Variable parameter video SAR self-registration imaging method suitable for non-ideal trajectory

Abstract

The embodiment of the invention provides a variable-parameter video SAR (Synthetic Aperture Radar) self-registration imaging method suitable for a non-ideal trajectory. Predicting position information of the radar platform; calculating time-varying radar parameters of the radar platform according to the position information of the radar platform; calculating time-varying azimuth sampling parameters of a radar platform according to the resolution of the video SAR and the observation angle of each frame of data; according to the time-varying radar parameter and the time-varying azimuth sampling parameter, acquiring echo data of each frame of the video SAR; and performing two-dimensional delineation frequency modulation processing, wavenumber domain data format correction and focusing processing on the echo data to obtain a focusing result. According to the embodiment of the invention, image focusing, frame image pixel position consistency and frame image resolution matching can be realized in the imaging process, the timeliness of the video SAR can be fully exerted, and the processing efficiency and accuracy are relatively high.

Description

Technical Field

[0001] This application relates to the field of synthetic aperture radar technology, and in particular to a method and apparatus for variable parameter video SAR self-registration imaging applicable to non-ideal trajectories, an electronic device, and a storage medium. Background Technology

[0002] Video Synthetic Aperture Radar (SAR) imaging processes radar echoes received from continuous illumination of a ground area to form a high-frame-rate image stream, providing rich dynamic scattering information for post-processing. Its high resolution and low latency give it advantages unmatched by other detection methods, making it widely used in applications such as moving target detection and real-time ground target tracking. In the military field, video SAR imaging technology is often used for staring imaging and real-time detection and tracking of military targets (such as airport runways, aircraft, missiles, tanks, and ships). In civilian applications, video SAR systems can be used for real-time detection of large-scale disasters such as earthquakes and landslides. These applications inevitably require image fusion, image feature extraction, and target parameter extraction from two or more SAR images acquired by the video SAR system. Efficient and real-time SAR image registration is the most important prerequisite for video SAR systems to fully realize their advantages.

[0003] Currently, most SAR image registration methods are based on SAR image post-processing. This requires obtaining high-frame-rate SAR images and then extracting features to match the reference image with the image to be registered, severely impacting the real-time application of high-frame-rate SAR systems. To address this, researchers have proposed the concept of self-registration imaging: self-registration completes pixel position matching and frame image resolution matching in real time during the imaging process, eliminating the need for additional image-based post-processing. It satisfies the requirements of consistent frame image size, resolution, and target pixel position within the frames during real-time video SAR imaging. To achieve self-registration imaging, radar parameters and azimuth sampling parameters can be adjusted in real time to ensure that the same support domain is cut out in the wavenumber domain distribution of each frame signal. This time-varying parameter criterion relies on the assumption of an ideal linear motion trajectory, which assumes that the radar platform moves at a constant speed along a straight trajectory. However, in actual motion, it is difficult to guarantee an ideal linear trajectory, and motion errors can cause target pixel position shifts and image defocusing. Summary of the Invention

[0004] This application provides a variable parameter video SAR self-registration imaging method applicable to non-ideal trajectories, in order to solve the problem that radar platforms have difficulty completing real-time pixel position matching and frame image resolution matching of frame images under non-ideal motion trajectories.

[0005] Accordingly, embodiments of this application also provide a variable parameter video SAR self-registration imaging device suitable for non-ideal trajectories, an electronic device, and a storage medium to ensure the implementation and application of the above methods.

[0006] To address the aforementioned problems, this application discloses a variable-parameter video SAR self-registration imaging method applicable to non-ideal trajectories, the method comprising:

[0007] Predict the location information of the radar platform;

[0008] Calculate the time-varying radar parameters of the radar platform based on the position information of the radar platform at the next moment;

[0009] Based on the resolution of the video SAR and the observation angle of each frame of data, the time-varying azimuth sampling parameters of the radar platform are calculated.

[0010] Based on the time-varying radar parameters and the time-varying azimuth sampling parameters, obtain the echo data of each frame of the video SAR;

[0011] The echo data is subjected to two-dimensional de-firing and frequency modulation processing to obtain the de-firing and frequency-modulated signal;

[0012] The signal after the de-firing and frequency modulation process is subjected to wavenumber domain data format correction and focusing processing to obtain the focusing result.

[0013] Optionally, predicting the location information of the radar platform includes:

[0014] An autoregressive prediction model for the motion trajectory of the radar platform is established, and the position information of the radar platform at historical moments is obtained. The autoregressive prediction model is expressed by the following formula:

[0015]

[0016] Based on the autoregressive prediction model and the location information of the radar platform at historical moments, calculate the model parameters;

[0017] Based on the autoregressive prediction model and the model parameters, predict the position information of the radar platform at the next moment;

[0018] Where m is the azimuth sampling sequence. N a Let X(m), Y(m), and Z(m) represent the number of azimuth sampling points, respectively. X(m-1)…X(mp), Y(m-1)…Y(mp), and Z(m-1)…Z(mp) represent the three-dimensional position information of the radar platform. α0…α p ,β0…β p, χ0…χ p All of these are the model parameters, and ε(m) represents the error term.

[0019] Optionally, calculating the time-varying radar parameters of the radar platform based on the platform's position information at the next moment includes:

[0020] Based on the position information of the radar platform at the next moment, the pitch angle between the radar platform and the center point of the scene is calculated. The pitch angle between the radar platform and the center point of the scene is obtained by the following formula:

[0021]

[0022] Based on the position information of the radar platform at the next moment, the azimuth angle between the radar platform and the center point of the scene is calculated. The azimuth angle between the radar platform and the center point of the scene is obtained by the following formula:

[0023]

[0024] Based on the elevation angle and azimuth angle between the radar platform and the scene center point, a time-varying radar parameter adjustment law for the radar platform is established. This law is expressed by the following formula:

[0025]

[0026] Among them, f c0 f s0 T p0 K r0 f represents the radar carrier frequency, sampling rate, pulse width, and frequency modulation rate at the synthetic aperture center time of the front-view frame of video SAR, respectively. c (m), f s (m), T p (m), K r (m) represent the time-varying radar parameters that change in real time with the azimuth sampling position. This indicates the elevation angle between the radar platform and the center point of the scene at the center of the synthetic aperture frame in the frontal view.

[0027] Optionally, the observation angle is the angle between the projection direction of the line connecting the radar position at the synthetic aperture center time of each frame of data and the scene center point on the ground and the Y-axis. The calculation of the time-varying azimuth sampling parameters of the radar platform based on the resolution of the video SAR and the observation angle of each frame of data includes:

[0028] Obtain the radar wavelength of the radar platform;

[0029] Based on the resolution of the video SAR, the radar wavelength of the radar platform, the angle between the projection direction of the line connecting the radar position at the center time of the synthetic aperture of each frame and the scene center point on the ground and the Y-axis, and the projection distance of the shortest slant range of the frontal side view frame on the ground, the synthetic aperture length is calculated using the following formula:

[0030]

[0031] Obtain the equivalent motion velocity and pulse repetition frequency of the radar platform;

[0032] Based on the synthetic aperture length, the equivalent velocity of the radar platform, and the pulse repetition frequency, the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth is determined. The number of azimuth sampling points contained within the effective support domain wavenumber bandwidth is obtained by the following formula:

[0033]

[0034] Based on the synthetic aperture length, the projection length of the shortest slant range of the frontal side view frame on the ground, and the angle between the projection direction of the line connecting the radar position and the scene center point at the center of each frame on the ground and the Y-axis, calculate the azimuth angle corresponding to the first azimuth sampling point included in the effective support domain wavenumber bandwidth.

