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Sensors 2014, 14(12), 23067-23094; doi:10.3390/s141223067

A Cognition-Based Method to Ease the Computational Load for an Extended Kalman Filter

School of Electrical Science and Engineering, National University of Defense Technology, 137 Yanwachi Street, Changsha 410073, China
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Received: 22 September 2014 / Revised: 27 October 2014 / Accepted: 27 November 2014 / Published: 3 December 2014
(This article belongs to the Section Physical Sensors)
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Abstract

The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is proposed to ease the computational load for EKF in azimuth predicting and localizing under a nonlinear observation model. When there are nonlinear functions and inverse calculations for matrices, this method makes use of the major components (according to current performance and the performance requirements) in the Taylor expansion. As a result, the computational load is greatly lowered and the performance is ensured. Simulation results show that the proposed measure will deliver filtering output with a similar precision compared to the regular EKF. At the same time, the computational load is substantially lowered. View Full-Text
Keywords: computational load; extended Kalman filter; target localizing; target tracking computational load; extended Kalman filter; target localizing; target tracking
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, Y.; Li, X.; Deng, B.; Wang, H.; Qin, Y. A Cognition-Based Method to Ease the Computational Load for an Extended Kalman Filter. Sensors 2014, 14, 23067-23094.

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