Next Article in Journal
Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
Previous Article in Journal
Attention-Based Joint Entity Linking with Entity Embedding
Article Menu

Export Article

Open AccessArticle
Information 2019, 10(2), 48;

Fuzzy-Logic-Based, Obstacle Information-Aided Multiple-Model Target Tracking

School of Information Engineering, Lingnan Normal University, Zhanjiang 524000, China
College of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China
Chinese PLA Army Artillery Air Defense Academy Zhengzhou Campus, Zhengzhou 450000, China
Author to whom correspondence should be addressed.
Received: 8 December 2018 / Revised: 25 January 2019 / Accepted: 28 January 2019 / Published: 2 February 2019
(This article belongs to the Section Information Processes)
Full-Text   |   PDF [6290 KB, uploaded 3 February 2019]   |  
  |   Review Reports


Incorporating obstacle information into maneuvering target-tracking algorithms may lead to a better performance when the target when the target maneuver is caused by avoiding collision with obstacles. In this paper, we propose a fuzzy-logic-based method incorporating new obstacle information into the interacting multiple-model (IMM) algorithm (FOIA-MM). We use convex polygons to describe the obstacles and then extract the distance from and the field angle of these obstacle convex polygons to the predicted target position as obstacle information. This information is fed to two fuzzy logic inference systems; one system outputs the model weights to their probabilities, the other yields the expected sojourn time of the models for the transition probability matrix assignment. Finally, simulation experiments and an Unmanned Aerial Vehicle experiment are carried out to demonstrate the efficiency and effectiveness of the proposed algorithm. View Full-Text
Keywords: target tracking; multiple model estimation; obstacle information; fuzzy inference target tracking; multiple model estimation; obstacle information; fuzzy inference

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

Wang, Q.; Fan, E.; Li, P. Fuzzy-Logic-Based, Obstacle Information-Aided Multiple-Model Target Tracking. Information 2019, 10, 48.

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



[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top