Hyperbolic-SVD-Based Square-Root Unscented Kalman Filters in Continuous-Discrete Target Tracking Scenarios

• 成果类型: Article
• 署名作者: Kulikov, Gennady Yu; Kulikova, Maria, V
• 署名单位: Universidade de Lisboa
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3056338
• 发表日期: 2022
• 页码: 366-373
• 关键词: Filtering algorithms; nonlinear filters; Numerical stability; radar tracking; State estimation; Stochastic systems

摘要

This article aims at presenting novel square-root un-scented Kalman filters (UKFs) for treating various continuous-discrete nonlinear stochastic systems, including target tracking scenarios. These new methods are grounded in the commonly used singular value decomposition (SVD), that is, they propagate not the covariance matrix itself but its SVD factors instead. The SVD based on orthogonal transforms is applicable to any UKF with only nonnegative weights, whereas the remaining ones, which can enjoy negative weights as well, are treated by means of the hyperbolic SVD based on J-orthogonal transforms. The filters constructed are presented in a concise algorithmic form, which is convenient for practical utilization. Their two particular versions grounded in the classical and cubature UKF parameterizations and derived with use of the Ito-Taylor discretization are examined in severe conditions of tackling a seven-dimensional radar tracking problem, where an aircraft executes a coordinated turn, in the presence of ill-conditioned measurements.