Improved Indoor Location Systems in a Controlled Environments (original) (raw)
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A new method for improving Wi-Fi-based indoor positioning accuracy
Wi-Fi and smartphone based positioning technologies are play-ing a more and more important role in Location Based Service (LBS) industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To ad-dress this problem, a new method for improving the indoor posi-tioning accuracy was developed. The new method initially used the Nearest Neighbor (NN) algorithm of the fingerprinting meth-od to identify the initial position estimate of the smartphone us-er. Then two distance correction values in two roughly perpen-dicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.
Development of A Wi-Fi Based Indoor Location System Using Artificial Intelligence Techniques
2020
The main aim of this study is to resolve the problem of indoor positioning in closed areas, which has become a growing need nowadays, by using existing hardwaresolutions. Although the use of the GPS system, which requires satellite communication as an open space location solution, is very common, it cannot provide a solution for indoor. It is a well-known metric to measure signal strengths to determine distances between wireless nodes. However, the signal strength is affected by many external influences and causes erroneous measurements. With the developed approach, the transmission powers of the signals received from more than one transmitter located within a certain closed area are measured and given as an input to an artificial neural network. It has been seen that the outputs produced by the trained neural network are much more successful and reliable than the path-loss calculation.
Fuzzy Logic Based Compensated Wi-Fi Signal Strength for Indoor Positioning
2013 International Conference on Advanced Computer Science Applications and Technologies, 2013
Work in indoor positioning so far broadly relies on either signal propagation models or location fingerprinting. The former approach has inherent modelling complexity as a result of intervening walls and movement in the environment which, impacts the accuracy of such models. The latter approach on the other hand, is acclaimed to give better accuracy. However, for it to be used, an added overhead of surveying history data of a calibration of every indoor environment is required. Moreover, if any of the mobile Access Points (APs) included in the surveyed history data is down for any reason, the result of the location fingerprinting approach is impacted. This work proposes an indoor location determination approach that uses Fuzzy Weighted Aggregation of Received Signal Strengths (RSS) of Wi-Fi signals with Compensated Weighted Attenuation Factor (CWAF) in the form of fuzzy weighted signal quality and noise. The results are compared with locations away from APs with actual physical measurement in the environmental location to verify accuracy. The performance of the proposed algorithm shows that if the normalized weighted signal strength is properly compensated with weighted signal quality and noise, the approach offers a more computationally efficient positioning with adequate accuracy for indoor localization.
Intelligent Techniques applied to WiFi Localization Systems
2009
The goal of this paper is to study some of the most important WiFi signal variations, large and small scale variations. Moreover, the paper shows how to use intelligent techniques to deal with this uncertainties. The paper shows how to take adventage of neural networks to estimate a propagation model, and how to use this model to calculate distances. This work describes how to reduce uncertainty produced by small scale in indoor environments using fuzzy techniques. Some experimental results and conclusions are presented.
Positioning is the most attractive technology today. Various technologies are used now days for positioning purpose. GPS is mainly used for outdoor environment. Non-suitability of GPS in indoor conditions because of its NLOS conditions and signal attenuation has lead to several other techniques of indoor positioning. This paper compares few indoor positioning methods and proposes indoor positioning system using tri-lateration method which uses RSSI data from wi-fi access points to do localization in indoor environment.
The work presented here compares the performance of indoor positioning systems suitable for low power wireless sensor networks. Map matching, approximate positioning (weighted centroid) and exact positioning algorithms (least squares) were tested and compared in a small predefined indoor environment. We found that, for our test scenario, weighted centroid algorithms provided the best results. Least squares proved to be completely unreliable when using distances obtained by a propagation model. Major improvements in the positioning error were found when body influence was removed from the test scenario.
Sensors, 2021
Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are ...
Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016), 2017
we present Wireless Indoor Localization System (WILS) that enables commercial WiFi infrastructures to implement localization in the complex indoor environment. Receive Signal Strength Indication (RSSI) is utilized by some systems to get a high accuracy of localization, but they are easily be affected by the environment. Other use modified hardware or firmware, such as increasing the number of antennas to obtain enormous location precision. To solve this problem, WILS uses physical layer information, called Channel State Information (CSI). And it does not require modification in any hardware and firmware. WILS makes four technical contributions for achieving a high accuracy in localization. At first, it proposes a new algorithm, called Modified MUSIC (M-MUSIC), and it can estimate Angle of Arrival (AoA) and Time of Flight (ToF) at the same time from CSI information. Next, it introduces some methods to sanitize the phase, and improve the accuracy of measured phase. Third, it presents an idea of window slides to do CSI Smoothing for CSI measurements. Finally, it utilizes clustering algorithm to identify Line of Sight (LoS) path, and combines the AoA for LoS path and RSSI to locate the target. Our implementation on commodity WiFi cards demonstrates that WILS's accuracy is comparable to advanced localization systems, it can achieve a median accuracy of 60 cm. Keywords-indoor localization; channel state information (CSI); angle of arrival (AoA); time of flight (ToF); modified MUSIC, line of sight(LoS); phase sanitization I.
Distance-based Indoor Localization System Utilizing General Path Loss Model and RSSI
2020
Wireless sensor networks (WSNs) have a vital role in indoor localization development. As today, there are more demands in location-based service (LBS), mainly indoor environments, which put the researches on indoor localization massive attention. As the global-positioning-system (GPS) is unreliable indoor, some methods in WSNs-based indoor localization have been developed. Path loss model-based can be useful for providing the power-distance relationship the distance-based indoor localization. Received signal strength indicator (RSSI) has been commonly utilized and proven to be a reliable yet straightforward metric in the distance-based method. We face issues related to the complexity of indoor localization to be deployed in a real situation. Hence, it motivates us to propose a simple yet having acceptable accuracy results. In this research, we applied the standard distance-based methods, which are is trilateration and min-max or bounding box algorithm. We used the RSSI values as the...