Factor graph and fisher information matrix-assisted indoor cooperative positioning algorithm for wireless sensor networks

2021 ◽  
pp. 107601
Author(s):  
Fahad Ghalib Abdulkadhim ◽  
Yi zhang ◽  
Ahmed Alkhayyat ◽  
Mudassar Khalid ◽  
Chengkai Tang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fisher Information ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Wireless Sensor ◽  
Factor Graph ◽  
Cooperative Positioning ◽  
Positioning Algorithm

Analysis of Flying Vehicle Cooperative Positioning by Sea-Surface Wireless Sensor Networks

2012 ◽  
Vol 505 ◽  
pp. 338-344
Author(s):  
Wei Ming Xu ◽  
Xiao Dong Yin ◽  
Geng Feng Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Synchronization ◽  
Sea Surface ◽  
Wireless Sensor ◽  
Relative Positioning ◽  
Cooperative Positioning ◽  
Positioning Method ◽  
Absolute Bias ◽  
Positioning Algorithm

Sea-surface wireless sensor networks (S2WSN) is a combination of many nodes forming a certain geometric shape, such as ships and sea-surface radio buoys. To satisfy the requirement of precise tracking for flying vehicle (FV) in times of exterior location datum outages, a cooperative positioning algorithm (CPA) for the FV is proposed. Time synchronization among the nodes is crucial to guarantee CPA. Taking a single-hop S2WSN as an example, the problem of low synchronization precision is resolved by two-way timing with unequal reply time (TWT-UTD). Monte Carlo simulation results show that, through optimizing the position dilution of precision among the sea-surface nodes and the FV, the absolute bias of the FV tracking by the proposed CPA is superior to that of the conventional single ship-based relative positioning method. Meanwhile, the synchronization precision is increased by more than 20% via TWT-UTD method.

scholarly journals A Combined Localization Algorithm for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaogang Qi ◽  
Xiaoke Liu ◽  
Lifang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Vehicle Tracking ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Environment And Health ◽  
Anchor Nodes ◽  
Positioning Algorithm

Wireless sensor networks (WSNs) are widely used in various fields to monitor and track various targets by gathering information, such as vehicle tracking and environment and health monitoring. The information gathered by the sensor nodes becomes meaningful only if it is known where it was collected from. Considering that multilateral algorithm and MDS algorithm can locate the position of each node, we proposed a localization algorithm combining the merits of these two approaches, which is called MA-MDS, to reduce the accumulation of errors in the process of multilateral positioning algorithm and improve the nodes’ positioning accuracy in WSNs. It works in more robust fashion for noise sparse networks, even with less number of anchor nodes. In the MDS positioning phase of this algorithm, the Prussian Analysis algorithm is used to obtain more accurate coordinate transformation. Through extensive simulations and the repeatable experiments under diverse representative networks, it can be confirmed that the proposed algorithm is more accurate and more efficient than the state-of-the-art algorithms.

A Decentralized Positioning Method for Wireless Sensor Networks

2014 ◽  
Vol 668-669 ◽  
pp. 1194-1197 ◽  
Author(s):  
Yan Feng ◽  
Bo Yi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Research Direction ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Position Information ◽  
Initial Location ◽  
3D Positioning ◽  
Positioning Algorithm

The three-dimensional positioning algorithm has become a hot research direction in wireless sensor networks localization algorithms, however the existing 3D positioning algorithms have general shortcomings, such as high complexity, low positioning accuracy, great energy consumption. Aiming at the existing problems of 3D localization algorithm, we propose an decentralized 3D positioning algorithm based on RSSI ranging and free ranging mechanism. The algorithm firstly use measured RSSI to establish beacon node neighborhood. Then the method adopts regional division to obtain initial location information for unknown nodes. Finally, the method use the iterative optimization process to achieve a position information updates. Simulation results demonstrate that proposed algorithm is feasible and has better localization accuracy.

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