[0035]

[0036] A constraint condition is established for the azimuth angle corresponding to the starting point of azimuth sampling when the radar platform collects data for each frame. The constraint condition is expressed by the following formula:

[0037]

[0038] Based on the azimuth angle corresponding to the first azimuth sampling point included in the effective support domain wavenumber bandwidth, the azimuth angle corresponding to the azimuth sampling start point when the radar platform collects data for each frame, the projection length of the shortest slant range of the frontal side-view frame on the ground, the motion speed, and the pulse repetition frequency, the number of azimuth sampling points that the radar platform collects data from is greater than the number of azimuth sampling points included in the effective support domain wavenumber bandwidth is calculated. This number of azimuth sampling points that the radar platform collects data from is greater than the number of azimuth sampling points included in the effective support domain wavenumber bandwidth is obtained using the following formula:

[0039]

[0040] Based on the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth and the number of azimuth sampling points that the radar platform acquires when collecting data that exceed the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth, the time-varying azimuth sampling parameters of the radar platform are calculated. The time-varying azimuth sampling parameters are obtained using the following formula:

[0041] N a =N aref +N a,start

[0042] Where, ρ a Where L is the resolution of the video SAR, λ is the synthetic aperture length, θ0 is the angle between the projection direction of the line connecting the radar position and the scene center point at the center of the synthetic aperture in each frame onto the ground and the Y-axis, R0 is the projection distance of the shortest slant range on the ground in the frontal side-view frame, and N is the resolution of the video SAR. aref The effective support domain wavenumber bandwidth contains the number of azimuth sampling points, PRF is the pulse repetition frequency, V is the equivalent motion velocity, and θ is the number of azimuth sampling points within the effective support domain wavenumber bandwidth. lower θ is the azimuth angle corresponding to the first azimuth sampling point contained within the effective support domain wavenumber bandwidth. start K represents the azimuth angle corresponding to the starting point of azimuth sampling when the radar platform collects data for each frame. y N represents the range wavenumber. r N represents the number of distance sampling points. a N represents the number of time-varying azimuth sampling points. a,start This indicates the number of azimuth samples that the radar platform collects when it collects data, which is more than the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth.

[0043] Optionally, obtaining the echo data of each frame of the video SAR based on the time-varying radar parameters and the time-varying azimuth sampling parameters includes:

[0044] Based on the time-varying radar parameters and the time-varying azimuth sampling parameters, electromagnetic wave signals are transmitted to the target area through the radar platform to obtain echo data of each frame of the video SAR.

[0045] Optionally, the step of performing two-dimensional demodulation and frequency modulation processing on the echo data to obtain the demodulated and frequency-modulated signal includes:

[0046] Perform a Fourier transform on the echo data along the range direction to obtain range frequency domain echo data;

[0047] Construct a frequency domain filter, which is expressed by the following formula:

[0048]

[0049] Multiplying the frequency domain filter and the distance frequency domain echo data yields the signal after line demodulation and frequency modulation processing. The signal after line demodulation and frequency modulation processing is obtained by the following formula:

[0050]

[0051] Among them, f τ Represents distance frequency, j represents the imaginary unit, R ref S1 represents the distance from the radar platform to the center of the scene, c represents the speed of light, S2 represents the signal after the de-firing and frequency modulation processing, S1 represents the range-frequency domain echo data, and R... t τ represents the distance between the radar platform and any target point in the scene, and τ represents the range-major time.

[0052] Optionally, the step of performing wavenumber domain data format correction and focusing processing on the signal after the de-firing and frequency modulation processing to obtain the focusing result includes:

[0053] Performing a first-order Taylor expansion on the scene center point yields the distance difference between the distance from the radar platform to the scene center and the distance between the radar platform and any target point in the scene. This distance difference is calculated using the following formula:

[0054]

[0055] The signal after de-firing and frequency modulation can be discretized as follows:

[0056] ss l (i,m)=exp{-j(K y (i)y t +K x (i,m)x t )}

[0057] The sampling sequence for azimuth resampling is determined, and the azimuth resampling sampling sequence is obtained by the following formula:

[0058]

[0059] The resampled signal is obtained, and the resampled signal is represented as follows:

[0060] ss lp (i,m)=exp{-j(K yref (i1)y t +K xref (m1)x t )}

[0061] Perform range IFFT and azimuth FFT on the resampled signal to obtain the focusing result;

[0062] Where, x t and y t K represents the coordinates of the target point along the azimuth and range directions, respectively. y (i) and K x (i,m) represent the range wavenumber and azimuth wavenumber, respectively. l (i,m) represents the discretized echo signal after de-firing and frequency modulation processing, K xup K represents the effective support domain wavenumber distribution. x The maximum value, K xlow K represents the effective support domain wavenumber distribution. x The minimum value of K yref K represents the uniformly sampled sequence along the range after resampling. xref This represents a uniform sampling sequence in the azimuth direction after resampling.

[0063] This application also discloses an electronic device, including: a processor; and a memory storing executable code thereon, which, when executed, causes the processor to execute any of the variable parameter video SAR self-registration imaging methods for non-ideal trajectories as described in any of the embodiments of this application.

[0064] This application also discloses one or more machine-readable media storing executable code, which, when executed, causes a processor to perform a variable-parameter video SAR self-registration imaging method for non-ideal trajectories as described in any one of the embodiments of this application.

[0065] Compared with the prior art, the embodiments of this application have the following advantages:

[0066] In this embodiment, the location information of the radar platform is predicted; based on the location information of the radar platform at the next moment, the time-varying radar parameters of the radar platform are calculated; based on the resolution of the video SAR and the observation angle of each frame of data, the time-varying azimuth sampling parameters of the radar platform are calculated; based on the time-varying radar parameters and the time-varying azimuth sampling parameters, the echo data of the video SAR is obtained; the echo data is subjected to two-dimensional demodulation frequency modulation processing, wavenumber domain data format correction, and focusing processing to obtain the focusing result. This embodiment, by predicting the motion trajectory of the radar platform, can obtain the location information of the radar platform, and then design time-varying radar parameters based on the location information of the radar platform to offset the influence of motion errors. It can achieve image focusing, consistent pixel positions in frame images, and frame image resolution matching during the imaging process, without the need for image post-processing to achieve image focusing and image registration. This fully utilizes the timeliness of video SAR and has high processing efficiency and accuracy. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating the steps of an embodiment of a variable parameter video SAR self-registration imaging method applicable to non-ideal trajectories according to this application;

[0068] Figure 2 This is a schematic diagram of a non-ideal motion model provided in an embodiment of this application;

[0069] Figure 3 This is a schematic diagram illustrating the process of converting non-uniform sampling in the azimuth and range directions to uniform sampling, as provided in an embodiment of this application.

[0070] Figure 4 This is a schematic diagram of a frame data interpolation region at different observation perspectives provided in an embodiment of this application;

[0071] Figure 5 This is a schematic diagram of a frame data interpolation region based on time-varying parameters provided in an embodiment of this application;

[0072] Figure 6 This is a schematic diagram of the imaging results of a scene edge point target (-50m, 50m) from different observation angles, provided in an embodiment of this application.

[0073] Figure 7 This is a schematic diagram illustrating the relative pixel changes of a scene edge point target (-50m, 50m) in different frame images, as provided in an embodiment of this application.

[0074] Figure 8 This is a schematic diagram of the imaging results of a scene edge point target (-50m, 50m) at a 15-degree oblique angle when using the traditional phase gradient autofocus method;

[0075] Figure 9 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Detailed Implementation

[0076] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] To achieve self-registration of video SAR under non-ideal conditions, this application proposes a variable parameter video SAR self-registration imaging method applicable to non-ideal trajectories. This method aims to correct wavenumber domain deformation caused by motion errors by adjusting the shape of each frame signal of the non-ideal motion trajectory in the wavenumber domain through parameter changes, and to perform real-time motion compensation using variable parameters.

