Target State and Markovian Jump Ionospheric Height Bias Estimation for OTHR Tracking Systems

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 50; no. 7; pp. 2599 - 2611
• Main Authors: Geng, Hang, Liang, Yan, Cheng, Yuhua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bias; Bias estimation; Clutter; Estimation; Frequency measurement; Ionosondes; Ionosphere; Iterative methods; Markovian jump system (MJS); Mathematical models; multipath measurement; Optimization; Over-the-horizon radar; over-the-horizon radar (OTHR); Parameter estimation; Propagation modes; Radar tracking; Randomness; Target tracking; Tracking; Tracking systems
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2018.2822819

Ionospheric heights provided by ionosondes are vital for over-the-horizon radar (OTHR) target tracking. However, biases contained in the provided ionospheric heights definitely cause degradation of the tracking performance. This paper is concerned with the joint estimation problem of the target state and the ionospheric height bias for OTHR target tracking subject to multipath and cluttered measurements. A Markovian jump ionospheric height bias model is presented by simultaneously considering the intermittent and abrupt ionosphere changes. Meanwhile, a set of stochastic variables are adopted to depict association uncertainties among measurements, clutters, and propagation modes so that association is embedded in the resultant measurement model with random coefficients. Through such modeling transformation from association uncertainties to parameter randomness, the coupling processing of data association and state estimate is equivalent to pure state estimate subject to stochastic parameters. Furthermore, an optimal linear joint estimator containing causality constraints is developed in the minimum mean-squared error sense, and further extended to the case of nonlinear measurement model via iterative optimization. A target tracking example with four resolvable propagation modes illustrates the effectiveness of the proposed estimation scheme.