Adaptive filter of non‐linear systems with generalised unknown disturbances. Issue 4 (1st April 2014)

• Record Type: Journal Article
• Authors: Qin, Yuemei; Liang, Yan; Yang, Yanbo; Wang, Zengfu; Yang, Feng
• Is Part Of: IET radar, sonar & navigation. Volume 8:Issue 4(2014)
• Page Start: 307
• Page End: 317
• Publication Date: 2014-04-01
• Subjects: adaptive filters -- discrete time filters -- iterative methods -- nonlinear filters -- optimisation -- parameter estimation -- state estimation -- stochastic processes -- target tracking -- time‐varying filters; adaptive filter -- state estimation problem -- discrete‐time nonlinear stochastic system -- generalised unknown disturbance -- GUD -- filter design -- upper‐bound filter -- linear time‐varying system -- measurement equation -- optimal parameter -- iterative optimisation -- parameter identification -- linearised measurement matrix -- target tracking -- biased radar -- biased electronic support measure; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2013.0031
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : This study presents the state estimation problem of discrete‐time non‐linear stochastic system with generalised unknown disturbance (GUD) in the measurements. To the best of our knowledge, there is no research result on the filter design dealing with such estimation problem. Here, an upper‐bound filter is designed for the linear time‐varying system with the GUD existing in the measurement equation, and optimal parameters are derived so that the minimum upper‐bounds filter is obtained. The result is further extended to the non‐linear case via the iterative optimisation of joint state estimation and parameter identification (the linearised measurement matrix). The simulation about tracking a target via one biased radar and one biased electronic support measure (ESM) shows the effectiveness and robustness of the proposed filter.