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

Sensors

Yanpeng Li¹  Xiang Li²  Bin Deng²  Hongqiang Wang² 

[1] School of Electrical Science and Engineering, National University of Defense Technology, 137 Yanwachi Street, Changsha 410073, China;

关键词: computational load;    extended Kalman filter;    target localizing;    target tracking;   

DOI  :  10.3390/s141223067

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.