Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication

CSI-based sliding window fingerprinting method tailored for a signal blocking environment in VLP systems

In visible light indoor positioning systems, the localization performance of the received signal strength (RSS)-based fingerprinting algorithm would drop dramatically due to the occlusion of the line-of-sight (LOS) signal caused by randomly moving people or objects. A sliding window fingerprinting (SWF) algorithm based on channel state information (CSI) is put forward to enhance the accuracy and robustness of indoor positioning in this work. The core idea behind SWF is to combine CSI with sliding matching. The sliding window is used to match the received CSI and the fingerprints in the database twice to obtain the optimal matching value and reduce the interference caused by the lack of the LOS signal. On this premise, in order to reflect the different contributions of various paths in CSI to the calculation of match values, a weighted sliding window fingerprinting (W-SWF) is also proposed for the purpose of further improving the accuracy of fingerprint matching. A 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to evaluate the positioning performance. The simulation results reveal that the mean errors of the proposed method are 0.20 cm and 1.43 cm respectively when the LOS signal of 1 or 2 LEDs is blocked. Compared with the traditional RSS algorithm, the weighted k-nearest neighbor (WKNN) algorithm, and the adaptive residual weighted k-nearest neighbor (ARWKNN) algorithm, the SWF algorithm achieves over 90% improvement in terms of mean error and root mean square error (RMSE).

Advanced Sensors and Systems Technologies for Indoor Positioning

There is an increasing interest about indoor positioning, which is an emerging technology with a wide range of applications [...].

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning

To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL₁ and RL₂, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL₁ and RL₂ as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL₂ and RL₁ sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL.