High-precision Indoor Positioning Research Articles

Probability-Based Indoor Positioning Algorithm Using iBeacons.

High-precision indoor positioning is important for modern society. This paper proposes a way to achieve high positioning accuracy and obtain a trajectory close to the actual path in a common application scenario by smartphone without the use of a complicated algorithm. In the actual positioning process, a stable signal source can reduce the signal interference caused by environments. Bluetooth low energy has its own advantages in indoor positioning because it can be seen as a more stable signal source. In this study, we used smartphones to record the changing Bluetooth signals and used a basic nearest neighbor, weight centroid, and probability-based method, which we called an advanced weighted centroid method, to obtain position coordinates and the motion trajectory during the experiment. We used a weight centroid method based on least squares to solve the overdetermined problem. This can also be used to calculate the initial position of the advanced weight centroid. The advanced weighted centroid method introduced a Gaussian distribution to model the distribution of the signal. Translating a deterministic problem into a fuzzy probability problem aligns more with positioning facts and can achieve better results. Experimental results showed that the root-mean-square error (RMSE) of the dynamic positioning result obtained through the probabilistic method was within 1 m and had a more consistent trajectory. Moreover, the impact of the number of iBeacons on the positioning accuracy has been discussed, and a reference for iBeacon placement has been provided. In addition, an experiment was also conducted on the effect of signal transmission frequency on accuracy.

Local‐area nanosecond‐accuracy time synchronisation based on GPS L1 observations

To date, high-precision time synchronisation has been applied in many fields, such as high-precision indoor positioning, multistatic Synthetic Aperture Radar (SAR) and etc. This study focuses on the use of only global positioning system (GPS) L1 civilian signals to achieve regional nanosecond time synchronisation. GPS errors are analysed in this study to determine their effect on GPS timing. This study also examines the specific features of the error in fixed-position timing technology and related error reduction techniques to illustrate the feasibility of nanosecond high-precision time synchronisation with GPS L1 signals in a fixed-position timing mode. Then, the results of a specific GPS timing receiver working in a fixed-position mode and using GPS L1 signals are shown to indicate that the use of GPS L1 signals to achieve nanosecond-accuracy time synchronisation in a local area is feasible. Finally, this study also analyses the impacts of the imprecise position in a fixed-position timing mode.

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

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.

High-precision indoor three-dimensional positioning system based on visible light communication using modified artificial fish swarm algorithm

Indoor visible light positioning has become a research hotspot owing to its high accuracy, low cost, and easy implementation. However, many existing indoor positioning systems based on visible light communication (VLC) can only achieve two-dimensional (2-D) positioning and those focusing on three-dimensional (3-D) positioning usually contain various sensors or use complex algorithms. Consequently, a high-precision indoor 3-D positioning system based on VLC using modified artificial fish swarm algorithm is proposed, avoiding the problems mentioned above. To simplify the solution procedure of searching for 3-D coordinates, the 3-D positioning problem is transformed into a global optimization problem with the corresponding fitness function designed according to Lambertian radiation pattern. The simulation executed on 1350 points shows an average 3-D positioning error of 4.05 mm in an indoor space of 4 m × 4 m × 6 m, and 90% positioning errors are below 5.95 mm. Moreover, when tracking a moving target in the positioning space, the average error is 3.57 mm and the maximum error is 7.25 mm. All in all, the system proposed in this paper stands a good chance of widespread application for its excellent performance in indoor 3-D positioning.

Amp-Phi: A CSI-Based Indoor Positioning System

With rapid growth in the demand of location-based services (LBS) in indoor environments, localizations based on fingerprinting have attracted significant interest due to their convenience. Until now, most such methods were based on received signal strength indicator (RSSI), which is vulnerable to non-line-of-sight (NLOS). In order to realize high-precision indoor positioning, we propose a channel state information (CSI)-based Amp-Phi indoor-positioning system which exploits the amplitude and phase information of CSI at the same time to establish a fingerprinting database. Firstly, according to the characteristics of the raw CSI information collected at different positions under different environments, we build an NLOS mitigation model and a phase error mitigation model, respectively, to process the amplitude and phase of CSI. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After being processed with the models, the CSI features can be used to distinguish different positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build a fingerprinting database based on the features of amplitude and phase, realize to locate the target’s position with the K-Nearest Neighbor (KNN) matching algorithm. Experiments implemented in different situations show that Amp-Pi system is reliable and robust, whose position accuracy is higher than that of PhaseFi, Horus and machine learning (ML) systems under the same condition. It can be used in many scenarios, such as the localization of robots in our daily life, by doctors or patients in the hospital, for people localization in large supermarkets or museums and so on.

An RFID Indoor Positioning Algorithm Based on Support Vector Regression.

Nowadays, location-based services, which include services to identify the location of a person or an object, have many uses in social life. Though traditional GPS positioning can provide high quality positioning services in outdoor environments, due to the shielding of buildings and the interference of indoor environments, researchers and enterprises have paid more attention to how to perform high precision indoor positioning. There are many indoor positioning technologies, such as WiFi, Bluetooth, UWB and RFID. RFID positioning technology is favored by researchers because of its lower cost and higher accuracy. One of the methods that is applied to indoor positioning is the LANDMARC algorithm, which uses RFID tags and readers to implement an Indoor Positioning System (IPS). However, the accuracy of the LANDMARC positioning algorithm relies on the density of reference tags and the performance of RFID readers. In this paper, we introduce the weighted path length and support vector regression algorithm to improve the positioning precision of LANDMARC. The results show that the proposed algorithm is effective.