[0078] The core idea of ​​this application is to achieve consistent pixel positions of the target in frame images and matching frame image resolutions. This requires ensuring that the frame data distribution along the azimuth direction in the wavenumber domain has a uniform wavenumber width, and similarly, that the distribution along the range direction in the wavenumber domain also has a uniform wavenumber width. This is mainly achieved by adjusting the distribution of frame data in the wavenumber domain using time-varying radar parameters and time-varying azimuth sampling parameters to ensure that the wavenumber width of the oblique-view frame data is consistent with that of the front-view frame data. By adjusting the distribution of frame data along the range and azimuth directions in the wavenumber domain using time-varying radar parameters, the influence of motion errors is offset, resulting in an approximately ideal wavenumber distribution. It should be noted that since the non-ideal motion model cannot know the radar platform's position information in advance, it is necessary to predict the radar platform's position information at the next moment. This prediction is used to design time-varying radar parameters and obtain frame data with uniform wavenumber widths.

[0079] Reference Figure 1 This is a flowchart illustrating the steps of an embodiment of a variable-parameter video SAR self-registration imaging method applicable to non-ideal trajectories, as described in this application, including the following steps:

[0080] Step 101: Predict the location information of the radar platform;

[0081] In this embodiment of the application, since the non-ideal motion model cannot know the position information of the radar platform at each pulse transmission moment in advance, it is necessary to predict the position information of the radar platform at the next pulse transmission moment.

[0082] Step 102: Calculate the time-varying radar parameters of the radar platform based on the position information of the radar platform at the next moment;

[0083] In this embodiment of the application, it is necessary to design the time-varying radar parameters of the radar platform based on the predicted location information of the radar platform, so as to obtain frame data with equal wavenumber width based on the time-varying radar parameters.

[0084] Step 103: Calculate the time-varying azimuth sampling parameters of the radar platform based on the resolution of the video SAR and the observation angle of each frame of data;

[0085] After the radar parameters are adjusted, the azimuth wavenumber support domain width of the oblique-view frame data is the same as that of the orthogonal side-view frame. However, the parameter that truly determines the image resolution and target imaging position of the oblique-view frame is the wavenumber bandwidth of the effective wavenumber support domain. The effective support domain wavenumber bandwidth of the oblique-view frame data is significantly reduced compared to the effective bandwidth of the orthogonal side-view frame data.

[0086] Therefore, in this embodiment of the application, it is necessary to design the time-varying azimuth sampling parameters of the radar platform based on the resolution required by the video SAR application, so as to ensure that the wavenumber support domain range of the oblique-view frame data can also be cut out with the same size as that of the front-view frame data by expanding the wavenumber support domain range of the oblique-view frame data during the azimuth processing.

[0087] Step 104: Obtain echo data of each frame of the video SAR based on the time-varying radar parameters and the time-varying azimuth sampling parameters;

[0088] In the embodiments of this application, the time-varying radar parameters and time-varying azimuth sampling parameters designed based on the aforementioned steps can be used to obtain echo data of each frame of video SAR.

[0089] Step 105: Perform two-dimensional demodulation and frequency modulation processing on the echo data of each frame to obtain the demodulated and frequency-modulated signal;

[0090] Step 106: Perform wavenumber domain data format correction and focusing processing on the signal after the de-firing and frequency modulation processing to obtain the focusing result.

[0091] In this embodiment, by performing wavenumber domain data format correction and focusing processing on the echo signal, a focusing result can be obtained in which the pixel position of the target is consistent and the frame image resolution is matched. Specifically, the focusing result is a single-frame SAR image in video SAR, that is, an image sequence composed of multiple frames of SAR images (focusing results) acquired in continuous time, forming video SAR.

[0092] This application embodiment can obtain the position information of the radar platform by predicting the radar platform's motion trajectory. Then, time-varying radar parameters are designed based on the radar platform's position information to offset the effects of motion errors. This can achieve image focusing, consistent pixel positions in frame images, and frame image resolution matching during the imaging process. There is no need to achieve image focusing and image registration based on post-processing of the image. This can give full play to the timeliness of video SAR and has high processing efficiency and accuracy.

[0093] In one embodiment of this application, step 101, predicting the location information of the radar platform, includes:

[0094] An autoregressive prediction model for the motion trajectory of the radar platform is established, and the position information of the radar platform at historical moments is obtained. The autoregressive prediction model is expressed by the following formula:

[0095]

[0096] Based on the autoregressive prediction model and the location information of the radar platform at historical moments, calculate the model parameters;

[0097] Based on the autoregressive prediction model and the model parameters, predict the position information of the radar platform at the next moment;

[0098] Where m is the azimuth sampling sequence. N a Let X(m), Y(m), and Z(m) represent the number of azimuth sampling points, respectively. X(m-1)…X(mp), Y(m-1)…Y(mp), and Z(m-1)…Z(mp) represent the three-dimensional position information of the radar platform. α0…α p ,β0…β p , χ0…χ p All of these are the model parameters, and ε(m) represents the error term.

[0099] In this embodiment of the application, it is necessary to establish an autoregressive prediction model for the motion trajectory of the radar platform, as shown in the following formula (1):

[0100]

[0101] Where X(m), Y(m), and Z(m) are the position information of the radar platform, X(m-1)…X(mp), Y(m-1)…Y(mp), and Z(m-1)…Z(mp) are the position information of the radar platform at historical moments, and α0…α p ,β0…β p , χ0…χ p All of these are the model parameters, and ε(m) represents the error term.

[0102] Specifically, X(m), Y(m), and Z(m) represent the three-dimensional positions of the radar platform, respectively; X(m-1)…X(mp), Y(m-1)…Y(mp), and Z(m-1)…Z(mp) represent the position components of the radar platform along the azimuth, range, and elevation directions at the previous p sampling positions at the m-th sampling position, respectively; α0…α p ,β0…β p , χ0…χp These represent the model parameters of the autoregressive prediction model along the azimuth, distance, and elevation directions, respectively.

[0103] In this embodiment, the position information of the radar platform at a historical moment is the inertial navigation information collected by the inertial navigation system (INS) for a period of time before the formal data collection. Substituting the position information of the radar platform at a historical moment into formula (1), the equation can be solved by the least squares method or the maximum likelihood estimation method, and the model parameters can be obtained.

[0104] Substituting the obtained model parameters into the autoregressive prediction model (Formula (1)), the autoregressive model is obtained. Based on the autoregressive model, when the radar platform moves to the position at time p, the position information at time p+1 (the next time) can be predicted using the position information of the radar platform at the previous p times (historical times). Similarly, when the radar platform moves to the position at time p+1, the position information at time p+2 (the next time) can be predicted using the same number of radar platform position information. It should be noted that the relevant information used here to predict the position information of the radar platform at the next time is all inertial navigation information collected by the inertial navigation system.

[0105] This application embodiment uses an autoregressive model to predict the radar platform's position at the next moment based on inertial navigation information when the radar platform moves to its current position.

