Sensor selection for estimation, filtering, and detection

Sensor selection is a crucial aspect in sensor network design. Due to the limitations on the hardware costs, availability of storage or physical space, and to minimize the processing and communication burden, the limited number of available sensors has to be smartly deployed. The node deployment should be such that a certain performance is ensured. Optimizing the sensors' spatial constellation or their temporal sampling patterns can be casted as a sensor selection problem. Sensor selection is essentially a combinatorial problem involving a performance evaluation over all possible choices, and it is intractable even for problems of modest scale. Nevertheless, using convex relaxation techniques, the sensor selection problem can be solved efficiently. In this paper, we present a brief overview and recent advances on the sensor selection problem from a statistical signal processing perspective. In particular, we focus on some of the important statistical inference problems like estimation, tracking, and detection.