Relative direction estimation among neighboring vehicles in urban environment is essential to a wide variety of driving safety applications. To obtain accurate direction information solely from vehicle-to-vehicle (V2V) communications is desirable but very challenging due to the highly dynamic vehicular environments. In this paper, we propose an online vehicular AoA estimation scheme, called DeepAoA, based on a deep learning method. More specifically, Channel state information (CSI) is estimated from a set of synchronized receiving radios by a receiver vehicle. By taking the CSI phase difference between a pair of such radios, CSI phase errors in baseband can be effectively eliminated, which makes CSI phase difference a compelling feature to represent the direction of incident radio frequency (RF) signals and the dynamic channel characteristics. A convolutional neural network (CNN) model is then trained with labeled samples of CSI phase difference. We implement a prototype of DeepAoA receiver using four synchronized USRPs with their antennas in uniform circular array (UCA) configuration for full field of view. We collect real-world CSI trace and conduct trace-driven simulations. DeepAoA can achieve AoA estimation errors of less than 3 degrees with a 98% confidence interval with four antennas. The results demonstrate the efficacy of DeepAoA.
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DeepAoA: Online Vehicular Direction Finding Based on a Deep Learning Method
Semantic Scholar · Computer Science · 2019
Abstract
Relative direction estimation among neighboring vehicles in urban environment is essential to a wide variety of driving safety applications. To obtain accurate direction information solely from vehicle-to-vehicle (V2V) communications is desirable but very challenging due to the highly dynamic vehicular environments. In this paper, we propose an online vehicular AoA estimation scheme, called DeepAoA, based on a deep learning method. More specifically, Channel state information (CSI) is estimated from a set of synchronized receiving radios by a receiver vehicle. By taking the CSI phase difference between a pair of such radios, CSI phase errors in baseband can be effectively eliminated, which makes CSI phase difference a compelling feature to represent the direction of incident radio frequency (RF) signals and the dynamic channel characteristics. A convolutional neural network (CNN) model is then trained with labeled samples of CSI phase difference. We implement a prototype of DeepAoA receiver using four synchronized USRPs with their antennas in uniform circular array (UCA) configuration for full field of view. We collect real-world CSI trace and conduct trace-driven simulations. DeepAoA can achieve AoA estimation errors of less than 3 degrees with a 98% confidence interval with four antennas. The results demonstrate the efficacy of DeepAoA.