Improving Accuracy by Iterated Multiple Particle Filtering
Pau Closas, Mónica F. Bugallo · IEEE Signal Processing Letters · 2012
This paper analyzes and validates an enhanced implementation of the multiple particle filter that improves its accuracy when applied to high dimensional problems. The algorithm combines the divide et impera philosophy of the multiple particle filter, which avoids the collapse of traditional particle filters, with game theory strategies that provide with a powerful tool to improve the performance. The problem of multiple target tracking with received signal strength measurements is addressed and the results show remarkable improvement over both standard particle filtering and multiple particle filtering.