Wi-Fi Indoor Positioning System Based on RSSI Measurements from Wi-Fi Access Points –A Tri-lateration Approach (original) (raw)
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During this research about outdoor textit WiFi position is rarely explored, research conducted so far is searching textit WiFi position using GPS and GSM. In addition, research on textit indoor WiFi position uses the Trilateration method. In this journal, a textit WiFi position study uses the approved Triangulation method, compositions from textit Vincent Pierlot and textit Marc Van Droogenbroeck. Calculation method proved to be useful for searching for textit WiFi indoor and outdoor.
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Wireless network technology that is used today is developing rapidly because of the increasing need for location information of an object with high accuracy. Global Positioning System (GPS) is a technology to estimate the current location. Unfortunately, GPS has a disadvantage of low accuracy of 10 meters when used indoors. Therefore, it began to be developed with the concept of an indoor positioning system. This is a technology used to estimate the location of objects in a building by utilizing WSN (Wireless Sensor Network). The purpose of this study is to estimate the location of the unknown nodes in the lecturer room as an object and obtain the accuracy of the system being tested. The positioning process is based on the received signal strength (RSSI) on the unknown node using the ZigBee module. The trilateration method is used to estimate unknown node located at the observation area based on the signal strength received at the time of testing. The result shows that the path loss...
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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.
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This paper investigates the effectiveness and applicability of fusing three wireless positioning algorithms to determine the positions and track nomadic sensor nodes in real environment conditions. We fuse finger printing and atomic multilateration processes to give the system the best feasible region and to ensure that the later does not sway much due to accumulative errors. The extended Kalman filter is then used for refining the estimated position in near real time. The paper further assesses the response speed and the accuracy of estimating the position of the nomadic nodes with a prudent distribution of the processing load.
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In real life situations, location estimation of moving objects, armed personnel are of great importance. In this paper, we have attempted to locate targets which are mobile in a Wi-Fi environment. Radio Frequency (RF) localization techniques based on Received Signal Strength Indication (RSSI) algorithms are used. This study utilises Wireless Mon tool, software to provide complete technical information regarding received signal strength obtained from different wireless access points available in a campus Wi-Fi environment, considered for the study. All simulations have been done in MATLAB. The target location estimated by this approach agrees well with the actual GPS data.
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Various techniques that employ GPS signals such as have been introduced with the hope to provide a solution for indoor positioning detection. We proposed the implementation of trilateration technique to determine the position of users in indoor areas based on Wi-Fi signal strengths from access points (AP) within the indoor vicinity. In this paper, percentage of signal strengths obtained from Wi-Fi analyzer in a smartphone were converted into distance between users and each AP. A user's indoor position could then be determined using a formula proposed based on trilateration technique.
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Nowadays with the dispersion of wireless networks, smartphones and diverse related services, different localization techniques have been developed. Global Positioning System (GPS) has a high rate of accuracy for outdoor localization but the signal is not available inside of buildings. Also other existing methods for indoor localization have low accuracy. In addition, they use fixed infrastructure support. In this paper, we present a novel system for indoor localization, which also works well outside. We have developed a mathematical model for estimating location (distance and direction) of a mobile device using wireless technology. Our experimental results on Smartphones (Android and iOS) show good accuracy (an error less than 2.5 meters). We have also used our developed system in asset tracking and complex activity recognition. SECTION I. Introduction Man invented several methods and tools to identify their location a long time ago. Nowadays localization plays a very important role. Various location based services (LBS) has been developed using global positioning system (GPS) for outdoor environment. There are lots of applications where localization is used extensively such as navigation, map generation, complex activity recognition, patient identification, location and tracking in hospitals, child tracking, disaster management, monitoring firefighters, indoor and outdoor navigation for humans or mobile robots, inventory control in factories, anomaly detection, customer interest observation in supermarkets, visitors interest observation in exhibitions, and smart houses [1] [2] [3] [4] [5]. These applications of localization help to solve and improve a variety of real-life problems.
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Existing researches on location tracking focus either entirely on indoor or entirely on outdoor by using different devices and techniques. Several solutions have been proposed to adopt a single location sensing technology that fits in both situations. This paper aims to track a user position in both indoor and outdoor environments by using a single wireless device with minimal tracking error. RSSI (Received Signal Strength Indication) technique together with enhancement algorithms is proposed to cater this solution. The proposed RSSI-based tracking technique is divided into two main phases, namely the calibration of RSSI coefficients (deterministic phase) and the distance along with position estimation of user location by iterative trilateration (probabilistic phase). A low complexity RSSI smoothing algorithm is implemented to minimize the dynamic fluctuation of radio signal received from each reference node when the target node is moving. Experiment measurements are carried out to analyze the sensitivity of RSSI. The results reveal the feasibility of these algorithms in designing a more accurate real-time position monitoring system.
An Enhanced Indoor Positioning Method Based on Wi-Fi RSS Fingerprinting
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In WiFi-based indoor positioning, the received signal strength (RSS) measurements are commonly used to estimate the mobile user location. However, these measurements significantly fluctuate over time and are susceptible to human movement, multipath and Non-Line-of-Sight (NLOS) propagation, which reduce the location accuracy. In this paper, an enhanced positioning method based on the nearest neighbor algorithm is proposed. The set of the RSS samples recorded from several Access Points (APs) is used rather than their average, for reducing the location errors introduced by the RSS variations and the multipath problem. The proposed algorithm, named the Nearest K th Nearest Neighbor (NK-NN) is experimentally evaluated and compared to other powerful methods. The results show that the proposed method outperforms these methods.