Name
Abstract | ||
|---|---|---|
This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. The neural network can differentiate more targets with higher accuracy, improving on previously reported methods. It achieves this by exploiting the identifying features in the differential amplitude and time-of-flight (TOF) characteristics of these targets. Robustness tests indicate that the amplitude information is more crucial than TOF for reliable operation. The study suggests wider use of neural networks and amplitude information in sonar-based mobile robotics |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/70.864239 | IEEE Transactions on Robotics and Automation |
Keywords | Field | DocType |
target differentiation,ultrasonic transducers.,tof characteristics,sonar sensing,sonar-based mobile robotics,robotics applications,sonar target recognition,mobile robots,neural network-based target differentiation,target localization,target classification,indoor robot environments,amplitude information,time-of-flight characteristics,majority voting,index terms—artificial neural networks,differential amplitude,neural nets,learning,sensor data fusion,evidential reasoning,sonar signal processing,indexing terms,robustness,artificial neural network,neural network,azimuth,time of flight,mobile robot,signal processing,transducers,testing,neural networks | Computer vision,Signal processing,Azimuth,Robustness (computer science),Sonar,Artificial intelligence,Artificial neural network,Sonar signal processing,Robotics,Mobile robot,Mathematics | Journal |
Volume | Issue | ISSN |
16 | 4 | 1042-296X |
Citations | PageRank | References |
18 | 1.13 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Billur Barshan | 1 | 313 | 27.83 |
| birsel ayrulu | 2 | 76 | 6.54 |
| Simukai W. Utete | 3 | 30 | 3.27 |