Square and circular light source layouts conventionally employed in visible light communication (VLC) systems suffer from communication blind spots and received signal nonuniformity as well as from large power fluctuations in the middle of the room. To address these shortcomings, we use the cross square and annulus layouts in conjunction with the cross-fertilize particle swarm optimization algorithm to improve VLC system performance. The distribution of the unit area received power (RPUA), the distribution of the signal-to-noise ratio (SNR), and the bit error rate (BER) performance were simulated and the rationality of the algorithm was compared. The results show that after optimization, the RPUA fluctuation value of the cross square layout decreases from 2.32 to 1.59 dB / m2, the SNR fluctuation value reduces from 8.5 to 4.2 dB, and the RPUA fluctuation value of the annulus layout decreases from 4.69 to 3.46 dB / m2, the SNR fluctuation value decreases from 13.3 to 8.8 dB. The RPUA value of cross-square layout fluctuates between −1.5 and −0.5 dB / m2, and the receiving area accounts for 93.4% in this range while in the case of the square layout, this area is only 86.7%. Therefore, it suggests that this scheme is reasonable for the equality of communication quality and system performance heightens greatly.
This paper proposes a three-dimensional (3-D) high-precision indoor positioning strategy using Tabu search based on visible light communication. Tabu search is a powerful global optimization algorithm, and the 3-D indoor positioning can be transformed into an optimal solution problem. Therefore, in the 3-D indoor positioning, the optimal receiver coordinate can be obtained by the Tabu search algorithm. For all we know, this is the first time the Tabu search algorithm is applied to visible light positioning. Each light-emitting diode (LED) in the system broadcasts a unique identity (ID) and transmits the ID information. When the receiver detects optical signals with ID information from different LEDs, using the global optimization of the Tabu search algorithm, the 3-D high-precision indoor positioning can be realized when the fitness value meets certain conditions. Simulation results show that the average positioning error is 0.79 cm, and the maximum error is 5.88 cm. The extended experiment of trajectory tracking also shows that 95.05% positioning errors are below 1.428 cm. It can be concluded from the data that the 3-D indoor positioning based on the Tabu search algorithm achieves the requirements of centimeter level indoor positioning. The algorithm used in indoor positioning is very effective and practical and is superior to other existing methods for visible light indoor positioning.
An indoor positioning algorithm based on visible light communication (VLC) is presented. This algorithm is used to calculate a three-dimensional (3-D) coordinate of an indoor optical wireless environment, which includes sufficient orders of multipath reflections from reflecting surfaces of the room. Leveraging the global optimization ability of the genetic algorithm (GA), an innovative framework for 3-D position estimation based on a modified genetic algorithm is proposed. Unlike other techniques using VLC for positioning, the proposed system can achieve indoor 3-D localization without making assumptions about the height or acquiring the orientation angle of the mobile terminal. Simulation results show that an average localization error of less than 1.02 cm can be achieved. In addition, in most VLC-positioning systems, the effect of reflection is always neglected and its performance is limited by reflection, which makes the results not so accurate for a real scenario and the positioning errors at the corners are relatively larger than other places. So, we take the first-order reflection into consideration and use artificial neural network to match the model of a nonlinear channel. The studies show that under the nonlinear matching of direct and reflected channels the average positioning errors of four corners decrease from 11.94 to 0.95 cm. The employed algorithm is emerged as an effective and practical method for indoor localization and outperform other existing indoor wireless localization approaches.
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