Title :
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
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;
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
DOI :
10.1109/CAMSAP.2007.4497987