[0106] In one embodiment of this application, step 102, calculating the time-varying radar parameters of the radar platform based on the position information of the radar platform at the next moment, includes:

[0107] Based on the position information of the radar platform at the next moment, the pitch angle between the radar platform and the center point of the scene is calculated. The pitch angle between the radar platform and the center point of the scene is obtained by the following formula:

[0108]

[0109] Based on the position information of the radar platform at the next moment, the azimuth angle between the radar platform and the center point of the scene is calculated. The azimuth angle between the radar platform and the center point of the scene is obtained by the following formula:

[0110]

[0111] Based on the elevation angle and azimuth angle between the radar platform and the scene center point, a time-varying radar parameter adjustment law for the radar platform is established. This law is expressed by the following formula:

[0112]

[0113] Among them, f c0 f s0 T p0 K r0 f represents the radar carrier frequency, sampling rate, pulse width, and frequency modulation rate at the synthetic aperture center time of the front-view frame of video SAR, respectively. c (m), f s (m), T p (m), K r (m) represent the time-varying radar parameters that change in real time with the azimuth sampling position. This indicates the elevation angle between the radar platform and the center point of the scene at the center of the synthetic aperture frame in the frontal view.

[0114] In this embodiment, based on the predicted position information of the radar platform at the next moment, the position of the radar platform at the next moment [X(m),Y(m),Z(m)] can be obtained. Based on the position of the radar platform at the next moment [X(m),Y(m),Z(m)], the pitch angle between the radar platform and the center point of the scene can be calculated. Specifically, it is calculated using the following formula (2):

[0115]

[0116] Furthermore, based on the radar platform's position [X(m),Y(m),Z(m)] at the next moment, the azimuth angle θ between the radar platform and the scene center point can be calculated, specifically using the following formula (3):

[0117]

[0118] Subsequently, based on the aforementioned elevation angle between the radar platform and the center point of the scene... The azimuth angle θ between the radar platform and the center point of the scene can be used to establish the time-varying radar parameter adjustment law of the radar platform, as shown in the following formula (4):

[0119]

[0120] Among them, f c0 f s0 T p0 K r0 f represents the radar carrier frequency, sampling rate, pulse width, and frequency modulation at the center of the synthetic aperture in the frontal side view frame, respectively. c (m), f s (m), T p (m), K r(m) represent the time-varying radar parameters that change in real time with the azimuth sampling position. This indicates the elevation angle between the radar platform and the center point of the scene at the center of the synthetic aperture frame in the frontal view.

[0121] This application embodiment designs time-varying radar parameters so that the radar platform can offset the effects of motion errors under non-ideal motion trajectories, thereby obtaining an approximately ideal frame data distribution and realizing the correction of frame data deformation in the wavenumber domain.

[0122] In one embodiment of this application, the observation angle is the angle between the projection direction of the line connecting the radar position at the center time of each frame's synthetic aperture and the scene center point on the ground and the Y-axis. Step 103, calculating the time-varying azimuth sampling parameters of the radar platform based on the resolution of the video SAR and the observation angle of each frame's data, includes:

[0123] Based on the resolution of the video SAR, the radar wavelength of the radar platform, the angle between the projection direction of the line connecting the radar position at the center time of the synthetic aperture of each frame and the scene center point on the ground and the Y-axis, and the projection distance of the shortest slant range of the frontal side view frame on the ground, the synthetic aperture length is calculated using the following formula:

[0124]

[0125] Obtain the equivalent motion velocity and pulse repetition frequency of the radar platform;

[0126] Based on the synthetic aperture length, the equivalent velocity of the radar platform, and the pulse repetition frequency, the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth is determined. The number of azimuth sampling points contained within the effective support domain wavenumber bandwidth is obtained by the following formula:

[0127]

[0128] Based on the synthetic aperture length, the projection length of the shortest slant range of the frontal side view frame on the ground, and the angle between the projection direction of the line connecting the radar position and the scene center point at the center of each frame on the ground and the Y-axis, calculate the azimuth angle corresponding to the first azimuth sampling point included in the effective support domain wavenumber bandwidth.

[0129]

[0130] A constraint condition is established for the azimuth angle corresponding to the starting point of azimuth sampling when the radar platform collects data for each frame. The constraint condition is expressed by the following formula:

[0131]

[0132] Based on the azimuth angle corresponding to the first azimuth sampling point included in the effective support domain wavenumber bandwidth, the azimuth angle corresponding to the azimuth sampling start point when the radar platform collects data for each frame, the projection length of the shortest slant range of the frontal side-view frame on the ground, the motion speed, and the pulse repetition frequency, the number of azimuth sampling points that the radar platform collects data from is greater than the number of azimuth sampling points included in the effective support domain wavenumber bandwidth is calculated. This number of azimuth sampling points that the radar platform collects data from is greater than the number of azimuth sampling points included in the effective support domain wavenumber bandwidth is obtained using the following formula:

[0133]

[0134] Based on the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth and the number of azimuth sampling points that the radar platform acquires when collecting data that exceed the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth, the time-varying azimuth sampling parameters of the radar platform are calculated. The time-varying azimuth sampling parameters are obtained using the following formula:

[0135] N a =N aref +N a,start

[0136] Where, ρ a Where L is the resolution of the video SAR, λ is the synthetic aperture length, θ0 is the angle between the projection direction of the line connecting the radar position and the scene center point at the center of the synthetic aperture in each frame onto the ground and the Y-axis, R0 is the projection distance of the shortest slant range on the ground in the frontal side-view frame, and N is the resolution of the video SAR. aref The effective support domain wavenumber bandwidth contains the number of azimuth sampling points, PRF is the pulse repetition frequency, V is the equivalent motion velocity, and θ is the number of azimuth sampling points within the effective support domain wavenumber bandwidth. lower θ is the azimuth angle corresponding to the first azimuth sampling point contained within the effective support domain wavenumber bandwidth. start K represents the azimuth angle corresponding to the starting point of azimuth sampling when the radar platform collects data for each frame. y N represents the range wavenumber. r N represents the number of distance sampling points. a N represents the number of time-varying azimuth sampling points. a,start This indicates the number of azimuth samples that the radar platform collects when it collects data, which is more than the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth.

[0137] In this embodiment, it is necessary to first obtain the radar wavelength λ of the radar platform, and the angle θ0 between the projection direction of the line connecting the radar position and the scene center point at the synthetic aperture center time of each frame and the Y-axis. It should be noted that the radar position refers to the position of the radar platform.

[0138] The resolution ρ required for video SAR applications a The radar wavelength λ of the radar platform, and the angle θ0 between the projection direction of the line connecting the radar position and the scene center point at the center of each frame onto the ground and the Y-axis, are used to calculate the synthetic aperture length L using the following formula (5):

[0139]

[0140] Subsequently, it is also necessary to obtain the equivalent motion velocity V and pulse repetition frequency PRF of the radar platform in order to calculate the number of azimuth sampling points N contained in the effective support domain wavenumber bandwidth using the following formula (6). aref :

[0141]

[0142] It is also necessary to calculate the azimuth angle θ corresponding to the first azimuth sampling point contained within the effective support domain wavenumber bandwidth based on the synthetic aperture length and the projection angle θ0 of the oblique angle on the ground. lower Specifically, it is calculated using the following formula (7):

[0143]

[0144] Wherein, L is the length of the synthesized aperture.

[0145] To ensure that the oblique-view frame data can cut out a support domain of the same size as the frontal-view frame data, it is necessary to specify the azimuth angle θ corresponding to the azimuth sampling start point when the radar platform collects each frame of data. start The constraints are as shown in formula (8):

[0146]

[0147] Where, θ start K represents the azimuth angle corresponding to the starting point of azimuth sampling when the radar platform collects data for each frame. y N represents the range wavenumber. r This indicates the number of sampling points in the distance direction.

