scholarly journals A Single-Way Ranging Localization of AUVs Based on PSO of Outliers Elimination

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xinnan Fan ◽  
Zhongjian Wu ◽  
Jianjun Ni ◽  
Chengming Luo
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Time Of Arrival ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Simulation Experiments ◽  
Range Measurements ◽  
Acoustic Localization ◽  
Long Baseline ◽  
Key Factor

Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In long baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the precision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm.

Observability Analysis for MAUVs Cooperative Navigation System Based on Moving Long Baseline

2013 ◽  
Vol 475-476 ◽  
pp. 609-615
Author(s):  
Peng Ma ◽  
Fu Bin Zhang ◽  
De Min Xu ◽  
Shao Kun Yang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Simulation Experiments ◽  
Cooperative Navigation ◽  
Long Baseline ◽  
Simulation Results ◽  
Moving Long Baseline ◽  
Theoretical Results

This paper addresses the observability problem of 2D Multiple Autonomous Underwater Vehicles (MAUVs) cooperative navigation system. We derive the conditions to keep the local weak observability of navigation system using the Lie derivatives, and characterize the unobservable trajectories of AUVs. We design a series of simulation experiments using the Extended Kalman Filter (EKF) to verify the theoretical results. Finally, the simulation results show that the good performance of navigation system can be presented if avoiding the unobservable trajectories of AUVs.

Cooperative Navigation for Multiple AUVs Based on Relative Range Measurements with a Single Leader

2014 ◽  
Vol 556-562 ◽  
pp. 3117-3123 ◽  
Author(s):  
Xing Li Huang ◽  
Li Yan Liu ◽  
Tao Tao Lv ◽  
Wen Bai Li
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Cooperative Localization ◽  
Positioning System ◽  
Navigation Problem ◽  
Range Measurements ◽  
Cooperative Navigation ◽  
Long Baseline ◽  
Novel Method ◽  
Simulation Results

his paper deals with the cooperative navigation problem of multiple autonomous underwater vehicles (AUV). A novel method which does not depend on a beacon network like in long baseline positioning system is proposed. The principle of this approach is to realize the cooperative localization of AUVs by using relative range measurements between the leader and the follower vehicles by means of an extended Kalman filter. Simulation results that validate the effectiveness of this approach are presented.

scholarly journals Probability Weighting Localization Algorithm Based on NLOS Identification in Wireless Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Shixun Wu ◽  
Shengjun Zhang ◽  
Kai Xu ◽  
Darong Huang
Keyword(s):  
Base Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Probability Weighting ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Chi Square ◽  
Home Base ◽  
Angle Measurements ◽  
Non Line Of Sight

In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.

scholarly journals Tracking Control of Autonomous Underwater Vehicles with Acoustic Localization and Extended Kalman Filter

2021 ◽  
Vol 11 (17) ◽  
pp. 8038
Author(s):  
Dongzhou Zhan ◽  
Huarong Zheng ◽  
Wen Xu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking Control ◽  
Autonomous Underwater Vehicles ◽  
Acoustic Signals ◽  
Underwater Vehicles ◽  
Ocean Currents ◽  
Acoustic Localization ◽  
And Control ◽  
Constraints Model

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.

scholarly journals Using Autonomous Underwater Vehicles for Diver Tracking and Navigation Aiding

2020 ◽  
Vol 8 (6) ◽  
pp. 413
Author(s):  
Đula Nađ ◽  
Filip Mandić ◽  
Nikola Mišković
Keyword(s):  
Life Support ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Technical Equipment ◽  
Short Baseline ◽  
Acoustic Localization ◽  
Multiple Viewpoints ◽  
Point Of Interest ◽  
Underwater Navigation ◽  
The Eu

SCUBA diving activities are classified as high-risk due to the dangerous environment, dependency on technical equipment that ensures life support, reduced underwater navigation and communication capabilities all of which compromise diver safety. While autonomous underwater vehicles (AUVs) have become irreplaceable tools for seabed exploration, monitoring, and mapping in various applications, they still lack the higher cognitive capabilities offered by a human diver. The research presented in this paper was carried out under the EU FP7 “CADDY—Cognitive Autonomous Diving Buddy”. It aims to take advantage of both human diver and AUV complementary traits by making their synergy a potential solution for mitigation of state of the art diving challenges. The AUV increases diver safety by constantly observing the diver, provides navigation aiding by directing the diver and offers assistance (e.g., lights, tool fetching, etc.). The control algorithms proposed in the paper provide a foundation for implementing these services. These algorithms use measurements from stereo-camera, sonar and ultra-short baseline acoustic localization to ensure the vehicle constantly follows and observes the diver. Additionally, the vehicle maintains a relative formation with the diver to allow observation from multiple viewpoints and to aid underwater navigation by pointing towards the next point of interest. Performance of the proposed algorithms is evaluated using results from pool experiments.

Sign in / Sign up

Export Citation Format

Share Document