Censoring distributed nonlinear state estimates in radar networks

In a distributed radar track fusion system, it is desired to limit the communication rate between the sensors and the central node to only the most relevant information available. One way to do this is to use some metric that judges quantity of new information available, in comparison to that which has already been provided. The J-Divergence is a symmetric metric, derived from the Kullback-Liebler divergence, which performs a comparison of the statistical distance between two probability distributions. For the comparison between new and old data, a large J-Divergence can represent the existence of new information, while a small J-Divergence represents the lack of new information. Previous work included an application where the J-Divergence was used to limit data for scenarios in which the primary state estimator was an Extended Kalman Filter and used only Gaussian approximations at the local sensors. This paper expands the range of estimators to particle filters in order to account for situations where censoring is desired to be applied to non-linear/non-Gaussian environments. A derivation of the J-Divergence between probability density functions (PDFs) which are approximated by particles is provided for use in a non-feedback fusion case. An example application is given involving a 2D radar tracking scenario using the J-Divergences of a particle filter with the Gaussian approximation and a particle filter with the approximated discrete prior/posterior PDFs.