Monte Carlo Initialization for Multi-Sensor Bearing Only Tracking

Author

Housfater, Alon Shalev ; Zhang, Xiao-Ping ; Zhou, Yifeng

Author_Institution

Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON

fYear

2007

fDate

12-14 Dec. 2007

Firstpage

149

Lastpage

152

Abstract

A new algorithm for particle filter initialization for multi-sensor bearing only tracking is developed to enhance tracker performance and stability. Multiple bearing observations are used by a least squares technique to form multiple initial position estimates; these estimates are in turn used to compute the statistics of the initial state distribution. Simulated data is used to demonstrate the performance and efficiency of the algorithm by comparing the new initialization technique to a filter initialized with the true initial state.

Keywords

Monte Carlo methods; least mean squares methods; particle filtering (numerical methods); sensor fusion; Monte Carlo initialization; least squares technique; multisensor bearing only tracking; particle filter initialization; Adaptive filters; Electronic mail; Filter bank; Least squares approximation; Monte Carlo methods; Particle filters; Particle tracking; Sampling methods; State estimation; State-space methods; Adaptive filters; Electronic countermeasures; Monte Carlo methods; Tracking filters;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Advances in Multi-Sensor Adaptive Processing, 2007. CAMPSAP 2007. 2nd IEEE International Workshop on

Conference_Location

St. Thomas, VI

Print_ISBN

978-1-4244-1713-1

Electronic_ISBN

978-1-4244-1714-8

Type

conf

DOI

10.1109/CAMSAP.2007.4497987

Filename

4497987