Accuracy Of Indoor Positioning System Research Articles

Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems

In recent years, the use of indoor localization techniques has increased significantly in a large number of areas, including industry and healthcare, primarily for monitoring and tracking reasons. From the field of radio frequency technologies, an ultra-wideband (UWB) system offers comparatively high accuracy and is therefore suitable for use cases with high precision requirements in position determination, for example for localizing an employee when interacting with a machine tool on the shopfloor. Indoor positioning systems with radio signals are influenced by environmental obstacles. Although the influence of building structures like walls and furniture was already analysed in the literature before, the influence of metal machine tools was not yet evaluated concerning the accuracy of the position determination. Accordingly, the research question for this article is defined: To what extent is the positioning accuracy of the UWB system influenced by a metal machine tool?The accuracy was measured in a test setup, which consists of a total of four scenarios in a production environment. For this purpose, the visual contact between the transmitter and the receiver modules, including the influence of further interfering factors of a commercially available indoor positioning system, was improved step by step from scenario 1 to 4. A laser tracker was used as the reference measuring device. The data was analysed based on the type A evaluation of standard uncertainty according to the guide to the expression of uncertainty in measurement (GUM). It was possible to show an improvement in standard deviation from 87.64cm±32.27cm to 6.07cm±2.24cm with confidence level 95% and thus provides conclusions about the setup of an indoor positioning system on the shopfloor.

Improving measurement accuracy of indoor positioning system of a Mecanum wheeled mobile robot using Monte Carlo - Latin hypercube sampling based machine learning algorithm

In this study, an efficient ultra-wideband technology based indoor positioning system is developed to improve the measurement accuracy of a mecanum wheeled mobile robot. Data processing structure of the system is created using Monte Carlo - Latin Hypercube Sampling based machine learning algorithm. Monte Carlo - Latin Hypercube Sampling method can be used to perform the position estimation since it enables the range of each variable is fully accessible. This makes the algorithm outputs more accurate and reliable and thus increases correctness in measurements. An experimental setup including hardware, software and mathematical model is developed. A number of experiments are conducted to validate the indoor positioning system proposed. Efficiency, accuracy and precision of methodology are also tested in the experimental studies. The adaptation of the algorithm in a real system and the field tests are also conducted using a four-mecanum-wheeled mobile robot. The experimental studies show that the position estimation is improved by more than 20% using the proposed algorithm compared to the methods presented in the literature.

Indoor positioning system using hybrid method of fingerprinting and pedestrian dead reckoning

Indoor Positioning System has been one of the most attractive research after Bluetooth Low Energy (BLE) was introduced. BLE technology is mainly used because of the reduction of material and energy cost over time that has huge impact compared to other technology which is more costly. In this research, we propose a new hybrid method to increase the accuracy of indoor positioning system using hybrid of BLE fingerprinting and PDR. Kalman Filter is used as benchmark of this experiment and ANN and SVR are proposed as a new method to combine both measurements. On benchmark, Kalman filter achieve positioning root-mean-squared error 212.21 cm and the proposed SVR 149.12 cm and ANN 111.78 cm.

Position Vectors Based Efficient Indoor Positioning System

With the advent and advancements in the wireless technologies, Wi-Fi fingerprinting-based Indoor Positioning System (IPS) has become one of the most promising solutions for localization in indoor environments. Unlike the outdoor environment, the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efficient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things (IoTs) and green computing. In this paper, we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors. Initially, in the database development phase, Motley Kennan propagation model is used with Hough transformation to classify, detect, and assign different attenuation factors related to the types of walls. Furthermore, important parameters for system accuracy, such as, placement and geometry of Access Points (APs) in the coverage area are also considered. New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm (GA) coupled with Enhanced Dilution of Precision (EDOP). Moreover, classification algorithm based on <i>k</i>-Nearest Neighbors (<i>k</i>-NN) is used to find the position of a stationary or mobile user inside the given coverage area. For <i>k</i>-NN to provide low localization error and reduced space dimensionality, three APs are required to be selected optimally. In this paper, we have suggested an idea to select APs based on Position Vectors (PV) as an input to the localization algorithm. Deducing from our comprehensive investigations, it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with significant improvements.

Design of a beam steering antenna array using 8x8 butter matrix for indoor positioning system

ABSTRACT This paper proposes a 5 GHz beam steering antenna array (BSAA) based on 8 × 8 Butler Matrix for Indoor Positioning System (IPS). The main beam of BSAA is narrow in azimuth plane with 12° of beam-width while the beam-width in elevation plane is 50°-120°. The steering angle of BSAA in azimuth plane is 108° with eight beams at −52°, −37°, −20°, −8°, +9°, +25°, +40°, +56°, and the peak gains of 7.8 dBi – 11 dBi. The wide beam-width in elevation plane helps to increase the positioning area of BSAA. Also, the narrow beam-width in azimuth plane allows improving the accuracy of IPS.

A fingerprint-based coarse-to-fine algorithm for indoor positioning system using Bluetooth Low Energy

In this research, we propose a fingerprint-based fine-tuning algorithm to increase the accuracy of indoor positioning system using Bluetooth Low Energy fingerprint. In the fine-tuning step, Delta rule is used to update the coordinates of reference points based on the wireless environment represented in the training dataset. The combination of the coarse estimation and the proposed fine-tuning makes the coarse-to-fine algorithm. The algorithms used for coarse estimation are weighted sum and k-nearest neighbour, with three weight calculation algorithms that are compared in this experiment: Minkowski distance, k-means Gaussian mixture model, and autoencoder. Two subject rooms are used in order to measure the performance of the algorithms. The experiment showed that the fine-tuning algorithm improves the accuracy of the positioning throughout all combination of methods in both rooms, which shows its versatility. It reduces the mean positioning error by up to 11.3% and depends on what algorithm used in the weight calculation. Another benefit from the fine-tuning model is that it does not increase the complexity of the algorithm in the online phase. Overall, the best result is achieved by the combination of weighted sum and k-nearest neighbour with Minkowski distance weight calculation, together with the proposed fine-tuning. Its mean positioning error is 0.8740 m for Room 1 and 1.5385 for Room 2. The average computing time for a single online query is 0.4 ms for Room 1 and 0.7 ms for Room 2.

Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Nowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task. approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy. The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furhtermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm.

Design and Analysis of Collision Reduction Algorithms for LED-Based Indoor Positioning With Simulation and Experimental Validation

In this paper, we develop a low complexity indoor positioning system (IPS) and design a lightweight, low-cost, and wearable receiver for it. The accuracy of proximity-based LED IPS has been improved using overlap between LED beams but LED packets in the overlap region are subject to collisions. In this paper, we design collision handling algorithms for the IPS that considers building and lighting infrastructures. Mathematical analyses of the proposed algorithms are done and models for the probability of collisions are developed. The models, which are verified using simulations, are used to calculate the time required for position update called positioning time. Analysis of the positioning time is done for single and multiple receivers systems and validated with experimental measurements. Results show positioning error as low as 56 cm with a positioning time of about 300 ms for slotted unsynchronized systems and 500 ms for unslotted unsynchronized systems which makes the developed system pragmatic and appropriate for human positioning.

Indoor Positioning System Using Artificial Neural Network

Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS) has become very useful and popular in recent years. A number of Location Based Services (LBS) have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS) using Artificial Neural Networks (ANN), which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP), hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method for collecting the training data can help in addressing the noise and interference, which are one of the major factors affecting the accuracy of IPS.