Multiple Objective D-Optimal Sensor Management for Group Target Tracking

A group target is moving in an area well covered by a network of passive sensor nods with known positions. Additionally, there are a number of mobile robots with active sensors. In order to obtain a robust estimate of the position of the target and decrease the amount of energy spent on active sensing and communications by the sensor network and the mobile robots a sensor management system optimises the spatial configuration of the mobile robots over time. A tracking algorithm predicts the position of the target over multiple steps. An estimate for the tracking accuracy for each possible sensor action is calculated based on a function of the expected resulting posterior inverse covariance (information) matrix given the position of the nodes of the sensor networks and the feasible position of the mobile robots in future time instants. We propose a novel approach for active sensor management that combines the Rao-Blackwellised particle filter/predictor and multi-objective D-optimal optimisation. The designed decentralised Rao-Blackwellised particle filter (RBPF) is composed of two parts: a decentralised Information or Kalman filter and a particle filter (PF). The sensor management framework that is based on the generalised D-optimal optimisation with slack variables is proposed