<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.</p></abstract>

Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.

Error Mitigation Algorithm Based on Bidirectional Fitting Method for Collision Avoidance of Unmanned Surface Vehicle

Polish Maritime Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years...

Error Mitigation Algorithm Based on Bidirectional Fitting Method for Collision Avoidance of Unmanned Surface Vehicle

Publié en ligne: 18 janv. 2019

Pages: 13 - 20

DOI: https://doi.org/10.2478/pomr-2018-0127

Mots clés
Unmanned Surface Vehicle, Position prediction, Error mitigation, Autoregressive model, Particle Swarm Optimization

© 2019 Lifei Song, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Error Mitigation Algorithm Based on Bidirectional Fitting Method for Collision Avoidance of Unmanned Surface Vehicle

Publié en ligne: 18 janv. 2019

Pages: 13 - 20

DOI: https://doi.org/10.2478/pomr-2018-0127

Mots clésUnmanned Surface Vehicle, Position prediction, Error mitigation, Autoregressive model, Particle Swarm Optimization

© 2019 Lifei Song, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Unmanned Surface Vehicle, Position prediction, Error mitigation, Autoregressive model, Particle Swarm Optimization