Paper Info
Title
Two linear complexity particle filters capable of maintaining target label probabilities for targets in close proximity
Abstract
In this work, we introduce two particle filters of linear complexity in the number of particles that take distinct approaches to solving the problem of tracking two targets in close proximity. We operate in the regime in which measurements do not discriminate between targets and hence uncertainties in the labeling of the tracks arise. For simplicity, we limit our study to the two target case for which there are only two possible associations between targets and tracks. The proposed Approximate Set Particle Filter (ASPF) introduces some approximations but has similar complexity and still provides much more accurate descriptions of the posterior uncertainties compared to standard particle filters. The fast Forward Filter Unlabeled Backward Simulator (fast FFUBSi) employs a smoothing technique based on rejection sampling for the calculation of target label probabilities. Simulations show that neither particle filter suffers from track coalescence (when outputting MMOSPA estimates) and both calculate correct target label probabilities.
Year
Venue
Keywords
2012
Information Fusion
particle filtering (numerical methods),probability,smoothing methods,target tracking,ASPF,MMOSPA estimates,approximate set particle filter,fast FFUBSi,fast forward filter unlabeled backward simulator,linear complexity particle filter,rejection sampling,smoothing technique,target label probability,target tracking,track coalescence,Particle filter,linear complexity,target labels
Field
DocType
ISBN
Particle number,Computer vision,Rejection sampling,Mathematical optimization,Computer science,Particle filter,Algorithm,Smoothing,Artificial intelligence,Linear complexity,Coalescence (physics)
Conference
978-0-9824438-4-2
Citations 
PageRank 
References 
6
0.54
6
Authors
4
Name
Order
Citations
PageRank
Ramona Georgescu1455.46
Peter Willett21962224.14
Lennart Svensson338543.46
Mark R. Morelande419524.96