The accuracy of the traditional compressed sensing (CS) based tomographic synthetic aperture radar (TomoSAR) imaging is limited by the inappropriate grid partitioning. The atomic norm based processing effectively solves this problem by implementing variable estimation in the continuous domain, that is, avoiding the undesired grid partitioning manipulation. Nevertheless, the performance of the atomic norm based TomoSAR imaging is limited in two main aspects: limited geometry adaptability caused by the uniform sampling requirement and the high computational load. In this paper, a novel atomic norm minimization (ANM) based off-grid TomoSAR imaging is proposed for the fast processing with nonuniform sampling. The main technical contributions are twofold: First, the nonuniformly sampled data is resampled to be uniform where a new geometrical projection-based interpolation is used; Second, the ANM problem is solved by using the non-symmetric cone model to speed up the processing, reducing the computational load fromO(N2) toO(N). The proposed approaches have been verified by the computer simulations and the real data experiments.

The compressive sensing (CS)-based tomographic SAR (TomoSAR) 3-D imaging method has the shortcoming of low efficiency, mainly represented in two aspects: first, the CS solver requires iterative calculation and hence is computationally expensive; second, the CS solver needs hyperparameters’ selection, which commonly requires cost-inefficient try-and-error attempts. Recently, the iterative CS solver is suggested to be replaced by a deep learning network for a tremendous processing speed improvement. However, the existing deep-learning-based TomoSAR imaging algorithms suffer from the problem of model inadaptability, i.e., being inadaptive to the observation model and the signal energy model and hence is low accuracy. This article proposes a new model-adaptive network (MAda-Net) to implement deep-learning-based TomoSAR 3-D imaging with a much improved processing accuracy. First, a new adaptive model-solving (AMS) module is introduced to solve the problem of the observation model inconsistency between the real spatially varying one and the approximately fixed one used by the network. Second, a new adaptive threshold-activation (ATC) module is introduced to solve the problem of signal energy model inconsistency between the real backscattered echo and the simulated echo for network training. The effectiveness of the proposed method has been verified by the computer simulations and the real unmanned aerial vehicle (UAV) SAR experiments.

Tomographic synthetic aperture radar (TomoSAR) is a 3-D imaging technology used to overcome the layover problem faced by traditional 2-D SAR systems. The quality of TomoSAR imaging capabilities relates closely to the number of acquisitions (NOAs). However, this number is often quite limited due to cost problems. The previous studies have shown some empirical requirements for the minimum NOAs. In this letter, an analytic constraint between the minimum NOAs and signal-to-noise ratio (SNR) is presented, and this constraint is more precise than the existing empirical one. The signal model is first analyzed, followed by the solution of the Fisher matrix, and finally leads to the constraint between the SNR and the minimum NOAs. The presented approach is evaluated via computer simulations.

Multifrequency (MF) interferometric synthetic aperture radar (InSAR) phase unwrapping (PU) technology is proposed for PU in steep terrains where the phase changes of adjacent pixels exceed the commonly required threshold of$\pi $. Traditional MF PU methods will fail in the case of extremely steep terrain such as artificial buildings due to insufficient quality of phase noise suppression (PNS). In this article, we propose a refined MF-InSAR PU method that can be robustly applied for extremely steep terrain PU via two main contributions. First, an additional steep edge extraction step is introduced for geological local PU window generation to prevent inaccurate PNS across the extracted steep edges. Second, the traditional linear phase model is extended to a nonlinear one for more accurate MF local fringe frequency estimation in PNS. The computer simulations and the real dual-frequency airborne experiment validate the presented approach.

Linear array (LA) multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) has the capacity of achieving 3-D images in a single pass. If processed by matched filtering-based linear 3-D imaging, sidelobe suppression is often required for image quality enhancement. However, in the case of imaging a large target in a short range, sidelobes of the target will become spatially variant and curved, and traditional sidelobe suppression methods will fail. This article proposes a new spatially variant curved sidelobe suppression method for LA MIMO SAR short-range 3-D imaging. The key technique is the employment of a new pseudopolar coordinate system where both the spatial variance and the curvature of 3-D sidelobes can be removed. Specifically, a new 3-D spatially variant apodization (SVA) kernel is applied for sidelobe suppression to maintain the resolution performance. The validity of the presented approach has been demonstrated via computer simulations, the tower crane experiment, and the unmanned ground vehicle experiment.

