Moving target inference with bayesian models in SAR imagery

Abstract

This work combines the physical, kinematic, and statistical properties of targets, clutter, and sensor calibration as manifested in multi-channel SAR imagery into a unified Bayesian structure that simultaneously estimates (a) clutter distributions and nuisance parameters and (b) target signatures required for detection/inference. A Monte Carlo estimate of the posterior distribution is provided that infers the model parameters directly from the data with little tuning of algorithm parameters. Per- formance is demonstrated on both measured/synthetic wide-area datasets. Index Terms—synthetic aperture radar, moving target detec- tion, low-rank, hierarchical Bayesian models I. I NTRODUCTION This work provides an algorithm for inference in multi- antenna and multi-pass synthetic aperture radar (SAR) im- agery. Inference can mean many different things in this frame- work, including detection of moving targets, estimation of the underlying clutter distribution, estimation of the target radial velocity, and classification of pixels. To this end, the output of the proposed algorithm is an estimated posterior distribution over the variables in our model. This posterior distribution is estimated through Markov Chain Monte Carlo (MCMC) techniques. Subsequently, the inference tasks listed above are performed by appropriately using the posterior distribution. For example, detection can be done by thresholding the posterior probability that a target exists at any given location. Recently, there has been great interest by Wright et al. (1), Lin et al. (2), Candes et al. (3) and Ding et al. (4) in the so- called robust principal component analysis (RPCA) problem that decomposes high-dimensional signals as