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Improved multiple-model adaptive estimation Method for Integrated Navigation with Time-Varying Noise

SENSORS 2022年第16期22卷 1151-1167页

作者： Song, Jinhao Li, Jie Wei, Xiaokai Hu, Chenjun Zhang, Zeyu Zhao, Lening Jiao, Yubing North Univ China Natl Key Lab Elect Measurement Technol Taiyuan 030051 Peoples R China Aerosp Syst Engn Shanghai Shanghai 201108 Peoples R China

The accurate noise parameter is essential for the Kalman filter to obtain optimal estimates. However, problems such as variations in the noise environment and measurement anomalies can cause degradation of estimation accuracy or even divergence. The adaptive Kalman filter can simultaneously estimate state and noise parameters, while its performance will also be degraded in complex noise. To address the problem of estimation accuracy degradation and result divergence of the integrated navigation system in a complex time-varying noise environment, an improved multiple-model adaptive estimation (MMAE) that combines the Sage-Husa adaptive unscented Kalman filter with the MMAE is proposed in this paper. The forgetting factor is included as an unknown parameter of MMAE so that the algorithm can adjust the value of the forgetting factor according to different system states. In addition, we improve the hypothesis testing algorithm of classical MMAE to deal with the competition problem of undesirable models that severely impacts the performance of variable-parameter MMAE and enhance the algorithm's parameter identification capability. Simulation results show that this method enhances the system's robustness to noises of different statistical properties and improves the estimation accuracy of the filter in time-varying noise environments.

关键词： multiple-model adaptive estimation Sage-Husa adaptive Kalman filter

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multiple-model adaptive estimation for space surveillance with measurement uncertainty

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OPTIMAL CONTROL APPLICATIONS & METHODS 2016年第2期37卷 404-423页

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

An efficient multiple-model adaptive estimation (MMAE) algorithm is presented for time-variant system with both system and measurement uncertainties, whose statistics are supposed to be unknown. The model uncertainties are described by a set of noise covariance matrices, such that a small model set is sufficient to achieve good estimation performance. To demonstrate the feasibility of the presented MMAE for the considered time-variant uncertain system, a proof is provided that shows the filtering convergence. The performance of the algorithm is evaluated via different operating scenarios of double line-of-sight measuring space surveillance. Simulation results demonstrate that the MMAE algorithm outperforms the robust filtering algorithms in the presence of the uncertainty and yields positioning accuracy similar to the extended Kalman filter in the absence of the uncertainty. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： multiple-model adaptive estimation uncertain systems optimal filtering space surveillance

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An improved strong tracking multiple-model adaptive estimation: a fast diagnosis algorithm for aircraft actuator fault

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2016年第7期38卷 846-854页

作者： Ma, Jun Ni, Shi-hong Xie, Wu-jie Dong, Wen-han Air Force Engn Univ Aeronaut & Astronaut Engn Inst BaLin Rd 1 Xian 710038 Peoples R China

The multiple-model adaptive estimation method has low capability to track abrupt faults;therefore, multiple fading factors may result in diverging the Strong Tracking Filter. Moreover, the fault probability calculation is large. In this paper, an improved Strong Tracking multiple-model adaptive estimation fast diagnosis algorithm is proposed. The tracking performance of the filter was improved by multiple fading factors. An improved renewal equation of the step prediction covariance matrix is proposed. The stability of the filter was guaranteed, and the estimation accuracy was improved. Based on the Euclidean norm, a fast fault isolation method that reduces the fault probability calculation is proposed. The simulation results show that this algorithm is more efficient and has a better performance.

关键词： Strong Tracking Filter multiple-model adaptive estimation Fault Detection and Isolation Euclidean norm

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An Augmented multiple-model adaptive estimation for Time-Varying Uncertain Systems

An Augmented Multiple-Model Adaptive Estimation for Time-Var...

