With the geosynchronous synthetic aperture radar (SAR) satellite as the transmitter, the unmanned aerial vehicle (UAV) can passively receive the echo within the illuminated ground area and achieve 2-D imaging of the interested target. This SAR system, known as GEO-UAV bistatic SAR, is capable of autonomously accomplishing the bistatic SAR mission in rough terrain environments by prespecifying a path for the UAV receiver. In this paper, the GEO-UAV bistatic SAR system is first investigated. The practical advantages and spatial resolution are then analyzed in detail. The spatial resolution of GEO-UAV bistatic SAR is dependent on the observation geometry, which is determined by the UAV path. Therefore, the path planning for GEO-UAV bistatic SAR aims at identifying a set of optimal paths for the UAV receiver to travel through a 3-D terrain environment that simultaneously guarantees the safety of the UAV and achieves SAR imaging with optimized performance during the flight. The path planning is modeled as a constrained multiobjective optimization problem (MOP), which accurately represents the two main aspects for the path planning problem, i.e., UAV navigation and bistatic SAR imaging. Then, a path planning method based on a constrained-adaptive-multiobjective-differential-evolution algorithm is proposed to solve the MOP and generate multiple feasible paths for the UAV receiver with different tradeoffs between navigation for UAV and bistatic SAR imaging performance. The GEO-UAV bistatic SAR mission designer can choose a path from the solution set according to the application requirements, which makes the method more pragmatic.

In synthetic aperture radar (SAR) imagery, images of ground moving targets (GMTs) are smeared, distorted, and shifted. Current GMT imaging methods are mostly based on range-Doppler algorithms, which have two main drawbacks: 1) coupling between range cell migration correction (RCMC) and Doppler parameter estimation and 2) cross terms degrade the performance of the nonlinear estimation methods. In this paper, an optimal 2-D spectrum matching method for SAR GMT imaging is proposed. The main innovation or advantage of this method is that the GMT imaging problem is transformed into a constrained optimization problem, and differential evolution is applied to guarantee a high-processing efficiency. As they are associated with the Doppler centroid variation compensation and range shift compensation processing, all GMT point scatterers can be well focused and well located. Compared with the current methods, the improvements exhibited by this method include three main benefits: 1) RCMC and Doppler parameter estimation can be simultaneously accomplished; 2) both along- and cross-track GMT velocities can be simultaneously estimated; and 3) this method can be applied to both monostatic and bistatic SARs. Numerical simulations and experimental data processing have verified the effectiveness and robustness of the proposed method.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of the receiver. Current literature and reports about BFSAR mainly concentrate on the stationary scene and ground-moving target imaging. Unlike stationary and ground-moving targets, the translational and rotational movements of ship targets usually lead to complicated range cell migration (RCM) and Doppler frequency migration (DFM). Moreover, the characteristics of RCM and DFM for different scattering points of the ship target are significantly different, i.e., the characteristics of the RCM and DFM are 2-D spatial variation, ultimately leading to severe defocusing of ship target in the SAR image. To solve these problems, a kind of hybrid SAR-ISAR imaging formation is proposed for BFSAR ship target imaging. First, to solve the problem of the Doppler ambiguity caused by the forward-looking mode of the receiver, an efficient ambiguity estimation method based on the minimum entropy criterion is presented. Then, keystone transform and range alignment processing can be applied to correct the spatial variant range walk and higher order RCM, respectively. Moreover, in order to obtain a high-resolution and well-focused image after translational compensation, a new method based on the fractional Fourier transform (FrFT) and the Wigner–Ville distribution (WVD) is proposed, where FrFT is applied to separate the multiple main scattering points in each range cell, and WVD is applied to obtain the high-resolution time–frequency distribution of each scattering point. Compared with the conventional ISAR range-Doppler (RD) algorithm and time–frequency estimation-based imaging methods, this method not only has no cross terms but also has high processing accuracy and better antinoise performance.

