Traditional ground moving target indicator (GMTI) processing attempts to separate moving objects in the scene from stationary clutter. Techniques such as space-time adaptive processing (STAP) require the use of an unknown covariance matrix of the interference (clutter, jamming, and thermal noise) that must be estimated from the remaining data not currently under test. Many problems exist with estimating the interference covariance including: heterogeneous, contaminated, and/or limited training data. There are many existing techniques for obtaining an interference covariance matrix estimate, most of which incorporate some kind of prior knowledge to improve the estimate. We propose a Bayesian framework that estimates both clutter and movers on a range-by- range basis without the explicit estimation of an interference covariance matrix. The approach incorporates the knowledge of an approximate digital elevation map (DEM), platform kinematics (platform velocity, crab angle, and antenna spacings), and the belief that movers are sparse in the scene. Computation using this Bayesian model is enabled by recent algorithm developments for fast inference on linear mixing models. The signal model and required processing steps are detailed. We test our approach using the KASSPER I dataset and compare the results to other current approaches.
Wide-area persistent radar video offers the ability to track moving targets. A shortcoming of the current technology is an inability to maintain track when Doppler shift places moving target returns co-located with strong clutter. Further, the high down-link data rate required for wide-area imaging presents a stringent system bottleneck. We present a multi-channel approach to augment the synthetic aperture radar (SAR) modality with space time adaptive processing (STAP) while constraining the down-link data rate to that of a single antenna SAR system. To this end, we adopt a multiple transmit, single receive (MISO) architecture. A frequency division design for orthogonal transmit waveforms is presented; the approach maintains coherence on clutter, achieves the maximal unaliased band of radial velocities, retains full resolution SAR images, and requires no increase in receiver data rate vis-a-vis the wide-area SAR modality. For Nt transmit antennas and N samples per pulse, the enhanced sensing provides a STAP capability with Nt times larger range bins than the SAR mode, at the
cost of O(log N) more computations per pulse. The proposed MISO system and the associated signal processing
are detailed, and the approach is numerically demonstrated via simulation of an airborne X-band system.