Sparse sensor selection for distributed systems: an l1-relaxation approach

Abstract

We study the problem of sensor selection for distributed systems, where a large number of sensors are located spatially in many different locations. Specifically, we consider both perfect and packet-dropping communication channels. While the original problem is NP-hard, by adopting a sparse design, we can solve the problem via convex optimization and reduce the computation cost significantly. Our method not only handles correlated measurement noise but also can be easily extended to actuator selection or sensor-and-actuator (SaA) selection problems. Simulation shows that our sparsity-based approach performs similarly to the brute force optimal strategy while consuming significantly less computation time. Additionally, our method is shown to outperform the state-of-art method notably.