Insense: Incoherent sensor selection for sparse signals.

Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk

Abstract: Highlights • A new sensor selection algorithm for sparse signal recovery. • We select sensors forming linear systems with small column coherence. • Our new projection-based selection problem has an efficient solution. • Our algorithm outperforms classical algorithms in sparse recovery performance. • We apply our algorithm to several applications, including microbial diagnostics. Abstract Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal. In this paper, we develop a new sensor selection algorithm for sparse (or near sparse) signals that finds a subset of sensors that best recovers such signals from a number of measurements that is much smaller than the dimension of the signal. Existing sensor selection algorithms cannot be applied in such situations. Our proposed Incoherent Sensor Selection (Insense) algorithm minimizes a coherence-based cost function that is adapted from recent results in sparse recovery theory. Using six datasets, including two real-world datasets on microbial diagnostics and structural health monitoring, we demonstrate the superior performance of Insense for sparse-signal sensor selection. Previous article in issue Next article in issue