This thesis studies adaptive allocation of a limited set of sensing or computational resources in order to maximize some criteria, such as detection probability, estimation accuracy, or throughput, with specific application to inference with synthetic aperture radars (SAR). Sparse scenarios are considered where the interesting element is embedded in a much larger signal space. Policies are examined that adaptively distribute the constrained resources by using observed measurements to inform the allocation at subsequent stages. This thesis studies adaptive allocation policies in three main directions.
First, a framework for adaptive search for sparse targets is proposed to simultaneously detect and track moving targets. Previous work is extended to include a dynamic target model that incorporates target transitions, birth/death probabilities, and varying target amplitudes. Policies are proposed that are shown empirically to have excellent asymptotic performance in estimation error, detection probability, and robustness to model mismatch. Moreover, policies are provided with low computational complexity as compared to state-of-the-art dynamic programming solutions.
Second, adaptive sensor management is studied for stable tracking of targets under different modalities. A sensor scheduling policy is proposed that guarantees that the target spatial uncertainty remains bounded. When stability conditions are met, fundamental performance limits are derived such as the maximum number of targets that can be tracked stably and the maximum spatial uncertainty of those targets. The theory is extended to the case where the system may be engaged in tasks other than tracking, such as wide area search or target classification.
Lastly, these developed tools are applied to tracking targets using SAR imagery. A hierarchical Bayesian model is proposed for efficient estimation of the posterior distribution for the target and clutter states given observed SAR imagery. This model provides a unifying framework that models the physical, kinematic, and statistical properties of SAR imagery. It is shown that this method generally outperforms common algorithms for change detection. Moreover, the proposed method has the additional benefits of (a) easily incorporating additional information such as target motion models and/or correlated measurements, (b) having few tuning parameters, and (c) providing a characterization of the uncertainty in the state estimation process.PHDElectrical Engineering-SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97931/1/newstage_1.pd
