Title :
Improving performance of radar trackers by using H∞ techniques
Abstract :
Kalman filters and extended Kalman filters are extensively used as observers in radar trackers in order to estimate the future position of the target. Kalman filters are linear filters while extended Kalman filters are non linear in nature. Extended Kalman filters therefore show better performance because A,B,C and D matrices are continuously updated. In both cases however basic LQR techniques are used to calculate the state space matrices. The aim of this paper is to design a H∞ observer by robustifing the Blackman´s state space plant model through H∞ loop shaping procedure so that it can tolerate maximum uncertainty. The paper also compares the performance of the designed observer with an extended Kalman filter in order to demonstrate the superiority of the H∞ technique.
Keywords :
Kalman filters; observers; radar tracking; ∞ techniques; Blackman state space plant model; Kalman filters; extended Kalman filters; linear filters; radar trackers; Nonlinear filters; Predictive models; Radar tracking; Riccati equations; Robust control; Robustness; Stability; State-space methods; Target tracking; Uncertainty;
Conference_Titel :
Emerging Technologies, 2005. Proceedings of the IEEE Symposium on
Print_ISBN :
0-7803-9247-7
DOI :
10.1109/ICET.2005.1558873