scholarly journals Automatic tracking and positioning algorithm for moving targets in complex environment

2019 ◽  
Vol 12 (4-5) ◽  
pp. 1251-1264
Author(s):  
Rong Liu ◽  
◽  
Saini Jonathan Tishari ◽  
Keyword(s):  
Moving Targets ◽  
Complex Environment ◽  
Automatic Tracking ◽  
Positioning Algorithm

The Weighted Multilateral Positioning Algorithm Based on Time Reversal Ranging

2014 ◽  
Vol 644-650 ◽  
pp. 1464-1469
Author(s):  
Zheng Zhang ◽  
Xing Peng Tao ◽  
Lun Zeng ◽  
Chan Wang
Keyword(s):  
Time Reversal ◽  
Wireless Sensor ◽  
Positioning Error ◽  
Complex Environments ◽  
Complex Environment ◽  
General Applicability ◽  
Positioning Accuracy ◽  
Key Technology ◽  
Ranging Error ◽  
Positioning Algorithm

Indoor node positioning is a key technology in wireless sensor network but the general indoor nodes positioning algorithm is difficult to meet the precision positioning requirements due to the indoor complex environment such as multilateral positioning algorithm based on RSSI ranging. The weighted multilateral positioning algorithm is proposed based on time reversal ranging to solve the problem. We simulate within 50m*50m area, the experimental results show that the ranging error is less than 1%. The maximum positioning error is less than 0.6m. Compared with general positioning algorithm, it can improve the positioning accuracy greatly in complex environments and has general applicability.

scholarly journals Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bin Wu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Success Rate ◽  
Moving Targets ◽  
Sampling Area ◽  
Positioning Accuracy ◽  
Sample Quality ◽  
The Monte Carlo Method ◽  
Beacon Node ◽  
Positioning Algorithm

In order to improve the positioning accuracy of underground targets, especially the positioning accuracy of moving targets, an improved weighted Monte Carlo positioning algorithm is proposed. In the sampling initialization stage, the beacon node gradually constructs the sampling area according to the RSSI size and combines the Monte Carlo method to further narrow the range and improve the sampling success rate. In the filtering stage, refer to the sampling area at time and further improve the sample quality at t − 1 after two filterings. In the recollection stage, cooperate with invalid sample sets to reduce the number of recollections and weigh the final samples to improve the positioning accuracy of the nodes to be tested.

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