Interacting Multiple Model Algorithms for Manoeuvring Ship Tracking Based On New Ship Models

Semerdjiev, Emil; Mihaylova, L.; Semerdjiev, Tzvetan; Bogdanova, Violeta

doi:10.11610/isij.0211

Cited by 16 publications

(15 citation statements)

References 8 publications

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“…In each modeled state the force has a different effect, since the ship is likely to actuate a given motion (still anchored, navigation, approaching, etc.). In [84] the state vector of the considered model is extended to include the ship state, heading, rate of turn, drift angle and velocity; etc. Among the alternatives we can mention fixed/variable structure augmented IMM Algorithm for Ship Tracking, and hybrid algorithms doing simultaneous parameter and state estimation.…”

Section: Context In Filteringmentioning

confidence: 99%

Context-based Information Fusion: A survey and discussion

2015

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This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of ''context''. It shows how its fortune in the distributed computing world eventually per-meated in the world of IF, discussing the current strategies and techniques, and hinting possible future trends. IF processes can represent context at different levels (structural and physical constraints of the scenario, a priori known operational rules between entities and environment, dynamic relationships modelled to interpret the system output, etc.). In addition to the survey, several novel context exploita-tion dynamics and architectural aspects peculiar to the fusion domain are presented and discussed.

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Section: Context In Filteringmentioning

confidence: 99%

Context-based Information Fusion: A survey and discussion

2015

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“…, may allow the right interpretation of sensor data and the adaptation/optimization in a great deal of fusion system performance. Examples of sources of knowledge, additional to sensor data, are: constraints in motion depending on local topography [35,36], trafficability constraints [37], expected maneuvers motion patterns depending on situation [38], a priori known relationships between entities and environment [39,40], etc. …”

Section: Related Workmentioning

confidence: 99%

Context-Aided Sensor Fusion for Enhanced Urban Navigation

Martí

Martín

García

et al. 2012

Sensors

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The deployment of Intelligent Vehicles in urban environments requires reliable estimation of positioning for urban navigation. The inherent complexity of this kind of environments fosters the development of novel systems which should provide reliable and precise solutions to the vehicle. This article details an advanced GNSS/IMU fusion system based on a context-aided Unscented Kalman filter for navigation in urban conditions. The constrained non-linear filter is here conditioned by a contextual knowledge module which reasons about sensor quality and driving context in order to adapt it to the situation, while at the same time it carries out a continuous estimation and correction of INS drift errors. An exhaustive analysis has been carried out with available data in order to characterize the behavior of available sensors and take it into account in the developed solution. The performance is then analyzed with an extensive dataset containing representative situations. The proposed solution suits the use of fusion algorithms for deploying Intelligent Transport Systems in urban environments.

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“…We use either term if they coincide or one does not exist (strictly speaking, a point target without a shape has no heading). Turn rate is usually defined as the heading change rate.¯= 0 is given by [88,89,66] x k+1 = x k + Tv k sin Á k (94) y k+1 = y k + Tv k cos Á k (95) Á k+1 = Á k + Tv k [ k + 1 2 ( k 0 )Tv k ¿e Tv k ¿ ] (96) k+1 = k e Tv k ¿ + 0 (1 e Tv k ¿ ) (97) v k = Kv 0 = v 0 (1 + 1:9 2 k L 2 ) 1 (98) where ¿ = ( p=2 + p 2 =4 q)=L and 0 = =v 0 . The time constant ¿ was set to zero in [88,90], resulting in a constant turn rate (i.e., k+1 = k ), to eliminate the dependence of the model on the ship-specific hydrodynamic constants.…”

Section: E Ship Modelsmentioning

confidence: 99%

“…The unknown k was assumed to take on one of the three possible values 0, c , c with a preset constant c , representing rectilinear, left-turn, and right-turn motions, respectively, and the tracker presented therein was based on a multiple-model algorithm using these three models for k . The above nonlinear model has been proposed for ship tracking [88,89,66]. Other ship dynamic models are available (see, e.g., [91,33,92]), some of which appear simpler and more popular.…”

Section: E Ship Modelsmentioning

confidence: 99%

Survey of maneuvering targettracking . part I: dynamic models

Jilkov

2003

IEEE Trans. Aerosp. Electron. Syst.

1,801

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This is the first part of a comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. It surveys various mathematical models of target motion/dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among models and insight to the pros and cons of models are provided. Some material presented here has not appeared elsewhere. CONTENTS

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Interacting Multiple Model Algorithms for Manoeuvring Ship Tracking Based On New Ship Models

Cited by 16 publications

References 8 publications

Context-based Information Fusion: A survey and discussion

Context-based Information Fusion: A survey and discussion

Context-Aided Sensor Fusion for Enhanced Urban Navigation

Survey of maneuvering targettracking . part I: dynamic models

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