Due to the division of the transceiver, bistatic synthetic aperture radar (SAR) gains many advantages, such as forward-looking imaging and powerful anti-interference capabilities. In the meantime, information sharing (e.g., positions, status) between the transmitter and receiver is required for SAR imaging. This paper addresses the co-use waveform design for SAR-dual functional radar communication (SAR-DFRC). To this end, we embed information into time-frequency spectrum of the phase coded signal, which can be a feasible solution to SAR-DFRC, bringing the possibility of realizing a light-weighted, miniaturized, low-costed, spectrum reusable, and more confidential system. A novel time-frequency spectrum shaping (TFSS) SAR-DFRC architecture based on short time Fourier transform (STFT) is proposed for the first time. The weighted peak sidelobe level (WPSL) is considered a figure of merit for SAR imaging performance. Information is embedded by nulling the time-frequency spectrum of the waveform. Here, we develop the majorization-minimization peak sidelobe level-time frequency spectrum shaping (MMPSL-TFSS) algorithm to solve the resulting non-convex NP-hard optimization problem. The designed waveform can ensure imaging performance and obtain high communication capacity in the meantime. Experimental and numerical results highlight the effectiveness of the proposed SAR-DFRC framework for imaging performance and the secure transition to communication information.

Due to its flexibility and low cost, the bistatic forward-looking synthetic aperture radar (BFSAR) which employs side-looking transmitter and forward-looking receiver has been studied in recent years. Sparsity-based techniques have been applied in the field of BFSAR imaging and show great potential. In sparsity-based BFSAR imaging, compensation of the motion errors is crucial to get a well-focused image. For fields that admit a sparse representation, we propose a sparsity-based imaging approach integrated with motion error estimation and compensation in this paper. First, a novel joint phase-amplitude compensation-based motion error correction scheme is developed to cope with the spatial variance of motion error. Then, an inversion observation model of the range-Doppler algorithm combined with motion error correction is derived, based on which a joint problem of BFSAR imaging and motion error estimation is formulated as a sparse recovery problem and solved in an iterative way, where in each iteration, both image formation and motion error correction are carried out. Experiments on both the simulated and real BFSAR data show that the proposed method can obtain a more accurate estimation result, and generate better focused images compared with the existing methods.

Due to the high-altitude illumination and the separation of the receiver and transmitter, geosynchronous (GEO) spaceborne-airborne bistatic synthetic aperture radar (BiSAR) is more flexible and accessible in remote sensing applications. In this paper, the Doppler characteristics of GEO BiSAR with a squint receiver are analyzed. It is found that the Doppler spectrum is generally aliased in GEO BiSAR regarding the low pulse repetition frequency (PRF) adopted by the GEO SAR. In order to suppress the ambiguity without adjusting the PRF of GEO SAR, the azimuth multichannel receiving technique is applied to the receiver and then the multichannel transfer function for GEO BiSAR is derived. However, the whole bandwidth of the imaging scene is much larger than that of the center point, which requires extra receiving channels to suppress the ambiguity and thereby increasing the system complexity. A two-step nonlinear chirp scaling (NLCS) method is proposed to obtain the well-focused image with reduced receiving channels. First, a preprocessing step is conducted to achieve space-variant range cell migration correction. After that, the first-step NLCS processing is applied to equalize the 2-D space-variant Doppler centroid and thereby the Doppler bandwidth is decreased, i.e., the required number of receiving channels for reconstruction is reduced. Then, the unambiguous spectrum is reconstructed based on the proposed multichannel transfer function. Finally, the second-step NLCS processing is carried out to equalize the 2-D space-variant high-order Doppler parameters and obtain the well-focused image. The simulation results validate the effectiveness of the proposed method. With the proposed two-step NLCS method, the well-focused image for GEO BiSAR is obtained and the required number of receiving channels can be decreased, which is beneficial to reducing the system complexity and hardware cost.