JOURNAL ARTICLE

Nonlinear multiple model particle filters algorithm for tracking multiple targets

Abdennour SebbaghHicham Tebbikh

Year: 2011 Journal:   Archives of Control Sciences Vol: 21 (1)   Publisher: De Gruyter Open

Abstract

Nonlinear multiple model particle filters algorithm for tracking multiple targets ABDENNOUR SEBBAGH and HICHAM TEBBIKHThe paper addresses multiple targets tracking problem encountered in number of situations in signal and image processing.In this paper, we present an efficient filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, which we aim to contribute in solving the problem of multiple targets tracking using bearings-only measurements.The idea of this algorithm consists of the combination between the multiple model approach and particle filtering methods, which give a nonlinear multiple model particle filters algorithm.This algorithm is used to estimate the trajectories of multiple targets assumed to be nonlinear, from their noisy bearings.

Keywords:
Particle filter Tracking (education) Nonlinear system Algorithm Computer science Nonlinear model Auxiliary particle filter Control theory (sociology) Artificial intelligence Kalman filter Extended Kalman filter Ensemble Kalman filter Physics Control (management)

Metrics

2
Cited By
0.00
FWCI (Field Weighted Citation Impact)
24
Refs
0.11
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Remote Sensing and Land Use
Physical Sciences →  Earth and Planetary Sciences →  Atmospheric Science
Remote-Sensing Image Classification
Physical Sciences →  Engineering →  Media Technology

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