Recurrent neural network based scenario recognition with Multi-constellation GNSS measurements on a smartphone

Abstract As an upper layer context-aware mobile application, fast and accurate scenario recognition is essential for seamless indoor and outdoor localization and robust positioning in complex environments. With the popularity of multi-constellation smartphones, scenario recognition based on smartphone Global Navigation Satellite System (GNSS) measurements becomes desirable. In this paper, we divide the complex environments into four categories (deep indoor, shallow indoor, semi-outdoor and open outdoor) and conduct research work in two areas. Firstly, we analyze in detail the influence of multi-constellation satellite signals on scenario recognition performance based on a Hidden Markov Model (HMM) algorithm. The experimental results show that the scenario recognition accuracy is improved significantly with the increase of the number of constellations received by smartphones. Secondly, in order to solve the description degradation of the traditional model caused by scenario transitions and environmental changes around the scenario, we propose a new scenario recognition method based on Recurrent Neural Network (RNN). Considering the computational complexity and the availability of feature values, we utilize the position-independent features as the input of the RNN model, and then evaluate the performance of the model using test sets from the new places. The results indicate that our proposed algorithm has high recognition accuracy in both isolated scenarios and transition regions, with the overall accuracy of 98.65%. Especially in the scenario transitions, the recognition accuracy reaches 90.94% and in the three times of correct recognition for scenario transitions (four times in total), the maximum transition delay is only 3 s.