[0148] Finally, based on the number N of azimuth sampling points contained within the effective support domain wavenumber bandwidth. aref The number N that increases the number of azimuth samples compared to the number of azimuth sampling points within the effective support domain wavenumber bandwidth when the radar platform acquires data. a,start Calculate the time-varying azimuth sampling parameters N of the radar platform. a It is calculated using the following formula (9):

[0149] N a =N aref +N a,start (9)

[0150] Specifically, N is the number of azimuth sampling points that the radar platform collects when it acquires data, exceeding the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth. a,start The following formula (10) needs to be satisfied:

[0151]

[0152] The embodiments of this application can expand the wavenumber support domain range of the oblique view frame data by designing time-varying azimuth sampling parameters, thereby ensuring that the oblique view frame data can also cut out a wavenumber support domain range of the same size as the orthogonal view frame data.

[0153] In one embodiment of this application, step 104, obtaining echo data of each frame of the video SAR based on the time-varying radar parameters and the time-varying azimuth sampling parameters, includes:

[0154] Based on the time-varying radar parameters and the time-varying azimuth sampling parameters, electromagnetic wave signals are transmitted to the target area through the radar platform to obtain echo data of each frame of the video SAR.

[0155] Based on the time-varying radar parameters and time-varying azimuth sampling parameters designed in steps 101, 102 and 103, the radar platform transmits electromagnetic wave signals to the target area to acquire video SAR echo data S0.

[0156] It should be noted that the electromagnetic wave signals emitted by the radar platform due to non-ideal motion trajectories are different from the electromagnetic wave signals emitted by the traditional radar platform with fixed parameters. In the embodiments of this application, the radar parameters emitted by the radar platform are time-varying.

[0157] In one embodiment of this application, step 105, performing two-dimensional demodulation and frequency modulation processing on the echo data to obtain the demodulated and frequency-modulated signal, includes:

[0158] Perform a Fourier transform on the echo data along the range direction to obtain range frequency domain echo data;

[0159] Construct a frequency domain filter, which is expressed by the following formula:

[0160]

[0161] Multiplying the frequency domain filter and the distance frequency domain echo data yields the signal after line demodulation and frequency modulation processing. The signal after line demodulation and frequency modulation processing is obtained by the following formula:

[0162]

[0163] Among them, f τRepresents distance frequency, j represents the imaginary unit, R ref S1 represents the distance from the radar platform to the center of the scene, c represents the speed of light, S2 represents the signal after the de-firing and frequency modulation processing, S1 represents the range-frequency domain echo data, and R... t τ represents the distance between the radar platform and any target point in the scene, and τ represents the range-major time.

[0164] In this embodiment of the application, the echo data S0 obtained in step 104 is subjected to an Nr-point Fourier transform along the range direction to obtain the range-frequency domain echo data S1, which is the range-frequency domain echo data.

[0165] Subsequently, the frequency domain filter ss is constructed according to the following formula (11). ref (f τ Two-dimensional demodulation and frequency modulation processing are performed on the range-frequency domain echo data S1 (m).

[0166]

[0167] Among them, f τ Represents distance frequency, j represents the imaginary unit, R ref The distance from the radar platform to the center of the scene is represented by c, where c represents the speed of light, and K represents the distance from the radar platform to the center of the scene. r (m) represents the time-varying frequency modulation (one of the time-varying radar parameters) that changes in real time with the azimuth sampling position, f c (m) represents the time-varying radar carrier frequency (one of the time-varying radar parameters) that changes in real time with the azimuth sampling position.

[0168] Specifically, the distance frequency f τ The distance R from the radar platform to the center of the scene is obtained by the following formula (12). ref It can be obtained by the following formula (13):

[0169]

[0170] in, This represents the distance-oriented sampling point sequence.

[0171] The frequency domain filter (formula (11)) is then multiplied by the distance frequency domain echo data to output the signal S2 after the de-firing and frequency modulation processing is completed, as shown in the following formula (14):

[0172]

[0173] Among them, R t T represents the distance between the radar platform and any target point in the scene, τ represents the range-major time t. p (m) represents the time-varying pulse width (one of the time-varying radar parameters) that changes in real time with the azimuth sampling position.

[0174] In one embodiment of this application, step 106, performing wavenumber domain data format correction and focusing processing on the signal after de-firing and frequency modulation processing to obtain a focusing result, includes:

[0175] Performing a first-order Taylor expansion on the scene center point yields the distance difference between the distance from the radar platform to the scene center and the distance between the radar platform and any target point in the scene. This distance difference is calculated using the following formula:

[0176]

[0177] The signal after de-firing and frequency modulation can be discretized as follows:

[0178] ss l (i,m)=exp{-j(K y (i)y t +K x (i,m)x t )}

[0179] The sampling sequence for azimuth resampling is determined, and the azimuth resampling sampling sequence is obtained by the following formula:

[0180]

[0181] The resampled signal is acquired, and the resampled signal is represented as follows:

[0182] ss lp (i,m)=exp{-j(K yref (i1)y t +K xref (m1)x t )}

[0183] Perform range IFFT and azimuth FFT on the resampled signal to obtain the focusing result;

[0184] Where, x t and y t K represents the coordinates of the target point along the azimuth and range directions, respectively. y (i) and K x (i,m) represent the range wavenumber and azimuth wavenumber, respectively. l (i,m) represents the discretized echo signal after de-firing and frequency modulation processing, K xup K represents the effective support domain wavenumber distribution. x The maximum value, K xlow K represents the effective support domain wavenumber distribution. x The minimum value of K.yref K represents the uniformly sampled sequence along the range after resampling. xref This represents a uniform sampling sequence in the azimuth direction after resampling.

[0185] In this embodiment of the application, based on the planar wavefront assumption of electromagnetic wave transmission, R t (m) Perform a first-order Taylor expansion with the scene center as the reference point, R t (m)-R ref (m)(the distance difference between the radar platform and the center of the scene and the distance between the radar platform and any target point in the scene) is expressed as the following formula (15):

[0186]

[0187] Where, x t and y t These represent the coordinates of the target point along the azimuth and range directions, respectively.

[0188] Based on the plane wavefront assumption and the radar time-varying parameter criterion of formula (4), the signal S2 after the de-firing and frequency modulation processing is discretized as ss l (i,m), as shown in formula (16):

[0189] ss l (i,m)=exp{-j(K y (i)y t +K x (i,m)x t (16)

[0190] Among them, K y (i) and K x (i,m) represent the range wavenumber and azimuth wavenumber, respectively.

[0191] Specifically, the range wavenumber K y (i) and azimuth wave number K x (i,m) are expressed by the following formulas (17) and (18):

[0192]

[0193] K x (i,m)=K y (i)tanθ(m)(18)

[0194] Formulas (17) and (18) show that the target echo signal has a wedge-shaped distribution in the wavenumber domain, so range interpolation correction is not required, and azimuth interpolation can be performed directly. Based on step 103, in the azimuth processing, by expanding the wavenumber support domain range of the oblique view frame data, it is ensured that the oblique view frame data can cut out a support domain range of the same size as the frontal view frame data. The sampling sequence of azimuth resampling can be expressed as the following formula (19):

[0195]

[0196] Among them, K xup K represents the effective support domain wavenumber distribution. x The maximum value, K xlow K represents the effective support domain wavenumber distribution. x The minimum value.

[0197] Based on the sampling sequence of azimuth resampling, the resampled signal can be obtained, which is specifically represented by the following formula (20):

[0198] ss lp (i,m)=exp{-j(K yref (i1)y t +K xref (m1)x t )} (20)

[0199] Among them, K yref K represents the uniformly sampled sequence along the range after resampling. xref This represents a uniform sampling sequence in the azimuth direction after resampling.

