Indoor High Precision Three-Dimensional Positioning System Based on Visible Light Communication Using Improved Hybrid Bat Algorithm

Abstract

To solve problems of low positioning accuracy and long time-consuming in indoor visible light three-dimensional positioning system, a visible light three-dimensional positioning method based on the improved hybrid bat algorithm (IHBA) is proposed in this paper. Firstly, some beacon points are set at the beginning of the IHBA to reduce the number of iterations. Secondly, weight coefficient is defined to improve positioning accuracy when the fitness function is constructed. Thirdly, aiming at controlling the search speed reasonably, an adaptive search factor is introduced while the bat individual update formula is designed. Finally, the chaotic perturbation operation is used to avoid the algorithm falling into local optimum. In indoor simulation environment, the size of which is 5m×5m×3m, the average positioning error of the IHBA is 1.16cm and the positioning time is 1.85s. To verify the actual effectiveness of IHBA, a receiver is placed in the experimental environment, the size of which is 1.5m×1.2m×2m. Through the optical power detected by the receiver, the processor calculate its coordinates with IHBA. The average positioning error of the IHBA is 3.64cm and the positioning time is 0.89s. In this paper, the three-dimensional positioning system based on the IHBA algorithm can achieve high-precision and low-time positioning, and it has practical application value in existing indoor positioning methods.

An Indoor Visible Light Positioning System for Multi-Cell Networks

“…In [19], Huang et al employ received signal strength for a two-dimensional VLP system with a positioning accuracy of about 8 cm in a small 200 × 60 × 60 cm 3 space. On the other hand, there are only few research works that have proposed a VLP architecture for a multi-cell area based on white LEDs [14][15][16][20][21][22][23]. For example, in [24], Little et al proposed a multi-cell lighting testbed for a VLP system based on the RSS technique and radio frequency allocation.…”

Indoor high-accuracy multi-dimensional visible light positioning method with adaptive particle swarm optimization algorithm

“…In this system, the RSS model has been adopted to determine the accurate multi-dimensional coordinate of the target by measuring the DC gains between target and LEDs. Here LEDs can be regarded as the Lambert light sources, and the DC gain can be described by Lambert radiation model: 22 Hi(0)=PRiPTi=(m+1)A2πd2 cos φm cos ψnTs(ψ)g(ψ)rect(ψFOV).…”

“…As for any practical communication system, noise is an inevitable issue. In the indoor visible light multi-dimensional positioning system, there are mainly ambient light noise σa2, thermal noise σt2, and shot noise σs2, 15 , 16 , 22 where the background light noise σa2 is basically certain and can be relatively easily removed by the optical filter. Consequently, the thermal noise σt2 and shot noise σs2 will mainly affect the performance of positioning system, and the total noise σT2 of this positioning system can be expressed as 15 σT2=σt2+σs2.…”

“…In Refs. 21 and 22, the improved hybrid bat algorithm (IHBA) and improved immune particle swarm optimization algorithm were introduced into indoor VLP system, respectively, considering the impact of the tilt of receiver including tilt and azimuth angles. However, the tilt angles should be calculated in advance and taken as the input of algorithms to calculate the position.…”

Indoor High-Precision 3D Positioning System Based on Visible-Light Communication Using Improved Whale Optimization Algorithm

“…Xu et al presented an indoor 3D visible-light location system using adaptive parameter particle swarm optimization, and the system achieved similar positioning results with a smaller swarm [19]. Chen et al proposed an indoor VLP system based on an improved hybrid bat algorithm, and the system achieved an average positioning error of 1.16 cm in a space of 5 m × 5 m × 3 m [20]. Chen et al proposed an indoor 3D VLC system based on an improved immune particle swarm optimization algorithm, which considered the influence of environmental interference, and an average positioning error of 3.12 cm was achieved [21].…”