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multiple model adaptive estimator for Nonlinear System with Unknown Disturbance

ASIAN JOURNAL OF CONTROL 2015年第6期17卷 2252-2262页

作者： Xiong, Kai Wei, Chunling Beijing Inst Control Engn Sci & Technol Space Intelligent Control Lab Beijing 100190 Peoples R China

A multiple model adaptive estimator (MMAE) is presented for nonlinear systems with unknown disturbances. multiple models are constructed with a set of process noise covariance matrices, such that the algorithm can adapt to different levels of unknown disturbances. The performance of the MMAE is analyzed for the considered system. It is proved that, under certain assumptions, the MMAE keeps the dynamics of its estimation error stable. A performance comparison among different estimators is carried out for space surveillance, where the position of a space target is estimated by using double line-of-sight measurements. Simulation studies illustrate that the presented algorithm outperforms the extended Kalman filter and the nonlinear robust filter.

关键词： multiple model adaptive estimator nonlinear robust filter space surveillance line-of-sight

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A NECESSARY CONDITION FOR EFFECTIVE PERFORMANCE OF THE multiple model adaptive estimator

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1995年第3期31卷 1132-1139页

作者： CAPUTI, MJ Dept. of Eng. Hofstra Univ. Hempstead NY USA

Effective adaptive estimation for a general linear system driven by an input modeled by a randomly switching Gaussian process is considered. The performance of the multiple model adaptive estimator (MMAE) is, in some cases, unexpectedly hampered by a necessary condition not satisfied by the linear system. This key dependency for effective MMAE performance is based on a particular property of the DC gain of the linear system.

关键词： Gaussian processes adaptive Kalman filters adaptive estimation adaptive signal processing DC gain effective performance general linear system linear system multiple model adaptive estimator randomly switching Gaussian process adaptive signal processing Gaussian processes adaptive estimation adaptive Kalman filters Linear system

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Spacecraft autonomous navigation using multiple model adaptive estimator

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2015年第5期87卷 465-475页

作者： Xiong, Kai Wei, Chunling Liu, Liangdong Beijing Inst Control Engn Dept Sci & Technol Space Intelligent Control Lab Beijing Peoples R China

Purpose - The purpose of this paper is to present a variable structure multiple model adaptive estimator (VSMMAE) for liaison navigation system. Liaison navigation is an autonomous navigation method where inter-satellite range measurements are used to estimate the orbits of all participating spacecrafts simultaneously. Design/methodology/approach - To overcome the problem caused by an inaccurate initial state, a navigation algorithm is designed based on the multiple model adaptive estimation technique. The multiple models are constructed by different initial error covariance matrices. To reduce the computational cost, the likely-model set (LMS) algorithm is adopted to eliminate the unlikely models. Findings - It is specified that the performance of the liaison navigation based on the extended Kalman filter (EKF) is sensitive to the initial error. Simulation results show that the VSMMAE outperforms the EKF in the presence of a large initial error. Practical implications - The presented algorithm is applicable to spacecraft autonomous navigation. Originality/value - A novel navigation algorithm based on the VSMMAE is developed. It is an effective method for the liaison navigation system.

关键词： Autonomous navigation Liaison navigation multiple model adaptive estimator Spacecraft

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Spacecraft Relative Navigation Based on multiple model adaptive estimator 33

Spacecraft Relative Navigation Based on Multiple Model Adapt...

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33rd Chinese Control Conference (CCC)

作者： Xiong Kai Wei Chunling Liu Liangdong Beijing Inst Control Engn Beijing 100190 Peoples R China Sci & Technol Space Intelligent Control Lab Beijing 100190 Peoples R China

ISBN: (纸本)9789881563842

This paper studies the multiple model adaptive estimator (MMAE) for nonlinear systems with unknown disturbances. multiple models are constructed with a set of process noise covariance matrices, such that the algorithm that consists of multiple parallel filters can adapt to different levels of unknown disturbances. The filtering stability of the MMAE is analyzed. Sufficient conditions to ensure the boundedness of the algorithm is provided. A performance comparison among an extended Kalman filter (EKF), a nonlinear robust filter (NRF) and the MMAE is carried out for spacecraft relative navigation, where the position of a space target is estimated by using double line-of-sight (LOS) measurements. Simulation studies illustrate that the MMAE performs better than the EKF and the NRF.

关键词： multiple model adaptive estimator robust Kalman filter spacecraft relative navigation

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NONLINEAR SYSTEM IDENTIFICATION USING CONSTELLATION BASED multiple model adaptive estimatorS 22

NONLINEAR SYSTEM IDENTIFICATION USING CONSTELLATION BASED MU...

