Table 1.
Example of norm.
Table 2.
Recording of instantaneous events and states.
Fig 1.
Time alignment in FERD system.
The time alignment process of n sensors.
Fig 2.
The Fusion Module include denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision.
Table 3.
Symbol notations.
Fig 3.
The size of this experiment scenario is 15m × 2.4m. Multiple obstacles are also placed within this experiment scenario.
Fig 4.
An unmanned vehicle (RoboMaster).
Table 4.
Norms of fusion moudle.
Fig 5.
In indoor scenario without no obstacle.
Comparison of UWB position and real values in different scenarios.
Fig 6.
In indoor scenario with multiple obstacles.
Comparison of UWB position and real values in different scenarios.
Table 5.
Data analysis of between observed values of UWB position and real values.
Fig 7.
In indoor scenario without no obstacle.
Comparison of observed values and real values of ranging by different sensors in different scenarios.
Fig 8.
In indoor scenario with multiple obstacles.
Comparison of observed values and real values of ranging by different sensors in different scenarios.
Table 6.
Data analysis of ranging in different sensors.
Fig 9.
In indoor scenario without no obstacle.
Comparison of observed values and real values in different scenarios.
Fig 10.
In indoor scenario with multiple obstacles.
Comparison of observed values and real values in different scenarios.
Table 7.
Data analysis of observed values and real values by different sensors in indoor scenario without obstacles.
Table 8.
Data analysis of observed values and real values by different sensors in indoor scenario with multiple obstacles.
Fig 11.
Training variation curves of confidence degree. The different color lines show the change training trend of different sensors’ confidence degrees. The pink line shows the change training trend of UWB positioning. The purple line shows the change training trend of Camera position (IDS ranging). The green line shows the change training trend of Camera position (Camera ranging). The yellow line indicates the change training trend of Camera position (Camera and IDS fusion ranging).
Fig 12.
Training variation curves of error thresholds.
Table 9.
Results of timeliness.
Fig 13.
This figure shows the real trajectory of a1 and a2 are perform a task.
Fig 14.
Positioning trajectory of a1 and a2 by UWB.
The red and blue lines indicate the positioning trajectory of a1 and a2, respectively. The red line is available at https://doi.org/10.5061/dryad.31zcrjdrt (DOI: 10.5061/dryad.31zcrjdrt).
Fig 15.
Comparison between UWB positioning and fusion positioning of a1.
Table 10.
Compare results between single sensor and multi-sensor fusion.