GOSPA-Driven Gaussian Bernoulli Sensor Management

This paper presents a multi-target metric driven approach to sensor management for Bernoulli filtering, in which at most one target of interest is present. The metric used is the generalised optimal sub pattern assignment (GOSPA) metric. We consider the problem of having an agile sensor operating in a surveillance area, tracking objects as they appear from a target birth distribution. Only one target of interest can exist at any given time-step and its single-target density is Gaussian. In this scenario, we have a grid of sensors that we can select from, one at a time using myopic planning. We evaluate the proposed sensor management algorithm via simulations.