Next Article in Journal
On the Combination of Multi-Cloud and Network Coding for Cost-Efficient Storage in Industrial Applications
Next Article in Special Issue
Extended Target Tracking and Feature Estimation for Optical Sensors Based on the Gaussian Process
Previous Article in Journal
Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine
Previous Article in Special Issue
State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds
Article Menu
Issue 7 (April-1) cover image

Export Article

Open AccessArticle

Tracking Multiple Targets from Multistatic Doppler Radar with Unknown Probability of Detection

1
School of Electrical Engineering, Computing, and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia
2
School of Computer Science, The University of Adelaide, Adelaide, SA 5005, Australia
*
Author to whom correspondence should be addressed.
Current address: ThaiNguyen University of Technology, ThaiNguyen University, ThaiNguyen 251810, Vietnam.
Sensors 2019, 19(7), 1672; https://doi.org/10.3390/s19071672
Received: 1 March 2019 / Revised: 3 April 2019 / Accepted: 4 April 2019 / Published: 8 April 2019
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
  |  
PDF [1860 KB, uploaded 11 April 2019]
  |  

Abstract

The measurements from multistatic radar systems are typically subjected to complicated data association, noise corruption, missed detection, and false alarms. Moreover, most of the current multistatic Doppler radar-based approaches in multitarget tracking are based on the assumption of known detection probability. This assumption can lead to biased or even complete corruption of estimation results. This paper proposes a method for tracking multiple targets from multistatic Doppler radar with unknown detection probability. A closed form labeled multitarget Bayes filter was used to track unknown and time-varying targets with unknown probability of detection in the presence of clutter, misdetection, and association uncertainty. The efficiency of the proposed algorithm was illustrated via numerical simulation examples. View Full-Text
Keywords: multitarget tracking; multistatic Doppler radar; unknown detection probability; Bayes recursion; labeled RFS; GLMB; bootstrapped detection probability multitarget tracking; multistatic Doppler radar; unknown detection probability; Bayes recursion; labeled RFS; GLMB; bootstrapped detection probability
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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Do, C.-T.; Van Nguyen, H. Tracking Multiple Targets from Multistatic Doppler Radar with Unknown Probability of Detection. Sensors 2019, 19, 1672.

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