Three-dimensional high-precision indoor positioning strategy using Tabu search based on visible light communication

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.

Basmag: An Optimized HMM-Based Localization System Using Backward Sequences Matching Algorithm Exploiting Geomagnetic Information

Due to the complex building construction materials, the geomagnetic field has great anomalies in indoor environments. These anomalies generate distinctive signatures corresponding to positions. Many hybrid localization schemes that combine geomagnetic information and pedestrian dead reckoning (PDR) have been proposed. In this paper, we regard these distinctive geomagnetic anomalies as fingerprints for high precision indoor positioning. In order to improve the hybrid localization accuracy, we present Basmag, a robust hidden Markov model (HMM)-based indoor localization system, and we also propose a backward sequences matching algorithm (BSMA) to optimize the HMM. In order to improve the low discernibility of single geomagnetic signal, we vectorize the backward consecutive geomagnetic signals to fingerprint sequences with the help of PDR. We match the backward sequences with a pre-constructed fingerprint database to get more precise transition probabilities in HMM. We conducted a theoretical analysis of the feasibility of the BSMA. Extensive simulation results show that Basmag can achieve high-accuracy positioning. Finally, we conducted experiments performed on a smartphone in an indoor area to compare Basmag with other geomagnetic field-based and WiFi-based localization methods, and the experimental results verify the effectiveness and robustness among users with different walking styles of Basmag.

Autocorrelation Phase Measurement of Spread Spectrum Signal for High Precision Indoor Microwave Positioning

The increasing demand for high precision indoor positioning in many public services has urged research to implement cost-effective systems for a rising number of applications. However, current systems with either short-range positioning technology based on wireless local area networks (WLAN) and ZigBee achieving meter-level accuracy, or ultra-wide band (UWB) and 60 GHz communication technology achieving high precision but with high cost required, could not meet the need of indoor wireless positioning. This paper presents a new method of high precision indoor positioning by autocorrelation phase measurement of spread spectrum signal utilizing carrier frequency lower than 1 GHz, thereby decreasing power emission and hardware cost. The phase measurement is more sensitive to the distance of microwave transmission than timing, thus achieving higher positioning accuracy. Simulation results demonstrate that the proposed positioning method can achieve high precision of less than 1 centimeter decreasing when various noise and interference added.

A High Precision Indoor Positioning System Based on VLC and Smart Handheld

This paper proposes an indoor positioning system based on visible light communication technology and intelligent terminals which equipped with image sensors, electronic compasses and gyroscope sensors. Each LED source is modulated with unique codes which represent its absolute location. The receiving terminal gets the LED’s absolute location through the optical link and capture the scene image with image sensor simultaneously. The electronic compass and the gyroscope sensor measure the terminal’s yaw angle, pitch angle and roll angle in real time, together with the scene image, relative position of the terminal and the LED light source can be calculated. Combine the absolute location of the LED light source with the relative position, the system is able to estimate the terminal’s position within the accuracy of about 2 centimeter. Position accuracy can be improved by using high-precision camera and attitude sensors.

Data Pre-Processing in Compressive Sensing Based Indoor Fingerprinting Positioning

Presented is a novel compressive sensing (CS) based indoor positioning approach, which uses the signal strength differentials (SSDs) as location fingerprints (LFs). By using certain kernel-based transformation basis, the 2-D target location is represented as an unknown sparse location vector in the discrete spatial domain. Then it just takes a little number of online noisy SSD measurements for the exact recovery of the sparse location vector by solving an l1-minimization program. In order to effectively apply CS theory for high precision indoor positioning, we further import some data pre-processing algorithms in the LF space. Firstly, to mitigate the influence of large measurements noise on the recovery accuracy, a LF space denosing algorithm is designed to discriminate the unequal localization contribution rate of every SSD measurement in each LF. The basic idea of the denosing algorithm is to transform the original LF space into a robust and decorrelated LF space. Moreover, in order to lower the high computational complexity of the CS recovery algorithm, several LF space filtering algorithms are also exploited to remove certain percentage of useless LFs in the radio map according to the real-time RSS observations. The performance of these denosing and filtering algorithms are investigated and compared in real-world WLAN experiment test. Both experimental results and simulations demonstrate that we achieve remarkable improvements on the positioning performance of the CS based localization by using the proposed algorithms.

High Precision Indoor and Outdoor Positioning using LocataNet

Today, GPS is the most popular and widely used three-dimensional positioning technology in the world. However, in many everyday environments such as indoors or in urban areas, GPS signals are not available for positioning (due to the very weak signals). Even with high sensitivity GPS receivers, positioning for urban and indoor environments cannot be guaranteed in all situations, and accuracies are typically of the order of tens to hundreds of meters at best. Other emerging technologies obtain positions from systems that are not designed for positioning, such as mobile phones or television. As a result, the accuracy, reliability and simplicity of the position solution is typically very poor in comparison to GPS with a clear view of the sky. Locata is a new positioning technology, developed to address the failure of current technologies for reliable ubiquitous (outdoor and indoor) positioning. In this paper key aspects of the new technology are discussed, with particular emphasis on the positioning network (LocataNet). An innovative characteristic of the LocataNet is its ability to propagate (autonomously) into difficult environments and over wide areas. Through an experimental LocataNet installation, a key mechanism for achieving this is tested, and real-time stand-alone positioning (without a base station and additional data link) with sub-centimeter precision is demonstrated.