The focusing of bistatic synthetic aperture radar (BiSAR) data is more challenging than the traditional monostatic counterparts because of the strong range-azimuth coupling of echo signal, and the range cell migration (RCM) and Doppler frequency modulation (FM) rate, which are caused by complex geometric configuration. Although several monostatic algorithms have been modified to handle general bistatic cases, these algorithms are derived from the assumption that the flying platforms are moving along a linear trajectory with uniform velocity. In practical situation, the flight path of the spaceborne SAR platform inevitably deviates from the ideal trajectory in the long integration time. In this case, besides the influence of the inherent geometric configuration of BiSAR, the curved trajectory of the platforms also causes an additional range-azimuth coupling and the spatial variance of RCM and Doppler FM rate, which cannot be processed by the traditional algorithms. In this article, considering the curved orbit of the SAR platforms and the motion of ground targets caused by the Earth's rotation, a high-order motion range model is proposed. Based on the range model, the spatial variance characteristic of the BiSAR with curved trajectory is analyzed. Then, an extended azimuth nonlinear chirp scaling (EANLCS) algorithm with an addition of highly varying residual Doppler centroid correction for BiSAR with curved trajectory is proposed. Simulation results show the effectiveness of the range model and the modified algorithm.

Differential synthetic aperture radar tomography (D-TomoSAR) has proven an effective method for retrieving the elevations and deformations of both terrains and urban infrastructures. Particularly, D-TomoSAR with high-resolution X-band SAR data provides even higher accuracy for monitoring of deformations of the man-made structures. However, higher frequency increases the sensitivity of the system to the minute deformations causing a mismatch with the conventional linear deformation model. Currently, the traditional D-TomoSAR system has been extended to higher order versions to extract the seasonal deformation caused by the thermal dilation of structures. Nevertheless, the accelerated motion of scatterers representing another minute deformation component was neglected. As a result, the current D-TomoSAR model cannot accurately retrieve the linear, seasonal, and accelerated motions of the scatterers at the same time. In this letter, a generalized D-TomoSAR system model is proposed by a combination of the polynomial and the seasonal deformation model. Then, an effective algorithm for simultaneously retrieving the multiple deformation components based on the quasi-maximum likelihood (QML) technology is proposed. Finally, numerical simulations and experimental results with real data verify the effectiveness of the proposed system model and algorithm.

Conventional differential synthetic aperture radar tomography (D-TomoSAR) can only capture the scatterers’ one-dimensional (1-D) deformation information along the line of sight (LOS) of the synthetic aperture radar (SAR), which means that it cannot retrieve the three-dimensional (3-D) movements of the ground surface. To retrieve the 3-D deformation displacements, several methods have been proposed; the performance is limited due to the insufficient sensitivity for retrieving the North-South motion component. In this paper, an improved D-TomoSAR model is established by introducing the scatterers’ 3-D deformation parameters in slant range, azimuth, and elevation directions into the traditional D-TomoSAR model. The improved D-TomoSAR can be regarded as a multi-component two-dimensional (2-D) polynomial phase signal (PPS). Then, an effective algorithm is proposed to retrieve the 3-D deformation parameters of the ground surface by the 2-D product high-order ambiguity function (PHAF) with the relax (RELAX) algorithm. The estimation performance is investigated and compared with the traditional algorithm. Simulations and experimental results with semi-real data verify the effectiveness of the proposed signal model and algorithm.