Censoring and Fusion in Non-linear Distributed Tracking Systems with Application to 2D Radar

摘要

The objective of this research is to study various methods for censoring state estimate updates generated from radar measurements. The generated 2-D radar data are sent to a fusion center using the J-Divergence metric as the means to assess the quality of the data. Three different distributed sensor network architectures are considered which include different levels of feedback. The Extended Kalman Filter (EKF) and the Gaussian Particle Filter (GPF) were used in order to test the censoring methods in scenarios which vary in their degrees of non-linearity. A derivation for the direct calculation of the J-Divergence using a particle filter is provided. Results show that state estimate updates can be censored using the J-Divergence as a metric controlled via feedback, with higher J-Divergence thresholds leading to a larger covariance at the fusion center.