For inverse synthetic aperture radar (ISAR) imaging of the maneuvering target, when the time-varying rotational acceleration arises, the image will become blurred because of high-order phase terms. In this letter, the received signal in a range bin is modeled as the multicomponent high-order polynomial phase signal (PPS). To eliminate high-order phase terms, a novel ISAR imaging algorithm based on the cubic phase function and changing sampling rate (CPF-CSR) is proposed in this letter. First, we regard the high-order PPS as a piecewise cubic phase signal (CPS), and thus the cubic phase function (CPF) algorithm can estimate the instantaneous frequency rate (IFR). Next, to suppress cross-terms for the multicomponent PPS, an improved algorithm based on the CPF is proposed, which searches the range bin composed of multiple strong point scatterers and estimates the IFR of the dominant one. Finally, the changing sampling rate (CSR) algorithm is proposed to eliminate high-order phase terms by adjusting the instantaneous frequency of the signal, and no additional estimation for the IFR of other components is required. After the cross-range compression, a well-focused ISAR image can be obtained. The experimental results of the simulated data and real data are given to validate the effectiveness of the CPF-CSR algorithm.

As an innovative concept of high-resolution and wide-swath system, the low-oversampled staggered synthetic aperture radar (SAR) can not only deal with the blind ranges but also suppress range ambiguities and reduce the data volume. Due to the variation of pulse repetition interval (PRI), there will be echo pulse loss and nonuniform sampling in azimuth. Generally, the existing reconstruction algorithms mostly resample the nonuniformly sampled signal into a uniform grid and then perform conventional focused processing. However, the accuracy of the resampling is limited, and the advantage of the nonuniformity over uniform sampling in terms of reconstruction performance is ignored, especially with low oversampling factors. In this article, a reconstruction algorithm for low-oversampled staggered SAR is proposed based on the sparsity Bayesian learning (SBL) in the presence of a nonlinear PRI variation strategy. To ensure that the blind range distribution, which depends on the PRI variation strategy, brings a superior reconstruction performance, we define a novel objective function and optimize a sequence of nonlinear PRI variation with genetic algorithm (GA). On the basis of the optimized sequence, the proposed reconstruction algorithm performs the second-order keystone transform to achieve range curvature correction for nonuniformly sampled data in azimuth. Then, a nonuniform observation model is established. The SBL using a hierarchical form of the Laplace prior is applied to reconstruct the focused images directly with the nonuniform sampling. Simulations and experiments on raw data generated in the staggered SAR mode with low oversampling factors are performed to verify the effectiveness of the proposed method.

A bistatic synthetic aperture radar (BiSAR) system with a Medium-Earth-Orbit (MEO) SAR transmitter and high-maneuvering receiver (MEO/HM-BiSAR) can achieve a wide swath and high resolution. However, due to the complex orbit characteristics and the nonlinear trajectory of the receiver, MEO/HM-BiSAR high-resolution imaging faces two major challenges. First, the complex geometric configuration of the BiSAR platforms is difficult to model accurately, and the ‘non-stop-go’ effects should also be considered. Second, non-negligible wavefront curvature caused by the nonlinear trajectories introduces residual phase errors. The existing spaceborne BiSAR imaging algorithms often suffer from image defocusing if applied to MEO/HM-BiSAR. To address these problems, a novel high-resolution imaging algorithm named MSSWCC (Modified Second-Order Space-Variant Wavefront Curvature Correction) is proposed. First, a high-precision range model is established based on an analysis of MEO SAR’s orbital characteristics and the receiver’s curved trajectory. Based on the echo model, the wavefront curvature error is then addressed by two-dimensional Taylor expansion to obtain the analytical expressions for the high-order phase errors. By analyzing the phase errors in the wavenumber domain, the compensation functions can be designed. The MSSWCC algorithm not only corrects the geometric distortion through reverse projection, but it also compensates for the second-order residual spatial-variant phase errors by the analytical expressions for the two-dimensional phase errors. It can achieve high-resolution imaging ability in large imaging scenes with low computational load. Simulations and real experiments validate the high-resolution imaging capabilities of the proposed MSSWCC algorithm in MEO/HM-BiSAR.

Maritime moving target imaging using synthetic aperture radar (SAR) demands high resolution and wide swath (HRWS). Using the variable pulse repetition interval (PRI), staggered SAR can achieve seamless HRWS imaging. The reconstruction should be performed since the variable PRI causes echo pulse loss and nonuniformly sampled signals in azimuth, both of which result in spectrum aliasing. The existing reconstruction methods are designed for stationary scenes and have achieved impressive results. However, for moving targets, these methods inevitably introduce reconstruction errors. The target motion coupled with non-uniform sampling aggravates the spectral aliasing and degrades the reconstruction performance. This phenomenon becomes more severe, particularly in scenes involving multiple moving targets, since the distinct motion parameter has its unique effect on spectrum aliasing, resulting in the overlapping of various aliasing effects. Consequently, it becomes difficult to reconstruct and separate the echoes of the multiple moving targets with high precision in staggered mode. To this end, motivated by deep learning, this paper proposes a novel Transformer-based algorithm to image multiple moving targets in a staggered SAR system. The reconstruction and the separation of the multiple moving targets are achieved through a proposed network named MosReFormer (Multiple moving target separation and reconstruction Transformer). Adopting a gated single-head Transformer network with convolution-augmented joint self-attention, the proposed MosReFormer network can mitigate the reconstruction errors and separate the signals of multiple moving targets simultaneously. Simulations and experiments on raw data show that the reconstructed and separated results are close to ideal imaging results which are sampled uniformly in azimuth with constant PRI, verifying the feasibility and effectiveness of the proposed algorithm.

Staggered synthetic aperture radar (SAR), which operates with pulse repetition interval (PRI) variation, deals with the blind range problem caused by constant PRI in conventional high-resolution wide-swath SAR imaging. Due to the variation of PRI, there appear periodic loss of pulses and nonuniform sampling in azimuth. However, the exiting reconstruction methods mostly correct range curvature after resampling the nonuniform samplings into a uniform grid. It inevitably introduces the errors and thus affects the effectiveness of the correction. To solve the nonuniform sampling problem and eliminate the errors, our study proposes a reconstruction algorithm [reconstruction algorithm with modified second-order keystone transform (RMSKT)] with modified second-order keystone transform in staggered SAR mode. Moreover, to provide a priori knowledge required by the reconstruction, i.e., the nonuniform sampling distribution, which depends on PRI variation, our study first designs an optimal PRI variation scheme and optimizes the sequence parameters. With the designed PRIs sequence, the proposed RMSKT algorithm can achieve nonuniform range curvature correction and data reconstruction jointly. The proposed RMSKT algorithm shows significant improvements over the traditional reconstruction methods in terms of the peak-sidelobe ratio, the integral sidelobe ratio, and the azimuth ambiguity-to-signal ratio. Simulations and experiments on raw data generated in staggered mode are performed to verify the effectiveness of the proposed method.

This paper presents bistatic synthetic aperture radar imaging preliminary results with multiGNSS transmitters and a fixed receiver. Geometry, ambiguity function and imaging algorithms are discussed to describe multi-GNSS BiSAR system. Simulation results are provided to demonstrate the performance improvement of the amount of information of a given scene.