Offline Docked-Phone Indoor Carpark Navigation: Fusing RF and IMU Signals With HMM

We study the challenging problem of navigating in an indoor carpark in the absence of GNSS (Global Navigation Satellite System) and cellular signals using an offline smartphone docked at the car dashboard. There is basic RF (radio-frequency) infrastructure in the venue but, due to signal attenuation by the car body, the location computed based on 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 IMU (inertial measurement unit) 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 HMM (Hidden Markov Model). 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 trade-off 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 computationally efficiency (less than 10ms per location).