A Reduced-Complexity Interacting Multiple Model Algorithm for Location Tracking in Heterogeneous Observation

Xiaoyan Fu, Yuanyuan Shang
Corresponding Author
Xiaoyan Fu
Available Online 1 December 2015.
data fusion, interacting multiple model algorithm, location tracking, wireless sensor networks.
This paper is devoted to the problem of state estimate of discrete-time stochastic systems. A low-complexity and high accuracy algorithm is presented to reduce the computational load of the traditional interacting multiple model algorithm with heterogeneous observations for location tracking. By decoupling the x and y dimensions to simplify the implementation of location, updated information is iteratively passed based on an adaptive fusion decision. Simulations show that the algorithm is more computationally attractive than existing multiple model methods.

© 2013, the Authors. Published by ALife Robotics Corp. Ltd
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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