Decentralized sigma-point information filters for target tracking in collaborative sensor networks

Tracking a target in a cluttered environment is a representative application of sensor networks and a benchmark for collaborative signal processing algorithms. This paper presents a strictly decentralized approach to Bayesian filtering that is well fit for in-network signal processing. By combining the sigma-point filter methodology and the information filter framework, a class of algorithms denoted as sigma-point information filters is developed. These techniques exhibit the robustness and accuracy of the sigma-point filters for nonlinear dynamic inference while being as easily decentralized as the information filters. Furthermore, the computational cost of this approach is equivalent to a local Kalman filter running in each active node while the communication burden can be made linearly growing in the number of sensors involved. The proposed algorithms are then adapted to the specific problem of target tracking with data association ambiguity. Making use of a local probabilistic data association, we formulate a decentralized tracking scheme that significantly outperforms the existing schemes with similar computational and communication complexity.