Observer Kalman filter identification of an autonomous underwater vehicle

Tiano, A.; Sutton, Robert I.; Lozowicki, Adam; Naeem, Wasif

doi:10.1016/j.conengprac.2006.08.004

Cited by 61 publications

(8 citation statements)

References 7 publications

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“…Among the various non-linear filtering techniques, the EKF fusion algorithm is the simplest algorithm that locally linearizes the Kalman filter fusion algorithm and is suitable for weakly nonlinear [27,28], non-Gaussian white noise environments. The EKF fusion algorithm performs a first-order Taylor expansion of the nonlinear function of the system and obtains a linearized system formula to complete the filtering estimation of the target [29].…”

Section: Positioning System Designmentioning

confidence: 99%

Design of a Low-Cost Indoor Navigation System for Food Delivery Robot Based on Multi-Sensor Information Fusion

Sun

Guan

Chang

et al. 2019

Sensors

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As the restaurant industry is facing labor shortage issues, the use of meal delivery robots instead of waiters/waitresses not only allows the customers to experience the impact of robot technology but also benefits the restaurant business financially by reducing labor costs. Most existing meal delivery robots employ magnetic navigation technologies, which require magnetic strip installation and changes to the restaurant decor. Once the moving path is changed, the magnetic strips need to be re-laid. This study proposes multisource information fusion, i.e., the fusion of ultra-wide band positioning technology with an odometer and a low-cost gyroscope accelerometer, to achieve the positioning of a non-rail meal delivery robot with navigation. By using a low-cost electronic compass and gyroscope accelerometer, the delivery robot can move along a fixed orbit in a flexible and cost-effective manner with steering control. Ultra-wide band (UWB) and track estimation algorithm are combined by extended Kalman filter (EKF), and the positioning error after fusion is about 15 cm, which is accepted by restaurants. In summary, the proposed approach has some potential for commercial applications.

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Section: Positioning System Designmentioning

confidence: 99%

Design of a Low-Cost Indoor Navigation System for Food Delivery Robot Based on Multi-Sensor Information Fusion

Sun

Guan

Chang

et al. 2019

Sensors

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“…The classical methods include least square method (LS) [1,2], extended Kalman filter (EKF) [3,4], model reference method (MR), recursive prediction error method (RPE) [5,6], maximum likelihood method (ML) [7] etc. as well as their improved ones [8]. To overcome the inherent defects of the classical SI methods, i.e., dependency on initial values, ill-conditioned solutions and simultaneous drift, frequency domain identification method [9][10][11], neural network (NN) and support vector machines (SVM) are proposed for parametric identification.…”

Section: Introductionmentioning

confidence: 99%

Parametric estimation of ship maneuvering motion with integral sample structure for identification

Cao

Zhuang

et al. 2015

Applied Ocean Research

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“…A robust Kalman filter-based subspace tracking algorithm in an impulsive noise environmen has been given in [15]. It evaluated the applicability of a Kalman filter to the identification of an autonomous underwater vehicle [17]. The control of an inverted pendulum has been studied and demonstrated in control laboratories in connection with the control of launching the rocket for decades.…”

mentioning

confidence: 99%

A Novel Delta Operator Kalman Filter Design and Convergence Analysis

Yang

Xia

Shi

et al. 2011

IEEE Trans. Circuits Syst. I

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This paper focuses on the development of a delta operator Kalman filter and its convergence analysis. The delta operator Kalman filter is designed to estimate the state vectors of a delta operator system. Note that the designed delta operator Kalman filter can express both continuous-time and discrete-time cases. Then, the convergence analysis of the delta operator Kalman filter is also investigated by using Lyapunov approach in delta domain. Furthermore, this paper gives fundamental results for the analysis and application of the delta operator Kalman filter as a state observer in an inverted pendulum model. Some experimental results of an inverted pendulum on a laboratory-scale setup are presented to illustrate the effectiveness of the designed Kalman filter and its implementation.Index Terms-Convergence analysis, delta operator systems, inverted pendulum model, Kalman filter, Lyapunov approach.

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Observer Kalman filter identification of an autonomous underwater vehicle

Cited by 61 publications

References 7 publications

Design of a Low-Cost Indoor Navigation System for Food Delivery Robot Based on Multi-Sensor Information Fusion

Design of a Low-Cost Indoor Navigation System for Food Delivery Robot Based on Multi-Sensor Information Fusion

Parametric estimation of ship maneuvering motion with integral sample structure for identification

A Novel Delta Operator Kalman Filter Design and Convergence Analysis

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