Selective Sensor Control via Cauchy Schwarz Divergence

A novel information theoretic objective function is introduced for optimal control of sensors in multi-object systems (e.g., multi-target tracking solutions) in which the sensors need to be controlled toward acquiring the most accurate measurements relative to a selected subset of objects (not all of them). This is continuation of our previous works where we tackled the same problem by devising task-driven cost functions to be optimized within the sensor control solution. In this paper, a novel objective function is presented. It is formulated using an information divergence that presents the difference in information contents between prior density and posterior density. Numerical experiments are presented for a multi-target tracking application. The tracking results show the proposed method adequately works in principle, and the tracking MSE error for the targets of interest is similar or better than the state of art in most of the times.