Distributed adaptive two-stage Kalman filter for target tracking in the presence of unknown dynamic bias

Author

Zhang Cui ; Jia Yingmin ; Du Junping ; Zhang Jun

Author_Institution

Dept. of Syst. & Control, Beihang Univ., Beijing, China

fYear

2014

fDate

28-30 July 2014

Firstpage

7216

Lastpage

7221

Abstract

This paper is concerned with the problem of tracking target with multiple sensors in the presence of unknown dynamic bias. A suboptimal adaptive two-stage Kalman filter (ATKF) is designed with two reduce-order filters to estimate the target state and the dynamic bias in parallel when the bias model information is incomplete. Moreover, a distributed adaptive two-stage Kalman filter (DATKF) is developed for multi-sensor system based on the ATKF. The effectiveness of the ATKF and the DATKF are illustrated by the Monte Carlo simulation results.

Keywords

Kalman filters; Monte Carlo methods; sensor fusion; target tracking; DATKF; Monte Carlo simulation results; bias model information; distributed adaptive two-stage Kalman filter; multiple sensors; multisensor system; suboptimal adaptive two-stage Kalman filter; target tracking; unknown dynamic bias; Adaptation models; Equations; Kalman filters; Mathematical model; Sensor fusion; Vectors; Adaptive Two-stage Kalman Filter; Distributed Fusion; Dynamic Bias; Multi-sensor System;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2014 33rd Chinese

Conference_Location

Nanjing

Type

conf

DOI

10.1109/ChiCC.2014.6896193

Filename

6896193