Localization of multiple emitters based on the sequential PHD filter

Authors:
Hongjian Zhang;Zhongliang Jing;Shiqiang Hu
Affiliations:
Shanghai Jiao Tong University, Shanghai 200240, PR China;Shanghai Jiao Tong University, Shanghai 200240, PR China;Shanghai Jiao Tong University, Shanghai 200240, PR China
Venue:
Signal Processing
Year:
2010

Citing 7
Cited 3

Mathematics of Data Fusion

Mathematics of Data Fusion
Statistical Multisource-Multitarget Information Fusion

Statistical Multisource-Multitarget Information Fusion
Convergence Analysis of the Gaussian Mixture PHD Filter

IEEE Transactions on Signal Processing
The Gaussian Mixture Probability Hypothesis Density Filter

IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter

IEEE Transactions on Signal Processing - Part II
Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach

IEEE Transactions on Signal Processing
Multitarget miss distance via optimal assignment

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Fast communication: Gaussian mixture PHD filter for jump Markov models based on best-fitting Gaussian approximation

Signal Processing
Detection-guided multi-target Bayesian filter

Signal Processing
Adaptive multifeature visual tracking in a probability-hypothesis-density filtering framework

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

The localization of multiple emitters from passive angle measurements is a widely investigated problem. Traditionally, the central problem of state estimation for multiple targets by multiple passive sensors is data association. Mathematically, the formulation of the data association problem leads to a generalization of an S-dimensional (S-D) assignment problem. Unfortunately, the complexity of solving an S-D assignment problem for S=3 is NP hard. A practical solution is to solve the multidimensional assignment problem using multistage Lagrangian relaxation. However, the computational requirements of it explode with the number of sensors. Additionally, it cannot give satisfactory results in dense clutter environment. In this paper, the sequential probability hypothesis density (PHD) filter using passive sensors in two different manners for localization of multiple emitters is introduced. Simulation results show that the sequential PHD filter can achieve better performance with smaller computational complexity than the method based on S-D assignment programming in dense clutter environment.