Indoor Location Fingerprinting Using FM Radio Signals

Moghtadaiee, Vahideh; Dempstera, Andrew G.

doi:10.1109/tbc.2014.2322771

Cited by 71 publications

(50 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are a number of studies on this subject, using wireless technologies such as Wi-Fi, Bluetooth, FM, etc. Some systems use a combination of different signal types, such as FM and Wi-Fi [18], Wi-Fi and Bluetooth [21], or Wi-Fi and GSM [22]. Other approaches [23] [24] combine Wi-Fi signals with dead reckoning methods, using inertial and magnetic field sensors.…”

Section: ) Fingerprintingmentioning

confidence: 99%

“…Among these techniques, fingerprint-based methods make use of identifiers (e.g. BSSID for Wi-Fi signals) of signal sources present in the environment (such as GSM [2] [3] [9], Wi-Fi [7] [8] [10] [12] [16], Bluetooth [13] [14] [15] and FM radio [11] [17] [18] [19]), and signal strength values. This information is recorded at different locations in the environment and stored in a dataset together with the corresponding locations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilization of room-to-room transition time in Wi-Fi fingerprint-based indoor localization

Karabey

Bayındır

2015

2015 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

In indoor localization applications, many different methods have been proposed to increase positioning accuracy. Among these methods, fingerprint-based techniques are generally preferred because they use existing resources such as Wi-Fi, Bluetooth, FM signals, etc., and can be implemented on commonly used devices such as mobile phones. In this paper, we evaluate different Wi-Fi fingerprint-based methods on two datasets (with and without room-to-room transition features) created from the same environment, and we investigate the impact of room-to-room transition features on classification performance. To the best of our knowledge, transition time between rooms has not been used in past studies on fingerprint-based indoor localization. This information is of significant importance, due to the physical distance between rooms. Therefore, in this study source room and transition time to a target room have been included as features in addition to signal sources and signal strength values in the target room. From preliminary experimental results we observed that the transition time between rooms increases the performance of all tested positioning algorithms, with the Back-propagation classifier showing the best performance increase (13%).

show abstract

Section: ) Fingerprintingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utilization of room-to-room transition time in Wi-Fi fingerprint-based indoor localization

Karabey

Bayındır

2015

2015 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

show abstract

“…Most of the SoOP navigation uses signals with stable properties that are well known a priori. Examples of electromagnetic spectrum-based SoOP include AM/FM radio, [9][10][11] cellular phone, [12][13][14][15] digital television, [16][17][18] iridium 19 or other communication satellites, 20 WiFi, [21][22][23][24] VLF signals, 25,26 or combinations of signals. [27][28][29] These SoOP approaches make use of a priori information about their signals in order to derive navigation information.…”

Section: Introductionmentioning

confidence: 99%

Navigation using VLF signals with artificial neural networks

2018

View full text Add to dashboard Cite

This examines the use of very low frequency (VLF) electromagnetic signals with artificial neural networks (ANN) to estimate two‐dimensional location. The VLF source positions and type of signals are not known in advance. VLF signals were collected from the environment using a mobile antenna while the user manually tagged the true location. Data were collected over several months. The signals were divided into time segments, and features were generated for the ANN from the spectral power of the signals over time. The ANN uses long short‐term memory (LSTM) layers and fully connected layers and was trained using the time‐series spectral features of the VLF signal and the true location data. The position estimates are an indoor locale‐classification problem. Our task was to determine which of 50 rooms or hallway segments the sensor was in for a given building. The system achieved an average accuracy over 76% in determining discrete rooms or hallways. The approach achieved these exceptional accuracies with very little a priori knowledge of the VLF signals using only supervised learning.

show abstract

“…However, its performance is very poor in indoor environments [3]. Therefore, different types of indoor positioning systems have been developed for personal and commercial needs [4,5]. Among all of the indoor positioning methods, Benavente-Peces et al [6] found that Radio Frequency Identification (RFID) is the best choice for indoor positioning with high accuracy and low cost.…”

Section: Introductionmentioning

confidence: 99%

Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine

Wang

Shi

2017

Symmetry

View full text Add to dashboard Cite

Nowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme learning machine (ELM) is proposed to address the problem of inaccurate indoor positioning when significant Received Signal Strength Indication (RSSI) fluctuations happen during the measurement process. The Gaussian filtering method is analyzed and compared, which can effectively filter out the fluctuant signals that were caused by the environment effects in an RFID-based positioning system. Meanwhile, the fast learning ability of the proposed ELM algorithm can reduce the time consumption for the offline and online service, and establishes the network positioning regression model between the signal strengths of the tags and their corresponding positions. The proposed positioning system is tested in a real experimental environment. In addition, system test results demonstrate that the positioning algorithms can not only provide higher positioning accuracy, but also achieve a faster computational efficiency compared with other previous algorithms.

show abstract

Indoor Location Fingerprinting Using FM Radio Signals

Cited by 71 publications

References 23 publications

Utilization of room-to-room transition time in Wi-Fi fingerprint-based indoor localization

Utilization of room-to-room transition time in Wi-Fi fingerprint-based indoor localization

Navigation using VLF signals with artificial neural networks

Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine

Contact Info

Product

Resources

About