Title :
Efficient Delay-Tolerant Particle Filtering
Author :
Oreshkin, Boris N. ; Liu, Xuan ; Coates, Mark J.
Author_Institution :
Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada
fDate :
7/1/2011 12:00:00 AM
Abstract :
This paper proposes a novel framework for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. Within this framework the informativeness of a delayed (out-of-sequence) measurement (OOSM) is estimated using a lightweight procedure and uninformative measurements are immediately discarded. The framework requires the identification of a threshold that separates informative from uninformative; this threshold selection task is formulated as a constrained optimization problem, where the goal is to minimize state estimation error whilst controlling the computational requirements. We develop an algorithm that provides an approximate solution for the optimization problem. Simulation experiments provide an example where the proposed framework processes less than 40% of all OOSMs with only a small reduction in state estimation accuracy.
Keywords :
delays; optimisation; particle filtering (numerical methods); OOSM; constrained optimization problem; delay-tolerant particle filtering; out-of-sequence measurement; state estimation error; uninformative measurement; Atmospheric measurements; Current measurement; Markov processes; Optimization; Particle measurements; State estimation; Time measurement; Out of sequence measurement (OOSM); particle filtering; resource management; tracking;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2011.2140110