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A frequency-domain system identification-based multiple-input multiple-output (FDSI-MIMO)-SAR algorithm using multiple phase center multiple azimuth beams is proposed to obtain high-resolution wide-swath (HRWS) imaging. Frequency-division multiple access is used in such a way that each transmitter emits a conventional linear frequency-modulated (LFM) waveform modulated by a different carrier frequency and the obtained range resolution corresponds to the total transmitted bandwidth. In this article, an MIMO-SAR problem is modeled, using the principle of displaced phase center, as multiple but separate multiple-input single-output (MISO) system identification problems. The channel impulse responses of the individual MISO problems are identified in the range dimension using an FDSI-based estimation algorithm in such a way that the estimated range profile is free of interrange cell interference. In addition, the proposed algorithm does not require separating the subband waveforms at the receiver as they are processed jointly without a need to add guard bands between the adjacent subbands, which would allow utilizing the available bandwidth to the maximum efficiency. The method of synthesizing a wide transmit antenna beam from a narrow antenna beam applied in single-input multiple-output (SIMO) SAR is extended to MIMO SAR to remove the sidelobes effects of the receive beams and hence leading to the desired signal-to-noise ratio. A pulse repetition frequency lower than the Doppler bandwidth is used to obtain wide swath without experiencing aliasing in the Doppler spectrum. Finally, both simulated and constructed raw data are used to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a processing framework to separate moving targets from the clutter, under multichannel synthetic aperture radar (SAR) scenarios, and addresses the moving target imaging and velocity estimation problems for ground moving target indication (GMTI) applications. A practical implementation is introduced to break the SAR/GMTI problem into two processing stages, and the sparsity of the moving targets in the observed scene is exploited throughout the stages. The two-stage process extracts the moving targets from the monitored region via a sparsity-based iterative decomposition algorithm and subsequently estimates the complete velocity vectors of moving targets by enforcing sparsity constraints. The model is sufficiently versatile to incorporate digital elevation map information, which further improves the moving target relocation accuracy. The effectiveness of the presented framework is demonstrated using the Air Force Research Laboratory Gotcha GMTI challenge data. [ABSTRACT FROM AUTHOR]