point targets; Substitute formula (1) into formula (2) to get: R _E (t _a )=[ΔR (t _a ; x ₁ , y ₁ ) ΔR (t _a ; x ₂ , y ₂ ) … ΔR (t _a ; x _M , y _M )] ^T (3) The weighted least squares method is used to solve the three-dimensional track error ΔT _c (t _a ) at the orientation time t _a , and the track error estimation and compensation of each orbit are completed. 3. A multi-pass SAR three-dimensional imaging self-focusing method according to claim 2, the steps of the method include: In the second step, the specific method for jointly estimating and compensating the low-order error of the residual track and the target position is: Assume that the unmanned platform has flown a total of N tracks, n=1, 2, 3,...,N, then the nth track is: Among them, T _i,n (t _a ) is the ideal track of the radar flight nth track; ΔT _c,n (t _a ) is the compensation result of the track error ΔT (t _a ) of the nth track, ΔΔT _cl,n (t _a ) is the current value of the low-order track error of the nth orbit, expressed as A _n is a constant term, B _n is a linear term coefficient, and C _n is a quadratic term coefficient. Then, the echo of the mth ground point target after deskewing is expressed as: Among them, c is the speed of light, f _c is the bandwidth, f _a is the azimuth Doppler frequency, f _r is the range frequency, B _r is the bandwidth, is the deskew function,/> is the current value of the slope distance history; Obtain a three-dimensional image based on the deskewed echo When the correct target point position and the correct low-order track error of the track are used to construct the slant range history, the three-dimensional image I _m can obtain the maximum coherent superposition effect, that is, the point target energy accumulation reaches the maximum value/> for: Therefore, the joint estimation problem is expressed as 4. A multi-pass SAR three-dimensional imaging self-focusing method according to claim 3, the steps of the method include: In order to achieve faster solution, the particle swarm optimization algorithm is used to solve the optimization problem, jump out of the local minimum point, and obtain the global optimal solution. The solution method of this optimization problem is: Step 1: Select the local image where the feature points are located and obtain the pulse pressure echoes of all feature points; Step 2: Construct the deskew function H _deramp,m based on the current values of A _all , B _all , C _all , and P _all . After deskewing, the deskewed echo S _deramp,m is obtained; Step 3: According to equation (7), obtain the amplitude accumulation value I _p,m at each target point, thereby obtaining the energy accumulation index. Step 4: Estimate the low-order track error parameters. The specific method is: For the nth track among N tracks, use PSO to estimate the optimal low-order track errors A _n , B _n , C _n and update H _deramp,m , S _deramp,m and Step 5: Estimate the target position. The specific method is: For the m-th target point among the M ground point targets, use PSO to estimate the position of the target point ~P _m , and update H _deramp,m , S _deramp,m and Step 6: Repeat steps 2 to 5 until the change in the energy accumulation index is less than the threshold, and obtain the estimation results of the low-order track error ΔΔT _cl,n (t _a ) and the target position. Step 7. Estimation results of low-order track error ΔΔT _cl,n (t _a ) and target position estimation results Perform joint solution to complete the low-order error and target position estimation and compensation of each residual track, and obtain the compensated multi-pass tomographic SAR data and target position estimation results/> 5. A multi-pass SAR three-dimensional imaging self-focusing method according to claim 4, the steps of the method include: In the third step, the specific method for estimating and compensating the high-order error of the residual track is: After the compensation in step 2, the estimated results of the M point targets are The high-order error of the residual track when the azimuth time of the N tracks is t _a is ΔΔT _ch,n (t _a ), and the slant range error of the N tracks when the azimuth time is t _a is The relationship between slant range error and track error is R _ch,E (t _a )=M _ch (t _a )·ΔΔT _ch,n (t _a ) (9) Among them, R _ch,E (t _a ) is a vector composed of slant range errors of M point targets, and M _ch (t _a ) is the line of sight vector of M point targets; Put formula (1) into formula (9) to get in, For instant mopping,/> is the instantaneous forward oblique angle, and the two constitute the line of sight direction of the radar observation ground point target; The weighted least squares method is used to solve the high-order error of the residual track at azimuth time t _a as ΔΔT _ch,n (t _a ), and the estimation and compensation of the high-order error of the residual track are completed. 6. A multi-pass SAR three-dimensional imaging self-focusing method according to claim 5, the steps of the method include: The number of iterations is 1 to 3 to obtain the high-order estimation results of the residual track error and the compensated multi-pass tomographic SAR data. 7. A multi-pass SAR three-dimensional imaging self-focusing method according to claim 5 or 6, the steps of the method include: In the fourth step, the method for performing three-dimensional imaging on the multi-pass tomography SAR data after compensation in step three is: The obtained track is used for three-dimensional imaging to obtain the three-dimensional imaging effect. Finally, the complete track estimation result is used Carry out three-dimensional precision imaging and use three-dimensional BP algorithm imaging to observe the self-focusing imaging effect.