Title :
A new self-tuning Kalman filter for tracking abrupt input change
Author :
Li, Xuwen ; Wu, Qiang ; Wu, Shuicai
Author_Institution :
Coll. of Life Sci. & Bio-Eng., Beijing Univ. of Lechnology, Beijing, China
Abstract :
This paper proposes a new self-tuning Kalman filter with good tracking ability for unknown noise statistics and unknown abrupt input change. The new filter can easily compute unknown abrupt input and steady-state gain matrix by building up online identification of ARMAX innovation model in real time. The simulation results of tracking a maneuvering target shows the effectiveness of the new method in this paper.
Keywords :
Kalman filters; autoregressive moving average processes; target tracking; ARMAX innovation model; abrupt input change tracking; autoregressive moving average model; maneuvering target tracking; noise statistics; self-tuning Kalman filter; steady-state gain matrix; Adaptive filters; Educational institutions; Estimation; Kalman filters; Noise; Steady-state; Target tracking; Kalman filter; adaptive filter; colored noise; multiple delays;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2011 International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4244-8162-0
DOI :
10.1109/ICECENG.2011.6057815
