Smoothed probabilistic data association filter
Paper in proceedings, 2013

This paper presents the Smoothed Probabilistic Data Association Filter (SmPDAF) that attempts to improve the Gaussian approximations used in the Probabilistic Data Association Filter (PDAF). This is achieved by using information from future measurements. Newer approximations of the densities are obtained by using a combination of expectation propagation, which provides the backward likelihood information from the future measurements, and pruning, which uses these backward likelihoods to reduce the number of components in the Gaussian mixture. Performance comparison between SmPDAF and PDAF shows us that the root mean squared error performance of SmPDAF is significantly better than PDAF under comparable track loss performance.

filtering

pruning

message passing

factor graph

Gaussian mixtures

smoothing

target tracking

expectation propagation

PDA

Author

Abu Sajana Rahmathullah

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Daniel Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Peter Willett

FUSION 2013, 9-12 July 2013, Istanbul, Turkey

1296 - 1303

Areas of Advance

Information and Communication Technology

Subject Categories

Signal Processing

ISBN

978-605-86311-1-3

More information

Created

10/7/2017