Linear-array multiple-input–multiple-output (LA-MIMO) synthetic aperture radar (SAR) can obtain 3-D radar images by only one pass. However, it is sensitive to time-variant measurement errors of curved track and time-variant attitude angles, meaning that autofocus processing for the LA-MIMO SAR tomography is necessary. The existing autofocus methods cannot be used to estimate thetime-variantand3-D space-variantmotion errors (3-D SVME) of the LA-MIMO SAR. To solve this problem, a new autofocus approach based on multiple local autofocusing and the LA-MIMO SAR time-variant motion error estimation is proposed. First, the local motion error estimation based on the fast local spectral analysis (SPECAN) 3-D imaging and the maximum contrast optimization 2-D local autofocusing is performed to estimate the local time-variant motion errors. Then, based on the linear-array motion error model, the time-variant 3-D trajectory deviations of the array center and attitude angles are estimated by the weighted least square estimation (WLSE) to solve the 3-D SVMEs. Last, the 3-D fast factorized backprojection (FFBP) is performed to obtain the well-focused 3-D image of the whole beam. The proposed approach has been applied for the tomography of a new crawler-type unmanned-ground-vehicle (UGV) LA-MIMO SAR. Both the simulation and real data experiments verify the effectiveness of the proposed approach.

A distributed configuration is a promising realization of tomographic synthetic aperture radar (TomoSAR) 3-D imaging. It is able to implement single-pass tomographic imaging in a very short time and, hence, outperforms the traditional time-consuming multipass TomoSAR. It also outperforms the traditional single-platform TomoSAR by achieving a higher resolution in elevation by forming a much larger spatial baseline. However, there has been little research reported on the distributed TomoSAR so far. In this article, we, for the first time, experimentally demonstrate the great potential of the single-pass distributed TomoSAR 3-D imaging. The main contributions are threefold. First, a new distributed TomoSAR 3-D imaging model is built, characterized by using both inner monostatic and bistatic spatial configurations. Second, a new multistatic synchronization scheme is developed for accurately correcting both multistatic time and phase synchronization errors. Finally, a P-band distributed unmanned-aerial-vehicle (UAV) TomoSAR prototype with four separate stations is built with an elaborately designed time-division waveform for full data acquisition. To the best of our knowledge, this is the first distributed TomoSAR system. We have also implemented the first single-pass TomoSAR 3-D imaging experiment and successfully achieved a meter-level 3-D image in Pinggu, Beijing, China.

Linear array multiple-input multiple-output synthetic aperture radar (LA-MIMO SAR) tomography can provide 3-D radar images without layover and geometric distortion effects. However, suffering from the channel and motion error, and large computation, the real LA-MIMO-SAR data is difficult to be well focused quickly. Therefore, the 3-D imaging results of real LA-MIMO-SAR data are rarely shown in existing literature. In this paper, real LA-MIMO-SAR data imaging processing method based on 3-D fast factorized Backprojection is proposed to well focus the echoes with channel and motion errors, and large size of data. Then, our experiments of LA-MIMO-SAR 3-D imaging are demonstrated. The LA-MIMO radar is installed to a special tower crane and works in downward-looking and forward-looking geometry. Some 3-D imaging results are demonstrated to verify the downward-looking and forward-looking LA-MIMO-SAR modes and the processing method.

Synthetic Aperture Radar (SAR) tomography is an important technique for target elevation information inversion and reconstructs the 3D structure of the target via multi-pass observations. At present, SAR tomography is mainly used in large-scale, low-resolution scenes where the range between the radar and the target is far larger than the target size and the resolution only meters. Therefore, the high-order phase residue is small and will not affect the elevation recovery. However, in the case of the small-scale, high-resolution scenes, the traditional TomoSAR processing will introduce quadratic phase residuals and affecting the quality of recovery. In order to solve the problem, this paper proposes a new method to reconstruct the 3-D structure in high-resolution scenes via elevation segmentation recovery. The main idea is to establish a reference signal at different height sections of the target area so that the quadratic phase residual is less than π/2. Finally, the estimated result of each section is projectively transformed into a unified coordinate system to achieve stable and accurate recovery. Besides, the algorithm has been verified by simulation data and measured data.

Image coregistration is a key step in tomographic SAR (TomoSAR) signal processing, and the quality of coregistration directly determines the tomographic results. Traditional coregistration is performed based on the far-field assumption, where the scattering characteristics remain constant with different look angles and distances. However, for near-field TomoSAR observation, the traditional coregistration method will fail because of the mismatch caused by the change of scattering characteristics. In this study, we propose a new structure-based coregistration method for near-field ground-based MIMO TomoSAR. Corse coregistration based on the structure and fine coregistration combining correlation function and singular modification are conducted to achieve coregistration accurately and robustly. The validity of the presented approach is validated by real data.