Multiple model approach to over-the-horizon radar tracking
Leilei Guo, Jian Lan, X. Rong Li · 2017
For the over-the-horizon radar (OTHR) based target tracking, the reflecting height of the ionosphere, which reflects radar signals, is important. Existing methods assume that this height is exactly known a priori. In practice, however, we can only determine its range, not its specific value. To circumvent this problem, we propose to use a multiple-model approach in which each model corresponds to a specific height for OTHR tracking. More specifically, first an autonomous multiple-model multipath probabilistic data association (MPDA) tracker is proposed to estimate the target state. To handle the problem that a small fixed model set does not cover a large height range well, an expected-mode augmentation MPDA tracker is proposed, which augments a basic model set by an online expected mode of the height. Considering that the model is a nonlinear function of the height, we also present a best model augmentation MPDA tracker, in which a basic model set is augmented by a best candidate model that minimizes a Kullback-Leiber (K-L) divergence. This algorithm utilizes the nonlinear relationship of the model with the height. Simulation results demonstrate that the proposed algorithms are effective and have better performance than existing MPDA algorithm.