Resource Constrained Adaptive Sensing.

Adaptive Sensing;Estimation and Detection;Resource Constraints;Sensor Networks;Electrical Engineering;Engineering;Electrical Engineering: Systems

Rangarajan, RaghuramTeneketzis, Demosthenis ;

University of Michigan

关键词: Adaptive Sensing;    Estimation and Detection;    Resource Constraints;    Sensor Networks;    Electrical Engineering;    Engineering;    Electrical Engineering: Systems;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/57621/rangaraj_1.pdf?sequence=2&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

RESOURCE CONSTRAINED ADAPTIVE SENSINGbyRaghuram RangarajanChair: Alfred O. Hero IIIMany signal processing methods in applications such as radar imaging, communication systems, and wireless sensor networks can be presented in an adaptive sensing context. The goal in adaptive sensing is to control the acquisition of data measurements through adaptive design of the input parameters, e.g., waveforms, energies, projections, and sensors for optimizing performance. This dissertation develops new methods for resource constrained adaptive sensing in the context of parameter estimation and detection, sensor management, and target tracking.We begin by investigating the advantages of adaptive waveform amplitude design for estimating parameters of an unknown channel/medium under average energy constraints. We present a statistical framework for sequential design (e.g., design of waveforms in adaptive sensing) of experiments that improves parameter estimation (e.g., scatter coefficients for radar imaging, channel coefficients for channel estimation) performance in terms of reduction in mean-squared error (MSE). We derive optimal adaptive energy allocation strategies that achieve an MSE improvement of more than 5dB over non adaptive methods. As a natural extension to the problem of estimation, we derive optimal energy allocation strategies for binary hypotheses testing under the frequentist and Bayesian frameworks which yield at least 2dB improvement in performance. We then shift our focus towards spatial design of waveforms by considering the problem of optimal waveform selection from a large waveform library for a state estimation problem. Since the optimal solution to this subset selection problem is combinatorially complex, we propose a convex relaxation to the problem and provide a low complexity suboptimal solution that achieves near optimal performance. Finally, we address the problem of sensor and target localization in wireless sensor networks. We develop a novel sparsity penalized multidimensional scaling algorithm for blind target tracking, i.e., a sensor network which can simultaneously track targets and obtain sensor location estimates.