Offline Docked-Phone Indoor Carpark Navigation: Fusing RF and IMU Signals With HMM
Abstract: We study the challenging problem of navigating in an indoor carpark in the absence of global navigation satellite system (GNSS) and cellular signals using an offline smartphone docked at the car dashboard. There is basic radio frequency (RF) infrastructure in the venue but, due to signal attenuation by the car body, the location computed based on the in-car signal is noisy and intermittent. Previous works on carpark navigation often require costly specialized equipment as on-car additional infrastructure (OCAI), or suffer from error propagation stemmed from integrating inertial measurement unit (IMU) signals over time. We propose RICH, a novel, simple, accurate, and cost-effective docked-phone approach to fuse RF and IMU signals for indoor carpark navigation using the hidden Markov model (HMM). RICH uses IMU signals to detect the speed level and turning of the car, which is then fused with the crude RF localization in an HMM framework to estimate the car’s location distribution in real-time. We further present an analysis on the tradeoff between computation and accuracy of RICH. Our extensive experiments on smartphone in real carparks show that, as compared with the state-of-the-art, RICH achieves substantially lower localization error (by 40%) with high-computational efficiency (less than 10 ms per location).
Loading