Abstract
Indoor positioning technology is vital for various location-aware applications while visible light positioning (VLP) is especially promising due to its ubiquitous and energy-efficient features. VLP has been widely investigated under the assumption of line of sight (LoS), yet, VLP signal blockage can happen frequently in a practical indoor environment and brings about outage problems to indoor localization/tracking services. However, this problem is usually overlooked or sidestepped in the existing works. Our work, for the first time, investigates the outage problem in a received signal strength (RSS)-based VLP system. Efficient algorithms for outage bridging and trajectory recovery are proposed by smartly fusing with insufficient RSS information. Specifically, a partial-RSS-assisted inertial navigation system (PRAINS) inspired by extended Kalman filter (EKF) is developed to bridge sporadic outage, while a bi-directional structured PRAINS (Bid-PRAINS) is developed to use both pre- and post- outage information to recover the lost trajectory information. To further deal with a more general situation when the system noise features are not pre-known and hard to be measured/estimated, a semi-parameterized RNN based learnable Kalman filter (SPR-LKF) is proposed in place of the EKF to learn the observation/transition noise features and optimize the estimation simultaneously through a recurrent neural network (RNN). Extensive tests show that the PRAINS/ Bid-PRAINS has at least 62% accuracy improvement over the conventional inertial navigation system (INS)-only algorithm, while the proposed SPR-LKF/ Bid-SPR-LKF can offer an even better accuracy gain of 70% even without pre-knowing the system noise feature.
Highlights
Indoor positioning with high accuracy and low cost is in urgent need
We further propose a semi-parameterized learnable Kalman filter (SPR-LKF) which exploits the inherent recurrent structure of a Kalman filter
In the existing received signal strength (RSS)-based visible light positioning (VLP) systems, a sufficient number of line of sight (LoS) links are commonly assumed during the entire period of localization, which is unlikely to be satisfied all the time in a practical dynamic indoor environment, leading to interruption of localization
Summary
Indoor positioning ( known as indoor localization) with high accuracy and low cost is in urgent need. Work [15] proposed a single LED based VLP system, which reduces outage probability It requires a specially-designed circular LED with a marker and a high-resolution image sensor to support accurate geometric information extraction, which limits its applicability. Their model cannot deal with the situation where noise parameters of the model are not preknown or hard to be measured After reviewing these related works, it is concluded that the outage problem in VLP systems is rarely investigated, and no proper solution exists or can be transferred/learned from GPS. To fill this gap, our idea is to fully exploit the partial RSS information. By fusing with real-time RSS measurements, the major problems of INS mentioned in Section I, including sensor drifting and cumulative error, can be relieved or eliminated
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