This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor environments. The neural network can differentiate more targets, and achieves high differentiation and localization accuracy, improving on previously reported methods. It achieves this by exploiting the identifying features in the differential amplitude and time-of-flight characteristics of these targets. An important observation follows from the robustness tests, which indicate that the amplitude information is more crucial than time-of-flight for reliable operation. The study suggests wider use of neural networks and amplitude information in sonar-based mobile robotics.

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.

In this study, physical models are used to model reflections from target primitives commonly encountered in a mobile robot's environment. These targets are differentiated by employing a multi-transducer pulse/echo system which relies on both amplitude and time-of-flight data, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief which are subsequently fused by employing multiple logical sonars at different geographical sites. Feature data from multiple logical sensors are fused with the Dempster-Shafer rule of combination to improve the performance of classification by reducing perception uncertainty. Dempster-Shafer fusion results are contrasted with the results of combination of sensor beliefs through a simple majority vote. The method is verified by experiments with a real sonar system. The evidential approach employed here helps to overcome the vulnerability of the echo amplitude to noise and enables the modeling of non-parametric uncertainty in real time.