Multitarget bearings-only tracking using fuzzy clustering technique and Gaussian particle filter

Authors:
Jungen Zhang;Hongbing Ji;Cheng Ouyang
Affiliations:
School of Electronic Engineering, Xidian University, Xi'an, China;School of Electronic Engineering, Xidian University, Xi'an, China;School of Electronic Engineering, Xidian University, Xi'an, China
Venue:
The Journal of Supercomputing
Year:
2011

Citing 8
Cited 0

Tracking and data association

Tracking and data association
Fuzzy logic approach to multisensor data association

Mathematics and Computers in Simulation
On sequential Monte Carlo sampling methods for Bayesian filtering

Statistics and Computing
Hybrid fuzzy probabilistic data association filter and joint probabilistic data association filter

Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Maximum entropy fuzzy clustering with application to real-time target tracking

Signal Processing - Special section: Distributed source coding
Gaussian particle filtering

IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Online data-driven fuzzy clustering with applications to real-time robotic tracking

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel multitarget bearings-only tracking algorithm that combines the fuzzy clustering data association technique together with a Gaussian particle filter (GPF) is presented. Firstly, to deal with the data association problem that arises due to the uncertainty of the measurements, the fuzzy clustering method with the maximum entropy principle is utilized, which eliminates those invalid measurements. Secondly, this paper employs GPF to update each target state independently, since it has a much-improved performance and versatility over other Gaussian filters, especially when nontrivial nonlinearities are present. Moreover, in the multisensor scenario, a statistic test method based on the cotangent values of bearings is proposed, for associating the target bearing data observed at each sensor. Simulation results demonstrate the effectiveness of the algorithm.