[0200] The resampled signal is subjected to range IFFT (Inverse Fast Fourier Transform) and azimuth FFT (Fast Fourier Transform) to obtain the final focusing result.

[0201] The self-registration method provided in this application uses time-varying radar parameters and time-varying azimuth sampling parameters to adjust the shape of the frame data in the wavenumber domain under non-ideal motion trajectories of the radar platform, thereby correcting the wavenumber domain deformation caused by motion errors. It can solve the problems of image defocus, frame image geometric mismatch, and frame image resolution inconsistency during the imaging process. It does not require image focusing and image registration based on image post-processing, and can give full play to the timeliness of video SAR. It is an efficient and accurate SAR data processing method.

[0202] To enable those skilled in the art to better understand the technical solution of this application, a simulation example is provided here:

[0203] Reference Figure 2 This diagram illustrates a non-ideal motion model provided in an embodiment of this application. It assumes the radar platform's flight trajectory follows a sinusoidal trajectory (actual trajectory) based on an ideal trajectory at a fixed altitude of 2000m, with a trajectory amplitude of 5m and an angular frequency of 0.1 rad / s. The video SAR observation area is a square array scene with a radius of 100m × 100m, with a frontal side-view frame azimuth resolution of 0.21m and a range resolution of 0.18m. 25 targets to be observed are uniformly distributed within this area. Specifically, the main parameters of this simulation example are shown in Table 1 below:

[0204] Table 1 Simulation parameters of the non-ideal trajectory self-registration imaging method

[0205] parameter numerical values parameter numerical values Carrier frequency (GHz) 37.5 Frequency modulation (GHz / s) <![CDATA[1.5×10 6 ]]> Bandwidth (MHz) 750 Synthetic aperture length (m) 539.85 Sampling rate (MHz) 900 Pitch angle (°) at the center of the frame viewed from the side 3.8 Platform speed (m / s) 150 Pulse width (μs) 0.5 Pulse repetition frequency (Hz) 1000 Speed ​​of light (m / s) <![CDATA[3×10 8 ]]> Central slope distance (km) 30 Distance sampling points 9000 Motion trajectory amplitude (m) 5 Number of azimuth sampling points for 25-degree oblique frame 9364 Trajectory angular frequency (rad / s) 0.1 Number of azimuth interpolation points 3600

[0206] It should be noted that the carrier frequency, sampling rate, and pulse width in Table 1 are parameters at the center time of the synthetic aperture of the frontal side view frame. The parameters at other times need to be calculated based on the time-varying radar parameters designed above, which will not be elaborated here.

[0207] Reference Figure 3 This illustration shows a schematic diagram of a non-uniform sampling to uniform sampling method in the azimuth and range directions provided in an embodiment of this application. Figure 3 As can be seen from the above, after adjusting the time-varying radar parameters obtained from the aforementioned steps, the uniform distribution of each frame signal in the range and azimuth directions has been achieved. The white circle represents the first frame signal before adjustment, the black circle represents the first frame signal after adjustment, the white square represents the Kth frame signal before adjustment, and the black square represents the Kth frame signal after adjustment. The origin O in the coordinate system can be understood as the scene center point in the embodiment of this application.

[0208] Reference Figure 4 This illustration shows a schematic diagram of a frame data interpolation region at different observation viewpoints provided in an embodiment of this application. (Refer to...) Figure 5 The diagram illustrates a frame data interpolation region based on time-varying azimuth sampling parameters provided in an embodiment of this application.

[0209] Specifically, such as Figure 3 As shown, after adjustment based on the time-varying radar parameter adjustment law, the echo signal has a wedge-shaped distribution in the wavenumber domain. At this time, there is no need to perform range interpolation correction. Instead, the rectangular data of each frame signal can be cut out along the azimuth direction, and then the signal focusing processing along the azimuth direction can be performed.

[0210] Despite the azimuth wavenumber support domain width ΔK of the squint frame after radar parameter adjustment x2 The width ΔK of the azimuth wavenumber support domain of the frontal side view frame x1The parameters are the same, but the one that truly determines the resolution of the strabismic frame image and the target imaging position is the wavenumber bandwidth ΔK of the effective wavenumber support domain. x3 The effective support domain wavenumber bandwidth ΔK of the strabismus frame signal x3 The effective bandwidth ΔK of the correct side-view frame x1 Significantly reduced, such as Figure 4 As shown. Therefore, in step 104, the wavenumber support domain range ΔK of the oblique view frame is expanded during azimuth processing. x4 To ensure that the oblique view frame can cut out a support domain of the same size as the normal side view frame, such as... Figure 5 As shown, ΔK at this time x1 =ΔK x3 =ΔK x4 .

[0211] Reference Figure 6 This diagram illustrates the imaging results of a scene edge point target (-50m, 50m) from different viewing angles, according to an embodiment of this application. Specifically, Figure 6 The self-registration method provided in this application is given in two-dimensional contour maps, range and azimuth profiles of point targets in frontal side view, oblique angle of 15 degrees and oblique angle of 25 degrees respectively. It should be noted that in Figure (6), the vertical axis of (a), (d) and (g) is the number of azimuth sampling points and the horizontal axis is the number of range sampling points. In Figure (6), the vertical axis of (b), (e) and (h) is the amplitude / dB and the horizontal axis is the number of range sampling points. In Figure (6), the vertical axis of (c), (f) and (i) is the amplitude / dB and the horizontal axis is the number of azimuth sampling points.

[0212] Among them, by Figure 6 The specific image quality assessment results are as follows: At a frontal side view, the range resolution is 0.18m, peak sidelobe ratio is -13.23dB, and integral sidelobe ratio is -9.99dB; the azimuth resolution is 0.21m, peak sidelobe ratio is -13.14dB, and integral sidelobe ratio is -9.61dB. At a 15-degree oblique viewing angle, the range resolution is 0.18m, peak sidelobe ratio is -13.16dB, and integral sidelobe ratio is -10.02dB; the azimuth resolution is 0.21m, peak sidelobe ratio is -13.21dB, and integral sidelobe ratio is -9.99dB. At a 25-degree oblique viewing angle, the range resolution is 0.18m, peak sidelobe ratio is -13.26dB, and integral sidelobe ratio is -10.14dB; the azimuth resolution is 0.21m, peak sidelobe ratio is -13.19dB, and integral sidelobe ratio is -10.04dB. The imaging quality assessment results show that the self-registration method proposed in this application has good point target focusing effect and frame image resolution matching.

[0213] Reference Figure 7This illustration shows a schematic diagram of the relative pixel changes of a scene edge point target (-50m, 50m) in different frame images, according to an embodiment of this application. Specifically, Figure 7 The pixel offset of a point target with coordinates (-50m, 50m) in frame images at different observation angles relative to the frontal side view frame image is given. As the observation angle increases, the pixel offset gradually increases but always remains at the sub-pixel level.

[0214] The above results indicate that the self-registration method provided in this application can complete motion error correction, geometric matching, and pixel position matching and resolution matching of the frame images in real time during the imaging process.

[0215] The comparison algorithm used here is the traditional Phase Gradient Autofocus (PGA) method. (Refer to...) Figure 8 This is a schematic diagram of the imaging results of a scene edge point target (-50m, 50m) at a 15-degree oblique angle when using the traditional phase gradient autofocus method.

[0216] Specifically, the contrast algorithm first uses a polar coordinate format for imaging, and then uses PGA for autofocus, such as... Figure 8 As shown, Figures (a)-(c) are the focusing results using the polar coordinate format algorithm, and Figures (d)-(f) are the results after using PGA autofocus. It can be seen that when the observation angle is 15 degrees oblique, the target is severely defocused before motion compensation. After PGA autofocus, the distance and azimuth side lobes are improved to some extent, but the defocusing phenomenon is still obvious, with the main lobe widening and the side lobes being asymmetrical.