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Chinese Intelligent Systems Conference (CISC)

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

ISBN: (纸本)9789811023385;9789811023378

An augmented multiple-model adaptive estimation (MMAE) algorithm is presented for a time-varying system, where the model uncertainty may occur occasionally. Generally, it is difficult for a single filter to achieve superior performance for both the certain system and the uncertain system. An algorithm that is designed for an uncertain system may yield suboptimal performance in the situation, where the model uncertainty does not occur. To cope with this problem, we propose to use two filters in parallel in a multiple-model framework. One of the filters, an augmented Kalman filter (AKF), provides estimates of uncertain parameters when the model uncertainties occur, whereas the second filter, a Kalman filter (KF), yields high precision in the absence of the uncertainties. A practical example is given in simulation to show the potential application of the presented algorithm. It indicates that the augmented MMAE is efficient to deal with the occasional model uncertainty.

关键词： multiple-model adaptive estimation Uncertain system Augmented Kalman filter Space surveillance

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Improper Complex-Valued multiple-model adaptive estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第6期63卷 1528-1542页

作者： Mohammadi, Arash Plataniotis, Konstantinos N. Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

Motivated by the problem of estimating the discrete and continuous states of an improper complex-valued stochastic hybrid system, the paper proposes a class of widely linear (augmented) multiple model adaptive estimation algorithms, referred to as the C/MMAE. We show that for an improper complex-valued signal, pseudo-covariance of the innovation sequence is not zero and, therefore, carries useful statistical information regarding the unknown behaviour mode of the hybrid system. A new Bayesian law is, therefore, derived as a function of the pseudo-covariance of the innovation sequence and used to compute the probability that a hypothesized model is in effect at a certain time. We show that the C/MMAE, which uses the new Bayesian law and utilizes the complete second-order statistical characterization of the complex-valued innovation sequence, convergences faster than its counterpart, which only uses the conventional covariance of the innovation sequence. In order to simplify the computational complexity, we develop two circularized versions of the C/MMAE using a preprocessing step, referred to as the circularizing filter (CF). The CF is incorporated to convert the improper observations/innovations into the proper ones in order to reduce the computational complexity of the hypothesis testing step. Finally, an interacting version of the C/MMAE, referred to as C/IMM, is developed for improper complex-valued systems with Markovian switching coefficients. Simulation results indicate that the proposed hybrid estimators provide improved performance and convergence properties over their traditional counterparts.

关键词： Augmented complex Kalman filter improper signals interactive multiple-model multiple-model adaptive estimation widely-linear estimation

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Distributed Widely Linear multiple-model adaptive estimation

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2015年第3期1卷 164-179页

作者： Mohammadi, Arash Plataniotis, Konstantinos N. Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Univ Toronto Dept Elect & Comp Engn Biomed signal Proc Toronto ON Canada

The paper considers the problem of estimating the state of a complex-valued stochastic hybrid system observed distributively using an agent/sensor network (AN/SN) with complexvalued (possibly noncircular) observations. In several distributed estimation problems, a suitable model to describe the underlying system is unknown a priori, i.e., distributed state estimation with structural uncertainty. Motivated by application of widely linear processing techniques in such problems, the paper proposes a class of distributed multiple-model adaptive estimation algorithms, referred to as the CD/MMAE. By incorporating the particular structure of the complex-valued observations on the second moment, first we develop two hierarchical CD/MMAE implementations and then use them as the building blocks and develop a diffusion-based hybrid estimator for decentralized estimation without incorporation of a fusion centre. The paper derives a new form of the adaptation law and a new form of information fusion, which takes advantage of the full second-order statistical properties of the underlying observations. Convergence properties of the proposed diffusion-based CD/MMAE are then investigated. We show that the adaptive weight of all local nodes converges to the true mode with probability one. Simulation results indicate that the proposed hybrid estimators provide improved performance and convergence properties over their traditional counterparts.

关键词： Augmented complex Kalman filter Improper signals Interactive multiple-model multiple-model adaptive estimation Widely-linear estimation

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A model Error Formulation of the multiple model adaptive estimation Algorithm 17

A Model Error Formulation of the Multiple Model Adaptive Est...

