Trajectory estimation for ballistic missile in boost stage using robust filtering. Issue 3 (11th January 2017)

• Record Type: Journal Article
• Authors: Wang, Xiaogang; Qin, Wutao; Bai, Yuliang; Cui, Naigang
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 3(2017)
• Page Start: 513
• Page End: 519
• Publication Date: 2017-01-11
• Subjects: missiles -- nonlinear filters -- Gaussian processes -- covariance analysis -- nonlinear equations -- target tracking; l2 norm estimation -- l1 norm estimation -- nonlinear filtering -- tracking performance -- satellite line‐of‐sight measurements -- ballistic missile tracking -- nonGaussian noise -- measurement noise -- HCHF -- measurement update -- covariance computation -- weighted means computation -- nonlinear state equations -- Gaussian‐weighted integrals -- fifth‐degree cubature rule -- high‐degree cubature Huber‐based filtering -- robust filtering -- boost stage -- trajectory estimation; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2016.0335
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : This study proposes a new estimation method, referred as the high‐degree cubature Huber‐based filtering (HCHF). The novel algorithm makes use of the fifth‐degree cubature rule to numerically compute Gaussian‐weighted integrals, which, with a set of non‐linear state equations, are used to compute weighted means and covariance required for measurement update. The measurement update is designed based on the Huber technique which is a combined minimum of l 1 and l 2 norm estimation. Therefore, the HCHF could exhibit robustness when the measurement noise is highly non‐Gaussian. The method is applied for tracking ballistic missile in presence of non‐Gaussian noise in satellite line‐of‐sight measurements. The tracking performance is compared with that of extended Kalman filtering and conventional cubature Kalman filtering. The simulation results verified the effectiveness of the proposed non‐linear filtering.