Open Source GNSS Reference Server for Assisted-Global Navigation Satellite Systems

Li, Binghao; Zhang, Jiahuang; Dempster, Andrew G.; Rizos, Chris

doi:10.1017/s037346331000038x

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…For outdoor positioning, the Global Satellite Systems (GNSSs) such as American Global Positioning System (GPS) and Chinese Beidou Satellite Navigation System are performing very well. However, GNSSs are not suitable for indoor positioning as the satellite signal propagation can be easily interfered by indoor complex environment [1]. Therefore, other positioning solutions should be implemented for indoor positioning and navigation.…”

Section: Introductionmentioning

confidence: 99%

Combination of DNN and Improved KNN for Indoor Location Fingerprinting

Dai

Yang

Wang

et al. 2019

Wireless Communications and Mobile Computing

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Fingerprinting based on Wi-Fi Received Signal Strength Indicator (RSSI) has been widely studied in recent years for indoor localization. While current algorithms related to RSSI Fingerprinting show a much lower accuracy than multilateration based on time of arrival or the angle of arrival techniques, they highly depend on the number of access points (APs) and fingerprinting training phase. In this paper, we present an integrated method by combining the deep neural network (DNN) with improved K-Nearest Neighbor (KNN) algorithm for indoor location fingerprinting. The improved KNN is realized by boosting the weights on K-nearest neighbors according to the number of matching access points. This will overcome the limitation of the original KNN algorithm on ignoring the influence of the neighboring points, which directly affect localization accuracy. The DNN algorithm is first used to classify the Wi-Fi RSSI Fingerprinting dataset. Then these possible locations in a certain class are also classified by the improved KNN algorithm to determine the final position. The proposed method is validated inside a room within about 13⁎9 m2. To examine its performance, the presented method has been compared with some classical algorithms, i.e., the random forest (RF) based algorithm, the KNN based algorithm, the support vector machine (SVM) based algorithm, the decision tree (DT) based algorithm, etc. Our real-world experiment results indicate that the proposed method is less dependent on the dense of access points and indoor radio propagation interference. Furthermore, our method can provide some preliminary guidelines for the design of indoor Wi-Fi test bed.

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Section: Introductionmentioning

confidence: 99%

Combination of DNN and Improved KNN for Indoor Location Fingerprinting

Dai

Yang

Wang

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…For outdoor environments, a Global Navigation Satellite System (GNSS) such as Global Positioning System (GPS) is ideal. However, GPS is not suitable for indoor environments as the satellite signals cannot penetrate walls or roof of buildings [3,4]. Furthermore, when assisted-GPS techniques are used, the position may have errors of tens of meters [5].…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm to Generate a Wi-Fi Fingerprint Database for Indoor Positioning

Chen

Zhao

et al. 2013

Sensors

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The major problem of Wi-Fi fingerprint-based positioning technology is the signal strength fingerprint database creation and maintenance. The significant temporal variation of received signal strength (RSS) is the main factor responsible for the positioning error. A probabilistic approach can be used, but the RSS distribution is required. The Gaussian distribution or an empirically-derived distribution (histogram) is typically used. However, these distributions are either not always correct or require a large amount of data for each reference point. Double peaks of the RSS distribution have been observed in experiments at some reference points. In this paper a new algorithm based on an improved double-peak Gaussian distribution is proposed. Kurtosis testing is used to decide if this new distribution, or the normal Gaussian distribution, should be applied. Test results show that the proposed algorithm can significantly improve the positioning accuracy, as well as reduce the workload of the off-line data training phase.

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“…Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) provides accurate outdoor positioning solution (several metres accuracy); however, GPS does not have comparable indoor positioning accuracy as the signal from GPS satellites cannot penetrate walls of buildings [1]. Thus, research on indoor positioning is carried out to acknowledge and alleviate the problems regarding conventional method of positioning, Indoor positioning methods are extensively researched and highly on demand.…”

Section: Introductionmentioning

confidence: 99%

A new method to generate and maintain a WiFi fingerprinting database automatically by using RFID

Gunawan

Gallagher

et al. 2012

2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Location fingerprinting in WiFi positioning has been widely used in indoor environments. The key issue of the fingerprinting technology is the fingerprint database. The disadvantages of this technology are the database generation and maintenance requirements. The conventional method to create the database is that people carry out the survey manually (that is what the commercial products are doing). When the environment changes significantly (such as after a building renovation, or moving of furniture), the database has to be rebuilt. This paper proposes a new method to build and maintain the database in an efficient manner. This method only requires persons to carry a specific device which consists of a Radio Frequency Identification (RFID) reader and WiFi scanner to log the coordinates and the WiFi signal strengths. The coordinates are provided by some pre deployed medium range (1-2 meters) RFID tags in the building with a location technique based on 'cell ID'. As the persons conducting the survey are moving around the area of interest forpurposes other than the fingerprint survey (such as a security guard who regularly patrols the whole building anyway), so the fingerprint database can be generated "automatically". Also, the database can be refined as the data are being accumulated. When the environment changes, it can be detected by the self-refining database. A preliminary test was carried out for the proposed method. The results show that it works well.

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Open Source GNSS Reference Server for Assisted-Global Navigation Satellite Systems

Cited by 9 publications

References 7 publications

Combination of DNN and Improved KNN for Indoor Location Fingerprinting

Combination of DNN and Improved KNN for Indoor Location Fingerprinting

An Improved Algorithm to Generate a Wi-Fi Fingerprint Database for Indoor Positioning

A new method to generate and maintain a WiFi fingerprinting database automatically by using RFID

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