[0217] The above results show that if the radar platform position estimation accuracy is high enough, the motion error correction performance of the self-registration method proposed in this application is better than that of the comparison algorithm. At the same time, the method proposed in this application can achieve consistent pixel positions of the target in the frame image and frame image resolution matching, which are features that the comparison algorithm does not have. Furthermore, PGA requires post-processing of the image after obtaining the defocused image to obtain the focusing result, which is difficult to meet the timeliness requirements of video SAR.

[0218] The variable parameter video SAR self-registration imaging method based on non-ideal trajectory proposed in this application corrects the wavenumber domain deformation caused by motion error by adjusting the shape of each frame signal of the non-ideal motion trajectory in the wavenumber domain through changes in radar parameters and sampling parameters. This method uses time-varying parameters for real-time motion compensation and can realize image focusing and geometric correction during the imaging process. It is an efficient and accurate imaging processing method.

[0219] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0220] This application also provides a non-volatile readable storage medium storing one or more modules (programs). When these modules are applied to a device, they enable the device to execute the instructions for the method steps in this application.

[0221] This application provides one or more machine-readable media storing instructions that, when executed by one or more processors, cause an electronic device to perform one or more of the methods described in the above embodiments. In this application, the electronic device includes various types of devices such as terminal devices and servers (clusters).

[0222] The embodiments of this disclosure can be implemented as an apparatus configured as desired using any suitable hardware, firmware, software, or any combination thereof, including electronic devices such as terminal devices, servers (clusters), etc. Figure 9 An exemplary apparatus 900 is schematically shown that can be used to implement the various embodiments described in this application.

[0223] In one embodiment, Figure 9 An exemplary device 900 is shown, which includes one or more processors 902, a control module (chipset) 904 coupled to at least one of the processors 902, a memory 906 coupled to the control module 904, a non-volatile memory (NVM) / storage device 908 coupled to the control module 904, one or more input / output devices 910 coupled to the control module 904, and a network interface 912 coupled to the control module 904.

[0224] Processor 902 may include one or more single-core or multi-core processors, and processor 902 may include any combination of general-purpose processors or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In some embodiments, device 900 can serve as a terminal device, server (cluster), or other device as described in the embodiments of this application.

[0225] In some embodiments, the apparatus 900 may include one or more computer-readable media (e.g., memory 906 or NVM / storage device 908) having instructions 914 and one or more processors 902 that are combined with the one or more computer-readable media and configured to execute the instructions 914 to implement the module and thus perform the actions described in this disclosure.

[0226] In one embodiment, the control module 904 may include any suitable interface controller to provide any suitable interface to at least one of the processors 902 and / or any suitable device or component communicating with the control module 904.

[0227] The control module 904 may include a memory controller module to provide an interface to the memory 906. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0228] Memory 906 may be used, for example, to load and store data and / or instructions 914 for device 900. In one embodiment, memory 906 may include any suitable volatile memory, such as suitable DRAM. In some embodiments, memory 906 may include double data rate type quad synchronous dynamic random access memory (DDR4 SDRAM).

[0229] In one embodiment, the control module 904 may include one or more input / output controllers to provide an interface to the NVM / storage device 908 and (one or more) input / output devices 910.

[0230] For example, NVM / storage device 908 may be used to store data and / or instructions 914. NVM / storage device 908 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (one or more) non-volatile storage devices (e.g., one or more hard disk drives (HDDs), one or more optical disc drives (CDs), and / or one or more digital universal optical disc (DVD) drives).

[0231] NVM / storage device 908 may include storage resources that are physically part of a device on which device 900 is mounted, or that can be accessed by the device without needing to be part of the device. For example, NVM / storage device 908 may be accessed via a network via one or more input / output devices 910.

[0232] One or more input / output devices 910 may provide an interface for device 900 to communicate with any other suitable device. Input / output devices 910 may include communication components, audio components, sensor components, etc. A network interface 912 may provide an interface for device 900 to communicate via one or more networks. Device 900 may wirelessly communicate with one or more components of a wireless network according to any of one or more wireless network standards and / or protocols, such as accessing wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G, 5G, etc., or combinations thereof.

[0233] In one embodiment, at least one of the processors 902 may be logically packaged with one or more controllers (e.g., memory controller modules) of the control module 904. In one embodiment, at least one of the processors 902 may be logically packaged with one or more controllers of the control module 904 to form a system-in-package (SiP). In one embodiment, at least one of the processors 902 may be integrated with the logic of one or more controllers of the control module 904 on the same die. In one embodiment, at least one of the processors 902 may be integrated with the logic of one or more controllers of the control module 904 on the same die to form a system-on-a-chip (SoC).

[0234] In various embodiments, device 900 may be, but is not limited to, a server, desktop computing device, or mobile computing device (e.g., laptop, handheld computing device, tablet, netbook, etc.). In various embodiments, device 900 may have more or fewer components and / or different architectures. For example, in some embodiments, device 900 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touchscreen display), a non-volatile memory port, multiple antennas, a graphics chip, an application-specific integrated circuit (ASIC), and a speaker.

[0235] The detection device can use a main control chip as a processor or control module, and sensor data, position information, etc. can be stored in a memory or NVM / storage device. The sensor group can be used as an input / output device, and the communication interface can include a network interface.

[0236] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0237] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0238] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable variable-parameter video SAR self-registration imaging terminal device adapted for non-ideal trajectories to produce a machine, such that the instructions, executable by the computer or other programmable variable-parameter video SAR self-registration imaging terminal device adapted for non-ideal trajectories, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0239] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable variable-parameter video SAR self-registration imaging terminal device adapted to non-ideal trajectories to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0240] These computer program instructions can also be loaded onto a computer or other programmable variable-parameter video SAR self-registration imaging terminal device adapted to non-ideal trajectories, causing a series of operational steps to be executed on the computer or other programmable terminal device to produce computer-implemented processing, thereby providing instructions that execute on the computer or other programmable terminal device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0241] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0242] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0243] The foregoing has provided a detailed description of a variable-parameter video SAR self-registration imaging method and apparatus, an electronic device, and a storage medium applicable to non-ideal trajectories, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A variable-parameter video SAR self-registration imaging method applicable to non-ideal trajectories, the method comprising: Predict the location information of the radar platform; Calculate the time-varying radar parameters of the radar platform based on the position information of the radar platform at the next moment; Based on the resolution of the video SAR and the observation angle of each frame of data, the time-varying azimuth sampling parameters of the radar platform are calculated. Based on the time-varying radar parameters and the time-varying azimuth sampling parameters, obtain the echo data of each frame of the video SAR; The echo data is subjected to two-dimensional de-firing and frequency modulation processing to obtain the signal after de-firing and frequency modulation processing; The signal after the de-firing and frequency modulation process is subjected to wavenumber domain data format correction and focusing processing to obtain the focusing result.

2. The method according to claim 1, characterized in that, The prediction of the radar platform's location information includes: An autoregressive prediction model for the motion trajectory of the radar platform is established, and the position information of the radar platform at historical moments is obtained. The autoregressive prediction model is expressed by the following formula: Based on the autoregressive prediction model and the location information of the radar platform at historical moments, calculate the model parameters; Based on the autoregressive prediction model and the model parameters, predict the position information of the radar platform at the next moment; Where m is the azimuth sampling sequence. N a Let X(m), Y(m), and Z(m) represent the number of azimuth sampling points, respectively. X(m-1)…X(mp), Y(m-1)…Y(mp), and Z(m-1)…Z(mp) represent the three-dimensional position information of the radar platform. α0…α p ,β0…β p , χ0…χ p All of these are the model parameters, and ε(m) represents the error term.

