Indoor location and orientation detection based on visible light communication using an improved sparrow search algorithm

Visible light positioning (VLP) technology has recently been considered as a high-precision solution for indoor positioning. In a practical VLP system, since the receiver terminal is usually not placed horizontally, the receiver orientation needs to be considered during positioning to achieve high positioning accuracy. The VLP based on received signal strength (RSS) can be considered as a non-convex optimization problem regarding receiver location and orientation. Although various metaheuristic algorithms were extensively adopted in VLP systems to solve the optimization problems, most of them are based on the ideal assumption that the user device is placed horizontally. In this paper, a recently proposed metaheuristic algorithm called sparrow search algorithm (SSA) is considered for indoor VLP systems because of its good performance in solving the optimization problems. Moreover, the original SSA algorithm is improved to solve the optimization problems more effectively. In the improved SSA algorithm, both global and local optimal values are combined to enhance the capability of escaping local optima. In addition, nonlinear weights are used during position updating. Moreover, Levy flight strategy is introduced to achieve higher positioning accuracy. Simulation results show that compared to the popular metaheuristic algorithm called grey wolf optimization (GWO), both the original SSA and the improved SSA algorithms can achieve higher positioning accuracy, and the proposed NWL-SSA algorithm performs best.