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17th International Conference on Information Fusion (FUSION)

作者： Nebelecky, Christopher K. Crassidis, John L. Singla, Puneet CUBRC Inc Informat Fus Grp Buffalo NY 14225 USA SUNY Buffalo Dept Mech & Aero Eng Amherst NY 14260 USA

ISBN: (纸本)9781479916344

This paper presents a new form of the multiple model adaptive estimation algorithm for improved state tracking in systems with unknown system models. The proposed approach differs from existing multiple model methods in the manner in which the covariance and Kalman gains of the individual filters are calculated. By using the fused model estimate, recursions for the actual estimation error covariances are derived which account for the deviation of the hypothesized model from the fused model. Using these covariances to determine the Kalman gain leads to improved tracking estimates through fusion of model and measurement uncertainty. The proposed algorithm has been compared against the standard multiple model adaptive estimation and interacting multiple model algorithms in two simulated examples, resulting in improved, and comparable tracking performance, respectively.

关键词： multiple-model adaptive estimation filtering target tracking

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Observer Kalman filter identification and multiple-model adaptive estimation technique for classifying animal behaviour using wireless sensor networks

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2009年第1期68卷 9-17页

作者： Nadimi, E. S. Sogaard, H. T. Univ So Denmark Fac Engn DK-5230 Odense M Denmark Aalborg Univ Dept Elect Syst Automat & Control Aalborg Denmark Engn Coll Aarhus Aarhus Denmark

The identification of a mathematical model capable of describing the behaviour of animals given input such as feed has great potential for behavioural control purposes. Such models will allow to make predictions which are fundamental to any closed loop control such as control of the feeding. This paper investigates the problem of mathematically modelling animal behaviour. An observer Kalman filter identification method was successfully applied to input-output data and two models representing the hypotheses that animals are actively feeding and the hypotheses that animals are inactive were identified. The input and output of each of the identified models were feed dry matter offer and the pitch angle of the neck, respectively. The pitch angle of the neck of the animal was successfully measured and aggregated by a ZigBee-based wireless sensor network. Two fourth-order models describing the dynamics of an animal in the active and inactive behaviour modes showed good performance in terms of prediction error, cross-correlation between the residual and the output as well as cross-correlation between the residual and the input with 99% confidence interval. A multiple-model adaptive estimation approach was applied to determine the likelihood of each of the two models being the correct model for a specific input of dry matter feed. The average classification success rate was 87.2% for the whole experiment. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Observer Kalman filter multiple-model adaptive estimation Animal behaviour Wireless sensor networks

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Recursive Actuator Fault Detection and Diagnosis for Emergency Landing of UASs

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IFAC Proceedings Volumes 2014年第3期47卷 2495-2502页

作者： X. Yang L. Mejias M. Warren F. Gonzalez B. Upcroft Australian Research Center for Aerospace Automation (ARCAA) and Queensland University of Technology (QUT) Brisbane Australia School of EECS QUT Brisbane Australia

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.

关键词： Fault diagnosis and isolation unscented Kalman filter multiple-model adaptive estimation emergency landing unmanned aerial system

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Single Pan and Tilt Camera Indoor Positioning and Tracking System

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EUROPEAN JOURNAL OF CONTROL 2011年第4期17卷 414-428页

作者： Gaspar, Tiago Oliveira, Paulo Inst Super Tecn ISR IST Inst Syst & Robot Lisbon Portugal

An inexpensive single pan and tilt camera based indoor positioning and tracking system is proposed, supported on a functional architecture where three main modules can be identified: one related to the interface with the camera, tackled with parameter estimation techniques;other, responsible for isolating and identifying the target, based on advanced image processing techniques, and a third, that resorting to nonlinear dynamic system suboptimal state estimation techniques, performs the tracking of the target and estimates its position, and linear and angular velocities. The contributions of this work are fourfold: i) a new indoor positioning and tracking system architecture;ii) a new lens distortion calibration method, that preserves generic straight lines in images;iii) suboptimal nonlinear multiple-model adaptive estimation techniques, for the adopted target model, to tackle the positioning and tracking tasks, and iv) the implementation and validation in real time of a complex tracking system, based on a low cost single camera. To assess the performance of the proposed system, a series of indoor experimental tests for a range of operation of up to ten meter were carried out. A centimetric accuracy was obtained under realistic conditions.

关键词： Indoor positioning and tracking nonlinear filtering multiple-model adaptive estimation single camera vision systems

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