Robust SAR STAP via Kronecker decomposition

Abstract

Abstract-This paper proposes a spatio-temporal decomposition for the detection of moving targets in multiantenna SAR. As a high resolution radar imaging modality, SAR detects and localizes non-moving targets accurately, giving it an advantage over lower resolution GMTI radars. Moving target detection is more challenging due to target smearing and masking by clutter. Space-time adaptive processing (STAP) is often used to remove the stationary clutter and enhance the moving targets. In this work, it is shown that the performance of STAP can be improved by modeling the clutter covariance as a space vs. time Kronecker product with low rank factors. Based on this model, a low-rank Kronecker product covariance estimation algorithm is proposed, and a novel separable clutter cancelation filter based on the Kronecker covariance estimate is introduced. The proposed method provides orders of magnitude reduction in the required number of training samples, as well as improved robustness to corruption of the training data. Simulation results and experiments using the Gotcha SAR GMTI challenge dataset are presented that confirm the advantages of our approach relative to existing techniques.

Robust estimation of structured scatter matrices in (mis)matched models

“…However, they usually assume complex Gaussian distributed samples. Then, the structured CM estimation is addressed either by projecting the Sample Covariance Matrix onto a subset describing a structure [9,10] or by deriving the Maximum Likelihood (ML) estimator under structure constraints. In some practical applications, performance is degraded because the assumption of Gaussian distribution leads to nonrobustness, either to heavy-tailed distributed data or to outliers in the sample set.…”

Kronecker STAP and SAR GMTI

“…3 While in SAR GMTI B is not exactly low rank, it is approximately low rank in the sense that significant energy concentration in a few principal components is observed over small regions. 1,31 Due to the long integration time and high cross range resolution associated with SAR, the returns from the general class of moving targets are more complicated, making simple Doppler filtering difficult. During short intervals for which targets have constant Doppler shift f (proportional to the target radial velocity) within a range bin, the return has the form…”

“…Space-time adaptive processing (STAP) is often used on multiantenna SAR to remove the stationary clutter and enhance the moving targets. In (Greenewald et al, 2016), 1 it was shown that the performance of STAP can be improved by modeling the clutter covariance as a space vs. time Kronecker product with low rank factors, providing robustness and reducing the number of training samples required. In this work, we present a massively parallel algorithm for implementing Kronecker product STAP, enabling application to very large SAR datasets (such as the 2006 Gotcha data collection) using GPUs.…”

Ground moving target detection algorithm for monopulse‐SAR based on complex monopulse ratio

“…In general, current multi‐channel SAR moving target detection (MTD) techniques can be classified, based on whether a clutter cancellation or suppression step is applied prior to the detection, into two classes. The first one includes MTD approaches applying a clutter cancellation step before detection, such as space‐time adaptive processing (STAP) [2–4] and displaced phase centre antenna (DPCA) [5, 6]. STAP is a two‐dimensional (2D) adaptive filtering algorithm that suppresses the clutter by placing nulls in their directions of arrival and Doppler frequencies while maintaining a constant output of moving target contained in the tested signal [2].…”