Collaborative sensor networks with Bayesian Multitarget Tracking and Sensor Localization

We propose a method to track an unknown and variable number of targets without assuming the knowledge of the locations of the sensor nodes in the network. Then, the multitarget tracking and the localization of sensor nodes is performed jointly. As low-power consumption is a requirement in sensor networks, a collaborative estimation scheme is presented, where only a small set of sensors are active while the others remain in an idle state. The proposed technique is based on a Rao-Blackwellized sequential Monte Carlo (SMC) method that takes advantage of the fact that the state space of the unknown variables is separable. The problem is then divided in two parts. The first one generates samples to estimate the number of targets and solves the association uncertainty between measurements and targets; while the second one is a multiple target tracking problem that can be solved with a unscented Kalman filter for each sample. It is shown through simulations that it is possible to track the multiple targets and also get accurate estimates of the unknown locations of the sensor nodes.