Since the well-established navigation systems such as GPS are ineffective in indoor environments, research into developing novel indoor positioning technologies has emerged in recent years. While several technologies are being investigated, a practical and reliable indoor positioning system is yet to emerge. Indoor positioning using light signals holds a great potential to provide a reliable solution to the indoor positioning problem. This paper presents a comprehensive review of the available literature on light-based indoor positioning systems. The paper establishes a classification based on the design of various light-based indoor positioning (LIP) systems, and discusses their key components including hardware, software and algorithms. The limitations and challenges of the LIP systems are discussed in details, and current published solutions are presented. The review also provides a comparison between LIP systems and other optical positioning systems, and identifies the associated research gaps. Finally, based on the identified gaps, the future research directions in both LIP systems and other optical positioning systems are presented.

Light-Based Indoor Positioning Systems: A Review

In this paper, a 3D indoor visible light positioning (VLP) system with fast computation time using received signal strength (RSS) is proposed and experimentally demonstrated. Assisted by the deep learning techniques, the complexity of the trilateration problem is greatly reduced, and the trilateration problem can be formulated as a linear mapping leading to faster position estimation than the conventional estimation. Moreover, a new method of off-line preparation is adopted to minimize the workload of the VLP system deployment for more practical usage. The proposition is implemented on an atto-cellular VLP unit, through which the real-time performance and positioning accuracy are demonstrated and validated in a 3D positioning experiment performed in a space of 1.2 × 1.2 × 2 m
 3
. The experimental results show that a positioning accuracy of 11.93 cm in confidence of 90% is achieved with 50 times faster the computation time compared to the conventional scheme.

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Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique

A three-dimensional (3-D) positioning system on the basis of bat algorithm (BA) using visible light communication is proposed in this work. BA is a global optimization algorithm that can be used to solve the indoor positioning problem. In BA, the positioning process is considered to be a process of searching for the receiver by many bats in an indoor space of 3  m×3  m×4  m. Therefore, taking advantage of the bat search algorithm, 3-D low-complexity and high-precision indoor positioning is able to be actualized when the number of iterations reaches a certain condition. The simulation results show that the system can achieve high-precision positioning at different signal-to-noise ratios. Meanwhile, a trajectory tracking experiment is carried out to validate the good performance of this positioning system. The proposed algorithm is a positioning method with good potential in various positioning applications.

Indoor three-dimensional high-precision positioning system with bat algorithm based on visible light communication.

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 1.16 cm 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 3.64 cm and the positioning time is 0.89 s. The proposed algorithm has practical application value in existing indoor positioning methods.

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

The continuous positioning of a mobile user equipment (MU) in a prompt way is a fundamental requirement in several indoor applications related to ubiquitous computation, assisted living environments, and security/surveillance systems. Motivated by this fact, the current contribution reports a low-complexity visible light positioning (VLP) method suitable for indoor environments in the Big Data era. The proposed architecture consists of multiple light-emitting diodes (LEDs) as light sources and an MU equipped with a photodiode (PD). To guarantee higher spectral efficiency, the LEDs emit sinusoidal waveforms at slightly different predetermined frequencies. The light intensity received at the PD from every LED is continuously estimated after applying a short-time Fourier transformation. To this end, a grid-based numerical technique is proposed to recover the unknown MU position. Additionally, a closed-form solution is presented for the particular case of three LEDs that overrides the need for training points in the grid arrangement. Further, a method for handling noisy signals is proposed, based on averaging the calculated positions from densely overlapping received signals. Finally, a Kalman filter is employed as a post-processing precision improving tool. The proposed VLP efficiency is quantified through respective Monte Carlo simulations that allow the setting of different LED frequencies, received signal processing parameters, MU speed, and noise levels. The results reveal that the suggested approach is robust with significant tolerance in high ambient light levels, computationally efficient, and exhibits low positional error. Finally, to evaluate the performance improvements the new method introduces, we make comparisons against widely-used fingerprint approaches.

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A Low Complexity Indoor Visible Light Positioning Method

Visible light positioning has a good application prospect because of its simultaneous lighting, convenience, and security. However, most existing visible light communication (VLC) positioning systems cannot achieve real three-dimensional (3-D) positioning but just the small range approximation or fail to provide satisfactory positioning precision and speed. We propose a 3-D indoor localization system based on VLC using improved bacterial colony chemotaxis algorithm. The positioning problem is transformed into a global optimization problem. We reduce the dimensions of the positioning algorithm from 3-D to one-dimensional and propose a fitness function based on the simple geometric relationship between light-emitting diodes’ projection circles in the horizontal plane. In addition, we improve the traditional bacterial colony chemotaxis algorithm by adopting self-adaptive reception scope to improve global convergence and introducing differential evolution operator to overcome the algorithm premature. Our simulation results show that the mean positioning error is 0.73 mm in an indoor space of 5  m  ×  5  m  ×  6  m and the positioning time for a single point is 21.8 ms. Also, the positioning results show the advantages of the proposed positioning algorithm with high precision and high speed. The proposed positioning algorithm is practical and efficient, showing great application prospects in indoor positioning.

High precision and high speed of three-dimensional indoor localization system based on visible light communication using improved bacterial colony chemotaxis algorithm

Recently, indoor positioning system (IPS) based on visible light communication (VLC) has gradually become a hot research topic. However, many existing indoor localization schemes based on VLC only provide solutions of positioning on a two-dimensional plane. Some IPSs-based VLC that focus on three-dimensional (3-D) positioning usually contain various sensors or use complex algorithms. A high-precision indoor 3-D localization scheme based on VLC using modified fruit fly optimization algorithm (MFOA) is proposed. Since it is difficult to directly calculate the coordinate of PD receiver in 3-D positioning problem, we transform the 3-D positioning problem into a global optimization problem and design the corresponding fitness function according to the Lambertian radiation pattern, which can achieve a higher accuracy and reduce system complexity. By changing the structure of fly group in the traditional fruit fly optimization algorithm and designing an adaptive search scope, the MFOA is obtained to solve the positioning problem, which leads to a nice positioning effect. With a good convergence performance, our simulation shows an average error of 0.76 cm within 20 iterations in an indoor space of 4  m  ×  4  m  ×  6  m, and most 3-D positioning errors are <1.85  cm, which shows a good positioning performance. Therefore, our indoor localization scheme has a potential value in the actual practice of indoor localization.

Yingxian Xian

Papers

High precision and high speed of three-dimensional indoor localization system based on visible light communication using improved bacterial colony chemotaxis algorithm

High-precision indoor three-dimensional localization scheme based on visible light communication using modified fruit fly optimization algorithm