Abstract
In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches.
Highlights
The search of accurate wireless localization algorithms, which are a key enabler technology of emerging Location Based Services (LBS) has been receiving enormous attention
Utilizing both Wireless Local Area Network (WLAN) access points and Wireless Sensor Networks (WSNs) could leverage the limitations of any of the technologies while keeping their points of strength and in the end to improve the localization accuracy. While none of these technologies are a dominant solution for indoor positioning, the signal stability of the WiFi system could overcome the less stable signals which are typical of the WSN network and while the latter is less subjected to fading effects
The training of the Multilayer Perceptron (MLP) network was repeated an extensive amount of times, in order to observe its behavior and to record the typical parameters corresponding to minima in the value of the error of the testing data
Summary
The search of accurate wireless localization algorithms, which are a key enabler technology of emerging Location Based Services (LBS) has been receiving enormous attention. GPS in particular becomes ineffective for indoor localization purposes.[2] To overcome this limitation, Indoor Positioning Systems (IPS) are proposed by using single, two or more wireless technologies in combination.[3] The wireless-based localization is still affected by fading effects, which are caused by the indoor environment obstacles such as walls and partitions. These irregularities present in any indoor environment are among the major cause of positioning errors.[4] Keeping in view of these fading effects, hybrid indoor positioning systems have been proposed in order to achieve improved location accuracy as well as availability, by using the advantages and abilities of the dual technologies.[5] Due
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