A Novel System for WiFi Radio Map Automatic Adaptation and Indoor Positioning

Tao, Ye; Zhao, Long

doi:10.1109/tvt.2018.2867065

Cited by 80 publications

(36 citation statements)

References 27 publications

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“…The accuracy of centroid positioning relies heavily on the density and distribution of anchor nodes [13][14][15]. The positioning is very accurate when the nodes are dense and uniformly distributed.…”

Section: Initial Positioning Based On Weighted Centroid Algorithmmentioning

confidence: 99%

A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

Ren¹,

Huang²,

Song³

et al. 2019

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Considering the high accuracy needed for indoor positioning, this paper develops a novel indoor positioning algorithm for the wireless sensor network (WSN) in the following steps. First, the RSSIs of the network nodes were sampled and analyzed, and the excess errors were filtered to enhance positioning accuracy. Next, the initial position was iteratively obtained by the weighted centroid algorithm, and a correction matrix was developed to improve the Taylor series expansion (TSE), and the final position was determined through improved TSE iteration. The proposed positioning method was verified through simulation.

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Section: Initial Positioning Based On Weighted Centroid Algorithmmentioning

confidence: 99%

A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

Ren¹,

Huang²,

Song³

et al. 2019

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“…The main approach in an active beaconing method for indoor Wi-Fi localization is through Wi-Fi fingerprinting [5], [7]- [14]. Wi-Fi fingerprinting, however, suffers from two main shortcomings: 1) The process of associating the Wi-Fi signal strength to every given location within the environment of interest, which can be complex and time consuming [11]:…”

Section: A Related Workmentioning

confidence: 99%

“…Although the results are promising, little information is provided on how much reduction in overhead is achieved by using the algorithm described in [11]. Moreover, the approach in [11] can be power hungry since it requires the constant monitoring of data from a large number of access points. In fact, due to this overhead and complexity, new approaches are focusing on applying deep learning methodologies to reduce the overhead associated with Wi-Fi fingerprinting while also increasing accuracy [14].…”

Section: A Related Workmentioning

confidence: 99%

Hybrid Localization: A Low Cost, Low Complexity Approach Based on Wi-Fi and Odometry

Moro¹,

Mehrpouyan

2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

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Localization in indoor environments is essential to further support automation in a wide array of scenarios. Moreover, direction-of-arrival knowledge is essential to supporting high speed millimeter-wave (mmWave) links in indoor environments, since most mmWave links are of a line-of-sight nature to combat the high pathloss in this band. Accurate wireless localization in indoor environments, however, has proved a challenging task due to multi-path fading. Additionally, due to the effects of multi-path fading, methods such as trilateration alone do not result in accurate localization. As such, in this paper we propose to combine the knowledge of wireless localization methods with that of odometry sensors to track the location of a mobile robot. This paper presents significant real-world localization measurement results for both Wi-Fi and odometry in diverse environments at the Boise State University campus. Using these results, we devise an algorithm to combine data from both odometry and wireless localization. This algorithm is shown in hardware testing to reduce the localization error for a mobile robot.

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“…In the offline phase, a site survey is conducted to collect the value of the received signal strength indicator (RSSI) at many reference points (RPs) from all the detected access points (APs). Some researchers have proposed using indoor positioning technologies that do not require the construction of offline fingerprint maps [36] or just by updating the maps automatically [37]. In the online phase, a user samples or measures an RSSI vector at his/her position.…”

Section: Introductionmentioning

confidence: 99%

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Firdaus

Ahmad

Sahibuddin

2020

Int. j. electr. comput. eng. syst. (Online)

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Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem.

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A Novel System for WiFi Radio Map Automatic Adaptation and Indoor Positioning

Cited by 80 publications

References 27 publications

A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

Hybrid Localization: A Low Cost, Low Complexity Approach Based on Wi-Fi and Odometry

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

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