A multi-building WiFi-based indoor positioning system

Górski, Konrad; Groth, Mateusz; Kulas, Łukasz

doi:10.1109/mikon.2014.6899970

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…In the simplest version, to determine the position, the distance is calculated for every reference point and the estimated position is the reference position for which the Euclidean distance has the lowest value. A number of improvements to this method have been presented, such as the arithmetic mean distance from K nearest neighbors (KNN) [ 32 ] and the weighted mean distance (WKNN).…”

Section: Related Workmentioning

confidence: 99%

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Groth

Nyka

Kulas

2021

Sensors

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In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.

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Section: Related Workmentioning

confidence: 99%

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Groth

Nyka

Kulas

2021

Sensors

Self Cite

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“…The filtering algorithms were able to construct the real-time database and compensate for the cumulative positioning error and then it can also remove noise measurements. Bayesian filter [242][243][244], Kalman filter (KF) and extended Kalman filter (EKF) [245,246], and particle filter (PF) [247][248][249][250] have been implemented by integrating WLAN-based indoor localization determination techniques. The filtering process may aid in obtaining a continuous trajectory and decrease the estimation error.…”

Section: Filtering Approachmentioning

confidence: 99%

Review of Indoor Positioning: Radio Wave Technology

et al. 2020

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The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

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“…In offline training phase, the received Wi-Fi RSSI from surround environment is selected as the classification feature. Then, online positioning usually uses a classification model for positioning, such as KNN [8], neural network [25], and Adaboost [26]. In addition, several methods have been proposed instead of traditional classification model for reducing computational burden [27,28] or improving positioning accuracy [9].…”

Section: Related Workmentioning

confidence: 99%

“…Previous positioning methods using Wi-Fi technology are based on geometric properties of triangles [6,7], which evaluated the location of unknown point with three known reference points. In general, the Wi-Fi network access points (APs) usually work as reference point and three methods can be used to evaluate the distance between unknown point and reference point, namely, received signal strength indicator (RSSI) [8,9], angle of arrival [10], and time of arrival [11]. However, positioning methods using angle or time of arrival need special hardware to accurately measure the arrival time or angle, which is impractical for common devices.…”

Section: Introductionmentioning

confidence: 99%

Feature Learning for Fingerprint-Based Positioning in Indoor Environment

Zheng

Chen

et al. 2015

International Journal of Distributed Sensor Networks

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Recent years have witnessed a growing interest in using Wi-Fi received signal strength for indoor fingerprint-based positioning. However, previous study about this problem has primarily faced two main challenges. One is that positioning fingerprint feature using received signal strength is unstable due to heterogeneous devices and dynamic environment status, which will greatly degrade the positioning accuracy. Another is that some improved positioning fingerprint features will suffer the curse of dimensionality in online positioning. In this paper, we designed a novel positioning fingerprint feature using the segment similarity of Wi-Fi access points, considering both the received signal strength value and the Wi-Fi access point. Based on this designed fingerprint feature, we proposed a two-stage positioning algorithm for indoor fingerprint-based positioning. Experiment results indicate that our proposed positioning methodology can not only achieve better positioning performance but also consume less positioning time compared to three baseline methods.

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A multi-building WiFi-based indoor positioning system

Cited by 6 publications

References 5 publications

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Review of Indoor Positioning: Radio Wave Technology

Feature Learning for Fingerprint-Based Positioning in Indoor Environment

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