An optimization approach to adaptive Kalman filtering

被引：116

作者：

Karasalo, Maja ^{[2
]}

Hu, Xiaoming ^{[1
]}

机构：

[1] Royal Inst Technol, S-10044 Stockholm, Sweden

[2] Saab, Secur & Def Solut, S-17588 Jarfalla, Sweden

来源：

AUTOMATICA | 2011年 / 47卷 / 08期

基金：

瑞典研究理事会;

关键词：

Adaptive filtering; Optimization; Tracking; MULTIPLE-MODEL ESTIMATION; VARIABLE-STRUCTURE; PART V;

D O I：

10.1016/j.automatica.2011.04.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an optimization-based adaptive Kalman filtering method is proposed. The method produces an estimate of the process noise covariance matrix Q by solving an optimization problem over a short window of data. The algorithm recovers the observations h(x) from a system (x) over dot = f (x), y = h(x) + v without a priori knowledge of system dynamics. Potential applications include target tracking using a network of nonlinear sensors, servoing, mapping, and localization. The algorithm is demonstrated in simulations on a tracking example for a target with coupled and nonlinear kinematics. Simulations indicate superiority over a standard MMAE algorithm for a large class of systems. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：1785 / 1793

页数：9

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