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22nd European Signal Processing Conference (EUSIPCO)

作者： Martins, Joao C. Caeiro, Jose Jasnau Sousa, Leonel A. INESC ID Lisbon Portugal Escuela Super Tecnol & Gestao Inst Politecn Beja Beja Portugal Univ Lisbon Inst Super Tecn P-1699 Lisbon Portugal

ISBN: (纸本)9780992862619

This paper describes the application of the constellation based multiple model adaptive estimation (CBMMAE) algorithm to the identification and parameter estimation of nonlinear systems. The method was successfully applied to the identification of linear systems both stationary and nonstationary, being able to fine tune its parameters. The method starts by establishing a minimum set of models that are geometrically arranged in the space spanned by the unknown parameters, and adopts a strategy to adaptively update the constellation models in the parameter space in order to find the model resembling the system under identification. By downscaling the models parameters the constellation is shrunk, reducing the uncertainty of the parameters estimation. Simulations are presented to exhibit the application of the framework and the performance of the algorithm to the identification and parameters estimation of nonlinear systems.

关键词： Dynamic systems identification: suboptimal state estimation multiple model adaptive estimator parameter estimation extended Kalman filter unscented Kalman filter

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NONLINEAR SYSTEM IDENTIFICATION USING CONSTELLATION BASED multiple model adaptive estimatorS

NONLINEAR SYSTEM IDENTIFICATION USING CONSTELLATION BASED MU...

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European Signal Processing Conference

作者： Joao C. Martins Jose Jasnau Caeiro Leonel A. Sousa INESC-ID

ISBN: (纸本)9781479946037

关键词： Dynamic systems identification Sub-optimal state estimation multiple model adaptive estimator Parameter estimation Extended Kalman filter Unscented Kalman filter

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SLAM for an AUV using vision and an acoustic beacon

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IFAC Proceedings Volumes 2010年第16期43卷 503-508页

作者： M. Bayat A. Pedro Aguiar Institute for Systems and Robotics (ISR) Instituto Superior Técnico (IST) Lisbon Portugal

The estimation of the position and attitude of an autonomous underwater vehicle (AUV) is a challenging and important problem in marine robotics. It is well known that the underwater environment posses considerable problems, that include i) the fact that there is no GPS signal, ii) the communication is usually done through acoustic signals, which suffers from faults, delays and low bandwidth, and iii) the use of vision and/or laser is very limited due to poor visibility. In this paper, we combine a multiple set of sensors to address the full state 6DOF pose estimation of an AUV. The problem is formulated assuming that we have partial measurements from an Inertial Measurement Unit (IMU), an acoustic ranging from a single beacon buoy, and a monocular camera attached to the AUV. Using multiple model estimation techniques and the concept of Extended Kalman Filters with Simultaneous Localization and Mapping (EKF-SLAM), we propose an algorithm that integrates the AUV measurements (that arrive at different sampling-times) and compute in real time an estimate of the position and attitude of the AUV. Simulation results are presented and discussed.

关键词： Underwater Vision IMU Extended Kalman Filter multiple model adaptive estimator Simultaneous Localization And Mapping Pose Estimation

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adaptive Estimation and Cooperative Guidance for Active Aircraft Defense in Stochastic Scenario

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SENSORS 2019年第4期19卷 979-979页

作者： Fang, Feng Cai, Yuanli Yu, Zhenhua Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Air Force Engn Univ Sch Informat & Nav Xian 710077 Shaanxi Peoples R China

The active aircraft defense problem is investigated for the stochastic scenario wherein a defending missile (or a defender) is employed to protect a target aircraft from an attacking missile whose pursuit guidance strategy is unknown. For the purpose of identifying the guidance strategy, the static multiple model estimator (sMME) based on the square-root cubature Kalman filter is proposed, and each model represents a potential attacking missile guidance strategy. Furthermore, an estimation enhancement approach is provided by using pseudo-measurement. For each model in the sMME, the model-matched cooperative guidance laws for the target and defender are derived by formulating the active defense problem as a constrained linear quadratic problem, where an accurate defensive interception and the minimum evasion miss distance are both considered. The proposed adaptive cooperative guidance laws are the result of mixing the model-matched optimal cooperative guidance laws in the criterion of maximum a posteriori probability in the framework of the sMME. By adopting the adaptive cooperative guidance laws, the target can facilitate the defender's interception with the attacking missile with less control effort. Also, simulation results show that the proposed guidance laws increase the probability of successful target protection in the stochastic scenario compared with other defensive guidance laws.

关键词： adaptive cooperative guidance multiple model adaptive estimator square-root cubature Kalman filter estimation enhancement active defense

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