Title :
Optimal update with out-of-sequence measurements
Author :
Zhang, Keshu ; Li, X. Rong ; Zhu, Yunmin
Author_Institution :
Dept. of Electr. Eng., Univ. of New Orleans, LA, USA
fDate :
6/1/2005 12:00:00 AM
Abstract :
This paper is concerned with optimal filtering in a distributed multiple sensor system with the so-called out-of-sequence measurements (OOSM). Based on best linear unbiased estimation (BLUE) fusion, we present two algorithms for updating with OOSM that are optimal for the information available at the time of update. Different minimum storages of information concerning the occurrence time of OOSMs are given for both algorithms. It is shown by analysis and simulation results that the two proposed algorithms are flexible and simple.
Keywords :
Kalman filters; filtering theory; least mean squares methods; sensor fusion; target tracking; Kalman filter; MMSE; best linear unbiased estimation fusion; distributed multiple sensor system; optimal filtering; optimal update; out-of-sequence measurement; target tracking; Algorithm design and analysis; Analytical models; Delay effects; Filtering; Filters; Sensor fusion; Sensor systems; State estimation; Target tracking; Time measurement; Kalman filter; LMMSE; out-of-sequence measurement; target tracking;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2005.847830
