Near-Optimal Sparse Sensing for Gaussian Detection with Correlated Observations

Abstract

Detection of a signal under noise is a classical signal processing problem. When monitoring spatial phenomena under a fixed budget, i.e., either physical, economical or computational constraints, the selection of a subset of available sensors, referred to as sparse sensing, that meets both the budget and performance requirements is highly desirable. Unfortunately, the subset selection problem for detection under dependent observations is combinatorial in nature and suboptimal subset selection algorithms must be employed. In this work, different from the widely used convex relaxation of the problem, we leverage submodularity, the diminishing returns property, to provide practical near optimal algorithms suitable for large-scale subset selection. This is achieved by means of low-complexity greedy algorithms, which incur a reduced computational complexity compared to their convex counterparts.

13 pages, 9 figures

References in corpus (5)

• Sensor Selection for Estimation with Correlated Measurement Noise
• Sensor placement by maximal projection on minimum eigenspace for linear inverse problems
• A submodular-supermodular procedure with applications to discriminative structure learning
• Scalable Greedy Feature Selection via Weak Submodularity
• Sensor Array Design Through Submodular Optimization

Cited by in corpus (5)

• Sparse Antenna and Pulse Placement for Colocated MIMO Radar
• Submodularity in Action: From Machine Learning to Signal Processing Applications
• Sparse Sampling for Inverse Problems with Tensors
• Discrete Signal Processing with Set Functions
• On the Performance Analysis of Binary Hypothesis Testing with Byzantine Sensors