Set-based Multi-Sensor Data Fusion For Integrated Navigation Systems

Author(s):  
Sara Ifqir ◽  
Christophe Combastel ◽  
Ali Zolghadri
Author(s):  
Wenwen Liu ◽  
Yuanchang Liu ◽  
Bryan Adam Gunawan ◽  
Richard Bucknall

Abstract As autonomous ships become the future trend for maritime transportation, it is of importance to develop intelligent autonomous navigation systems to ensure the navigation safety of ships. Among the three core components (sensing, planning and control modules) of the system, an accurate detection of target ships’ navigation information is critical. Within a typical maritime environment, the existence of sensor noises as well as the influences generated by varying environment conditions largely limit the reliability of using a single sensor for environment awareness. It is therefore vital to use multiple sensors together with a multi-sensor data fusion technology to improve the detection performance. In this paper, a fuzzy logic-based multi-sensor data fusion algorithm for moving target ships detection has been proposed and designed using both AIS and radar information. A two-stage fuzzy logic association method has been particularly developed and integrated with Kalman filtering to achieve a computationally efficient performance. The effectiveness of the proposed algorithm has been tested and validated in simulations where multiple target ships are transiting with complex movements.


2015 ◽  
Vol 8 (1) ◽  
pp. 93-102 ◽  
Author(s):  
Jie Shen ◽  
Zhen Zhang ◽  
Hongyang Bai ◽  
Shengchun Liu ◽  
Hui Gu ◽  
...  

2021 ◽  
Author(s):  
Sara Ifqir ◽  
Christophe Combastel ◽  
Ali Zolghadri ◽  
Guillaume Alcalay ◽  
Philippe Goupil ◽  
...  

2013 ◽  
Vol 739 ◽  
pp. 580-585
Author(s):  
He Nian Wang ◽  
Guo Xing Yi ◽  
Chang Hong Wang ◽  
Yang Guang Xie

In research of SINS/GPS/SS integrated navigation system and multi sensor data fusion method, we proposed an adaptive federated Kalman filtering method based on convex optimization. The method uses real-time OLS-SVM obtained information distribution factor, so that the information distribution factor can change with the local filter performance. So that can make timely response to the performance and failure of local sensors and filter, which affects the whole system accuracy. The simulation results show that, the method has stronger adaptability of model and noise interference, can effectively restrain the divergence and improves the system precision and real time.


Author(s):  
Geoffrey Ho ◽  
Erin Kim ◽  
Shahzaib Khattak ◽  
Stephanie Penta ◽  
Tharmarasa Ratnasingham ◽  
...  

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