Distributed ECM Algorithm for OTHR Multipath Target Tracking With Unknown Ionospheric Heights

• Published in: IEEE journal of selected topics in signal processing Vol. 12; no. 1; pp. 61 - 75
• Main Authors: Lan, Hua, Liang, Yan, Wang, Zengfu, Yang, Feng, Pan, Quan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer simulation; Computing time; Coupling; distributed expectation-conditional maximization; Ionosondes; Ionosphere; Iterative methods; Joint identification and estimation; Maximization; Over-the-horizon radar; Parameter estimation; Parameter identification; Radar tracking; Signal processing algorithms; State estimation; Target recognition; Target tracking; uncertain coordinate registration; Uncertainty
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2017.2787488

Over-the-horizon radar (OTHR) target tracking in the presence of complicated ionospheric environment mainly faces three challenges, i.e., discrete uncertainty of multipath data association, continuous uncertainty of ionospheric heights, and coupling of target state estimation and ionospheric parameters identification. The existing OTHR target tracking algorithms demanded that the ionospheric heights should be exactly known or statistical properties known. However, the ionospheric heights is inaccurate due to the inherent variability of ionosphere, especially when the deployment of ionosondes is unavailable in the sea area or hostile zone. This paper introduces a joint optimization scheme called distributed expectation-conditional maximization (DECM), which solves the target state estimation, multipath data association, and ionospheric heights identification simultaneously. The proposed DECM algorithm consists of a local estimation level and a global fusion level, whereas information is exchanged within these two levels until iteration terminates. This dual-level processing framework transforms the high-dimensional estimation problems into several low-dimensional parallel path-dependent estimation problems, which improves the computational efficiency of expectation maximization under high-dimensional latent variables case. In addition, the closed-loop structure is beneficial to the coupling problem. The simulation indicates the effectiveness of the proposed scheme.