A Robust Navigation Technique for Integration in the Guidance and Control of an Uninhabited Surface Vehicle

Motwani, Amit; Sharma, Sanjay; Sutton, Robert I.; Culverhouse, Phil F.; Yang, Chengxiang

doi:10.1017/s0373463315000065

Cited by 12 publications

(12 citation statements)

References 29 publications

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“…In this process, minimum variance estimation of state which is the optimal estimation of state can be obtained, but robustness of filter would get worse. With continuous decrease, the ∞ filter is less sensitive to the change of error of system model, error of initial state, and statistical property of noises, but robustness of filter would gradually increase, variance of estimated state would become big, and the precision of estimation would gradually reduce [15,16].…”

Section: The Algorithm Of ∞ Suboptimal Filtermentioning

confidence: 99%

Application ofH∞Filter on the Angular Rate Matching in the Transfer Alignment

Song¹,

Duan²,

Sun³

2016

Discrete Dynamics in Nature and Society

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The transfer alignment (TA) scheme is used for the initial alignment of Inertial Navigation System (INS) on dynamical base. The Kalman filter is often used in TA to improve the precision of TA. And the statistical characteristics of interference signal which is difficult to get must be known before the Kalman filter is used in the TA, because the interference signal is a random signal and there are some changes on the dynamic model of system. In this paper, theH∞filter is adopted in the TA scheme of the angular rate matching when the various stages of disturbance in measurement are unknown. And it is compared with the Kalman filter in the same environment of simulation and evaluation. The result of simulation shows that theH∞filter and the Kalman filter are both effective. The Kalman filter is more accurate than theH∞filter when system noise and measurement noise are white noise, but theH∞filter is more accurate and quicker than the Kalman filter when system noise and measurement noise are color noise. In the engineering practice, system noise and measurement noise are always color noise, so theH∞filter is more suitable for engineering practice than the Kalman filter.

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Section: The Algorithm Of ∞ Suboptimal Filtermentioning

confidence: 99%

Application ofH∞Filter on the Angular Rate Matching in the Transfer Alignment

Song¹,

Duan²,

Sun³

2016

Discrete Dynamics in Nature and Society

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“…The vehicle's guidance system, in turn, generates and updates the reference path of the vehicle via a series of waypoints. A detailed description of the waypoint tracking and autopilot systems used for the autonomous operation of Springer can be found in Annamalai et al (2014).…”

Section: Autonomous Surface Vehicle Navigationmentioning

confidence: 99%

Subsea cable tracking by an unmanned surface vehicle

Szyrowski¹,

Motwani²,

Sharma³

et al. 2015

uw tech: int j soc uw tech

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Subsea cable localisation is a demanding task that requires a lot of time, effort and expense. In the present paper the authors propose a methodology that is automated and inexpensive, based on magnetic detection from a small unmanned surface vehicle (USV) and the use of a batch particle filter (BPF) algorithm. A dynamic path planning algorithm for the USV is also developed so that adequate samples of the magnetic field readings can be gathered for processing by the BPF. All of these elements work together online as the cable is tracked, which was demonstrated in a simulated mission.

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“…;ŵ = a (3) = Θ (2) a (2) ð26Þ where a (l) are the outputs of the nodes of layer (l), and Θ (1) 2 R 5 3 13 and Θ (2) 2 R 1 3 6 are the matrices of parameters of the network such that Θ (l) ij represents the strength of the connection between node a (l) j and a (l + 1)…”

Section: Forward Propagationmentioning

confidence: 99%

“…In a recent study by Annamalai et al, 1 a system for determining the heading of an uninhabited surface vehicle (USV) based on an interval Kalman filter (IKF) was explored. The IKF, designed upon imprecise knowledge of the vehicle's yaw dynamics, provided upper and lower bounds to the statistically optimal estimate of its heading angle at each time instant.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks to weighted interval Kalman filtering

Motwani

Sharma

Sutton

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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The interval Kalman filter is a variant of the traditional Kalman filter for systems with bounded parametric uncertainty. For such systems, modelled in terms of intervals, the interval Kalman filter provides estimates of the system state also in the form of intervals, guaranteed to contain the Kalman filter estimates of all point-valued systems contained in the interval model. However, for practical purposes, a single, point-valued estimate of the system state is often required. This point value can be seen as a weighted average of the interval bounds provided by the interval Kalman filter. This article proposes a methodology based on the application of artificial neural networks by which an adequate weight can be computed at each time step, whereby the weighted average of the interval bounds approximates the optimal estimate or estimate which would be obtained using a Kalman filter if no parametric uncertainty was present in the system model, even when this is not the case. The practical applicability and robustness of the method are demonstrated through its application to the navigation of an uninhabited surface vehicle.

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A Robust Navigation Technique for Integration in the Guidance and Control of an Uninhabited Surface Vehicle

Cited by 12 publications

References 29 publications

Application ofH∞Filter on the Angular Rate Matching in the Transfer Alignment

Application ofH∞Filter on the Angular Rate Matching in the Transfer Alignment

Subsea cable tracking by an unmanned surface vehicle

Application of artificial neural networks to weighted interval Kalman filtering

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