3. The method according to claim 2, characterized in that, The step of calculating the time-varying radar parameters of the radar platform based on the platform's position information at the next moment includes: Based on the position information of the radar platform at the next moment, the pitch angle between the radar platform and the center point of the scene is calculated. The pitch angle between the radar platform and the center point of the scene is obtained by the following formula: Based on the position information of the radar platform at the next moment, the azimuth angle between the radar platform and the center point of the scene is calculated. The azimuth angle between the radar platform and the center point of the scene is obtained by the following formula: Based on the elevation angle and azimuth angle between the radar platform and the scene center point, a time-varying radar parameter adjustment law for the radar platform is established. This law is expressed by the following formula: Among them, f c0 f s0 T p0 K r0 f represents the radar carrier frequency, sampling rate, pulse width, and frequency modulation rate at the synthetic aperture center time of the front-view frame of video SAR, respectively. c (m), f s (m), T p (m), K r (m) represent the time-varying radar parameters that change in real time with the azimuth sampling position. This indicates the elevation angle between the radar platform and the center point of the scene at the center of the synthetic aperture frame in the frontal view.

4. The method according to claim 3, characterized in that, The observation angle is the angle between the projection direction of the line connecting the radar position at the center moment of the synthetic aperture of each frame of data and the center point of the scene on the ground and the Y-axis. The calculation of the time-varying azimuth sampling parameters of the radar platform based on the resolution of the video SAR and the observation angle of each frame of data includes: Based on the resolution of the video SAR, the radar wavelength of the radar platform, the angle between the projection direction of the line connecting the radar position at the center time of the synthetic aperture of each frame and the scene center point on the ground and the Y-axis, and the projection distance of the shortest slant range of the frontal side view frame on the ground, the synthetic aperture length is calculated using the following formula: Obtain the equivalent motion velocity and pulse repetition frequency of the radar platform; Based on the synthetic aperture length, the equivalent velocity of the radar platform, and the pulse repetition frequency, the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth is determined. The number of azimuth sampling points contained within the effective support domain wavenumber bandwidth is obtained by the following formula: Based on the synthetic aperture length, the projection length of the shortest slant range of the frontal side view frame on the ground, and the angle between the projection direction of the line connecting the radar position and the scene center point at the center of each frame on the ground and the Y-axis, calculate the azimuth angle corresponding to the first azimuth sampling point included in the effective support domain wavenumber bandwidth. A constraint condition is established for the azimuth angle corresponding to the starting point of azimuth sampling when the radar platform collects data for each frame. The constraint condition is expressed by the following formula: Based on the azimuth angle corresponding to the first azimuth sampling point included in the effective support domain wavenumber bandwidth, the azimuth angle corresponding to the azimuth sampling start point when the radar platform collects data for each frame, the projection length of the shortest slant range of the frontal side-view frame on the ground, the motion speed, and the pulse repetition frequency, the number of azimuth sampling points that the radar platform collects data from is greater than the number of azimuth sampling points included in the effective support domain wavenumber bandwidth is calculated. This number of azimuth sampling points that the radar platform collects data from is greater than the number of azimuth sampling points included in the effective support domain wavenumber bandwidth is obtained using the following formula: Based on the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth and the number of azimuth sampling points that the radar platform acquires when collecting data that exceed the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth, the time-varying azimuth sampling parameters of the radar platform are calculated. The time-varying azimuth sampling parameters are obtained using the following formula: N a =N aref +N a,start Where, ρ a Where L is the resolution of the video SAR, λ is the synthetic aperture length, θ0 is the angle between the projection direction of the line connecting the radar position and the scene center point at the center of the synthetic aperture in each frame onto the ground and the Y-axis, R0 is the projection distance of the shortest slant range on the ground in the frontal side-view frame, and N is the resolution of the video SAR. aref The effective support domain wavenumber bandwidth contains the number of azimuth sampling points, PRF is the pulse repetition frequency, V is the equivalent motion velocity, and θ is the number of azimuth sampling points within the effective support domain wavenumber bandwidth. lower θ is the azimuth angle corresponding to the first azimuth sampling point contained within the effective support domain wavenumber bandwidth. start K represents the azimuth angle corresponding to the starting point of azimuth sampling when the radar platform collects data for each frame. y N represents the range wavenumber. r N represents the number of distance sampling points. a N represents the number of time-varying azimuth sampling points. a,start This indicates the number of azimuth samples that the radar platform collects when it collects data, which is more than the number of azimuth sampling points contained within the effective support domain wavenumber bandwidth.

5. The method according to claim 4, characterized in that, The step of obtaining echo data for each frame of the video SAR based on the time-varying radar parameters and the time-varying azimuth sampling parameters includes: Based on the time-varying radar parameters and the time-varying azimuth sampling parameters, electromagnetic wave signals are transmitted to the target area through the radar platform to obtain echo data of each frame of the video SAR.

6. The method according to claim 5, characterized in that, The step of performing two-dimensional demodulation and frequency modulation processing on the echo data to obtain the demodulated and frequency-modulated signal includes: Perform a Fourier transform on the echo data along the range direction to obtain range frequency domain echo data; Construct a frequency domain filter, which is expressed by the following formula: Multiplying the frequency domain filter and the distance frequency domain echo data yields the signal after line demodulation and frequency modulation processing. The signal after line demodulation and frequency modulation processing is obtained by the following formula: Among them, f τ Represents distance frequency, j represents the imaginary unit, R ref S1 represents the distance from the radar platform to the center of the scene, c represents the speed of light, S2 represents the signal after the de-firing and frequency modulation processing, S1 represents the range-frequency domain echo data, and R... t τ represents the distance between the radar platform and any target point in the scene, and τ represents the range-major time.

7. The method according to claim 6, characterized in that, The process of performing wavenumber domain data format correction and focusing on the frequency-modulated signal to obtain a focusing result includes: Performing a first-order Taylor expansion on the scene center point yields the distance difference between the distance from the radar platform to the scene center and the distance between the radar platform and any target point in the scene. This distance difference is calculated using the following formula: The signal after de-firing and frequency modulation can be discretized as follows: ss l (i,m)=exp{-j(K y (i)y t +K x (i,m)x t )} The sampling sequence for azimuth resampling is determined, and the azimuth resampling sampling sequence is obtained by the following formula: The resampled signal is obtained, and the resampled signal is represented as follows: ss lp (i,m)=exp{-j(K yref (i1)y t +K xref (m1)x t )} Perform range IFFT and azimuth FFT on the resampled signal to obtain the focusing result; Where, x t and y t K represents the coordinates of the target point along the azimuth and range directions, respectively. y (i) and K x (i,m) represent the range wavenumber and azimuth wavenumber, respectively. l (i,m) represents the discretized echo signal after de-firing and frequency modulation processing, K xup K represents the effective support domain wavenumber distribution. x The maximum value, K xlow K represents the effective support domain wavenumber distribution. x The minimum value of K yref K represents the uniformly sampled sequence along the range after resampling. xref This represents a uniform sampling sequence in the azimuth direction after resampling.

8. An electronic device, characterized in that, include: processor; and A memory storing executable code, which, when executed, causes the processor to perform the variable-parameter video SAR self-registration imaging method for non-ideal trajectories as described in any one of claims 1-7.

9. One or more machine-readable media having executable code stored thereon, which, when executed, causes a processor to perform the variable-parameter video SAR self-registration imaging method for non-ideal trajectories as described in any one of claims 1-7.

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