Multi-input and multi-output (MIMO) radar has drawn much attention in synthetic aperture radar (SAR) due to the possession of more degrees of freedom (DOFs). However, there are some repeated equivalent phase centers (EPCs) caused by the same wave path have no contribution to the improvement of DOFs. To this end, a novel interpulse phase coding and multi-carrier (IPCMC) transmission scheme is investigated to restored repeated EPCs. Furthermore, an advanced range ambiguity separation method is proposed based on the increased efficient EPCs. Finally, distributed targets simulation experiments are performed and the experiment results illustrate that the range ambiguity suppression performance is significantly improved due to the restored EPCs.

Waveform design plays a critical role in ruling the performance of a pulse compression radar system, and keeps being a hotpot for several decades. Unfortunately, some coded waveforms being widely employed in recent years nearly have their limitations. The idealistic phase code waveform has a high spectral sidelobe brought by the instantaneous phase change. The polyphase-coded FM (PCFM) waveform can provide a continuous phase function, but the frequency template error (FTE) metric is indispensable for it to control the spectrum, thereby inducing high design complexity. The nonlinear frequency modulated (NLFM) waveform has a controlled spectral content, while its coding structure is non-parameterized. To this end, we develop a novel parameterized frequency modulated (PFM) waveform and a constant envelope. Simulation and real experimental results verify the superior performance of the proposed waveform in term of autocorrelation sidelobes and energy ratio within bandwidth compared to the phase code and PCFM waveforms.

The problem of echo separation using digital beamforming (DBF) on receive for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is of notable importance to allow for practical systems. Regrettably, current DBF-MIMO-SAR schemes, such as the short-term shift-orthogonal (STSO) scheme, are computationally cumbersome, increasing the required hardware complexity. To alleviate this problem, we here propose an improved echo separation solution for realizing a low-cost MIMO-SAR system. We detail a generic waveform design scheme as well as optimized monostatic radar waveforms (e.g., nonlinear frequency modulation (NLFM) signal) showing how these can be directly adopted in the proposed scheme to improve the imaging performance. The proposed scheme enables the number of the interference segments generated by unmatched waveforms to be halved by the use of the fast time spectrum preprocessing and segment synthesis, dramatically simplifying the array configuration and reduces the system complexity. By exploiting inter-pulse phase coding techniques, the proposed method can provide a reconfigurable waveform transmitting scheme, allowing the system resources in range frequency, elevation space, and Doppler domains to be jointly exploited for the separation of aliased signal returns. The proposed scheme is evaluated using extensive numerical and measured data sets, demonstrating the feasibility and potential of the proposed method for resource-limited spaceborne/airborne MIMO-SAR systems.

Ground moving target indication (GMTI), as a challenging task for synthetic aperture radar (SAR) systems, keeps drawing considerable attention. Robust principal component analysis (RPCA) aiming at separating low-rank and sparse components has been successfully employed in SAR systems for GMTI recently. However, its practical application is limited by the heavy computational burden as well as the requirement of manual parameter modification. To cope with this problem, a fast and free of presetting parameters RPCA network (RPCA-Net) is proposed for SAR-GMTI under strong clutter background. In the proposed method, a novel RPCA model is first introduced, where not only the low-rank and sparse terms but also the errors in practical SAR systems are taken into account. Moreover, the low-rank factorization plus scaled gradient descent (ScaledGD) is also employed to acquire low-rank clutter background rather than singular value decomposition (SVD). Then, we parameterize our proposed RPCA model and unfold it as a feedforward neural network (FNN) to acquire the iterative parameters through backpropagation. Compared to the GMTI methods based on traditional RPCA models, our proposed RPCA-Net can provide a higher detection ability and faster convergence without presetting parameters empirically. Experiments on two groups of measured data collected by airborne SAR systems validate the superior performance of the proposed RPCA-Net.

Moving target brings out the different position shifts and defocusing across the image sequences acquired by circular synthetic aperture radar (CSAR) due to the Doppler shift and range smear effects. In this paper, a novel moving target detection approach for single-channel CSAR is proposed based on deep neural network (DNN). A dual-channel densely connected convolutional network (DenseNet) in consideration of complex-valued information is exploited for distinguishing the ground clutter and moving target. In terms of limited CSAR measure data set available for training the DNN network, simulated moving target samples are generated and fused into the measured ones under the various motion parameters. Finally, experiments have demonstrated that the proposed DenseNet for single-channel CSAR system processes an accepted detection performance and effectively overcomes the insufficiency of the limited dataset applications.