An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model (original) (raw)
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RSSI-based distance estimation techniques for wireless indoor positioning system require extensive offline calibration to construct propagation model in order to describe the relationship between received signal strength and distance. This paper investigates the accuracy of the well-known propagation models against the measured data at indoor building. From the results, the dual slope model exhibits the best propagation model and it is chosen as the reference for further investigation. The accuracy of dual slope model in distance estimation suffers from the degradation due to the presence of Non Line of Sight (NLOS) condition between mobile station and access point. Therefore, to further improve the accuracy, this paper studies the effect of breakpoint distance and evaluates two simple techniques, running variance and kurtosis index, to identify the NLOS condition. Once the NLOS condition is identified, the best dual slope model can be selected for accurate distance estimation.
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Journal of Robotics and Control (JRC), 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 localization parameter from the ZigBee standard. We utilized the general path loss model to estimate the traveling distance between the transmitter (TX) and receiver (RX) based on the RSSI values. We conducted measurements in a simple indoor lobby environment to validate the performance of our proposed localization system. The results show that the min-max algorithm performs better accuracy compared to the trilateration, which yields an error distance of up to 3m. By these results, we conclude that the distance-based method using ZigBee standard working on 2.4 GHz center frequency can be reliable in the range of 1-3m. This small range is affected by the existence of interference objects (IOs) lead to signal multipath, causing the unreliability of RSSI values. These results can be the first step for building the indoor localization system, which low-cost, lowcomplexity, and can be applied in many fields, especially indoor robots and small devices in internet-of-things (IoT) world's today.
Short Range Indoor Distance Estimation by Using RSSI Metric
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In recent years, indoor localization problem is a highly preferred topic to study. In order to estimate the relative positions of the communication nodes in a localization system, the distance information of each node is required. In this paper, it is aimed to estimate the distances between the nodes by using the received signal strength (RSS) measurements. For an indoor environment, with the RSS based distance estimation methods, the short range distances between the nodes can be estimated by using the Received Signal Strength Indicator (RSSI) measurements taken over a standard wireless communication infrastructure, namely Wi-Fi.
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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...
RSSI Based Indoor Localization for Smartphone Using Fixed and Mobile Wireless Node
2013 IEEE 37th Annual Computer Software and Applications Conference, 2013
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.
Novel RSSI evaluation models for accurate indoor localization with sensor networks
2014 Twentieth National Conference on Communications (NCC), 2014
In a Wireless Sensor Network (WSN), it is often necessary to know the accurate location of one or more nodes. Various localization methods have been proposed, namely: TDOA, TOA, AOA, GPS and RSSI. All methods except RSSI require added hardware which increases the cost of the WSN. Moreover, the RSSI value of received radio signals is easily available on most sensor nodes. We propose a set of three techniques (mean+filter, mode, mode+filter) for evaluating RSSI for accurate indoor localization. The techniques are developed with the help of rigorous experimental data collected with TelosB nodes. Localization performance with all the models is compared with an existing technique, the mean technique. We assert that the mode-filter technique achieves a 64.86% less error over the mean technique in distance estimation between the transmitter and the receiver, and it has a 21.33% lower error in location estimation.