Fast and Precise Neural Network-Based Environment Detection utilizing UWB CSI for Seamless Localization Applications

Abstract

Seamless localization, navigation, and tracking applications can be realized utilizing different sensors and cameras, radio frequency signals such as WiFi, ultra-wideband, and global navigation satellite system, each of which is better suited for different types of environments. As such, awareness of the environment is crucial for the system to efficiently utilize the most relevant resources in each scenario and enable seamless transition between different environments. For example, when vehicles are moving from an open area such as open highway to crowded urban streets, or the opposite, they experience a considerable environment transition, which triggers opportunities for wide-range environment-specific device and algorithm optimization. In this paper, a novel infrastructure-free method utilizing channel state information of ultra-wideband signals and a convolutional neural network is proposed. This method enables a fast detection of the environment type, including crowded urban and open outdoor, reaching a detection latency of only three milliseconds. The experimental data is collected in the real environments of the city of Ghent, Belgium. The test data set, used for numerical performance evaluations, is collected from areas different from those used in the training set. The results show that the proposed method provides an average environment detection accuracy of 90% in the considered test setup.