Next Article in Journal
Fabry-Perot Interferometric High-Temperature Sensing Up to 1200 °C Based on a Silica Glass Photonic Crystal Fiber
Previous Article in Journal
Himawari-8 Satellite Based Dynamic Monitoring of Grassland Fire in China-Mongolia Border Regions
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(1), 269; https://doi.org/10.3390/s18010269

Joint Probabilistic Data Association Filter with Unknown Detection Probability and Clutter Rate

School of Aerospace, Transport and Manufacturing, Cranfield University, MK43 0AL Cranfield, UK
*
Author to whom correspondence should be addressed.
Received: 15 December 2017 / Revised: 10 January 2018 / Accepted: 16 January 2018 / Published: 18 January 2018
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [831 KB, uploaded 18 January 2018]   |  

Abstract

This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance under unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability and clutter rate estimation. The performance of the proposed JPDA filter is evaluated through empirical tests. The results of the empirical tests show that the proposed JPDA filter has comparable performance with ideal JPDA that is assumed to have perfect knowledge of detection probability and clutter rate. Therefore, the algorithm developed is practical and could be implemented in a wide range of applications. View Full-Text
Keywords: multiple target tracking; joint probabilistic data association; multi-Bernoulli filter; unknown detection probability; unknown clutter rate multiple target tracking; joint probabilistic data association; multi-Bernoulli filter; unknown detection probability; unknown clutter rate
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

He, S.; Shin, H.-S.; Tsourdos, A. Joint Probabilistic Data Association Filter with Unknown Detection Probability and Clutter Rate. Sensors 2018, 18, 269.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top