Gaussian Mixture PHD Filtering with Variable Probability of Detection
2014 (English)In: 17th International Conference on Information Fusion (FUSION), 2014, IEEE , 2014, 1-7 p.Conference paper (Refereed)
The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.
Place, publisher, year, edition, pages
IEEE , 2014. 1-7 p.
Gaussian-Mixture PHD; Multi-Target Tracking; Sonar
Control Engineering Signal Processing
IdentifiersURN: urn:nbn:se:liu:diva-108956ISI: 000363896100043ISBN: 978-849012355-3OAI: oai:DiVA.org:liu-108956DiVA: diva2:734107
17th International Conference on Information Fusion, Salamanca, Spain, July 7-10, 2014
FunderSecurity LinkSwedish Foundation for Strategic Research