Mutual Information Methods with Particle Filters for Mobile Sensor Network Control

This paper develops a set of methods enabling an information-theoretic distributed control architecture based on particle filters to facilitate search by a mobile sensor network, permitting the use of nonlinear and non-Gaussian sensor models. Given a particular configuration sensors, this technique exploits the structure of the probability distributions of the target state and of the sensor measurements to compute the control inputs to the mobile sensors leading to future observations that minimize, in expectation, the future uncertainty of the target state. We compute the mutual information using a particle set representation of the posterior distribution. In order to control a large number of mobile sensors as a network, single-node and pairwise-node approximation schemes are presented, with analytically bounded error, making the approach scalable to increasing network sizes, while still planning cooperatively. The methods are applied in simulation to bearings-only sensing, and to localizing an avalanche rescue beacon of a buried victim, using transceivers on quadrotor aircraft to measure the magnetic field. Monte Carlo simulations also demonstrate that as network size increases, the sensors more quickly localize the target, and the pairwise-node approximation results in superior performance to the single-node approximation