Performance metric in closed-loop sensor management for stochastic populations
Emmanuel Delande, Jérémie Houssineau, Daniel E. Clark · 2014
Methods for sensor control are crucial for modern surveillance and sensing systems to enable efficient allocation and prioritisation of resources. The framework of partially observed Markov decision processes enables decisions to be made based on data received by the sensors within an information-theoretic context. This work addresses the problem of closed-loop sensor management in a multi-target surveillance context where each target is assumed to move independently of other targets. Analytic expressions of the information gain are obtained, for a class of exact multi-target tracking filters are obtained and based on the Rényi divergence. The proposed method is sufficiently general to address a broad range of sensor management problems through the application-specific reward function defined by the operator.