Delayed-State Nonparametric Filtering in Cooperative Tracking

This paper presents a novel nonparametric approach toward delayed-state filtering for cooperative tracking. Standard parametric cooperative localization/tracking approaches are generally aimed at problems that can be easily parameterized and/or are limited to incorporate only real-time measurements. This paper provides a nonparametric yet computationally tractable alternative that is suitable for tracking cases where real-time observations are not always possible, e.g., in a sparse mesh network. The proposed delayed-state cooperative particle filter features forward filtering and backward smoothing to incorporate measurements that are received with time delays. A record of historical marginal states is kept for each mobile node within a sliding time window, instead of the high-dimensional joint state. Essentially, it replaces the importance sampling in traditional particle filters by a Gibbs sampler, which is a Markov chain Monte Carlo method, to fuse all available egocentric and internode relative observations into the global position estimate, thus alleviating the high-dimensionality problems in cooperative tracking. The performance of the proposed approach is evaluated in a multiagent simulation, and experimental results from a large-scale multivehicle industrial operation clearly demonstrate that the proposed approach effectively facilitates the tracking of mobile nodes without position awareness, through the use of relative range, negative detection, and time-delayed measurements.