FM-based indoor localization

Chen, Yin; Lymberopoulos, Dimitrios; Liu, Jie; Priyantha, Bodhi

doi:10.1145/2307636.2307653

Cited by 269 publications

(122 citation statements)

References 20 publications

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“…Most of these works use WiFi Receive Signal Strength (RSS) as the fingerprint [21], [22]. Recent works propose other forms of fingerprints such as FM Radio [9] and physical layer information Channel Frequency Response [23]. SurroundSense [10] generalizes the concept of fingerprint and explores ambient information such as noise, light color, etc.…”

Section: B Device-based Indoor Localizationmentioning

confidence: 99%

“…However, WiFi fingerprints are not tightly bound to our systems. Different fingerprints such as FM radio signal [9] or even ambient noise [10] can be associated with each step and used in the localization phase. Also, to improve the performance, other fingerprints such as indoor magnetic fingerprints can also be added into the system to provide more information.…”

Section: Extensions 1) Diverse Floor Plansmentioning

confidence: 99%

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PiLoc: A self-calibrating participatory indoor localization system

Luo

Hong

Chan

2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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Abstract-While location is one of the most important context information in mobile and ubiquitous computing, large-scale deployment of indoor localization system remains elusive.In this work, we propose PiLoc, an indoor localization system that utilizes opportunistically sensed data contributed by users. Our system does not require manual calibration, prior knowledge and infrastructure support. The key novelty of PiLoc is that it merges walking segments annotated with displacement and signal strength information from users to derive a map of walking paths annotated with radio signal strengths.We evaluate PiLoc over 4 different indoor areas. Evaluation shows that our system can achieve an average localization error of 1.5m.

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Section: B Device-based Indoor Localizationmentioning

confidence: 99%

Section: Extensions 1) Diverse Floor Plansmentioning

confidence: 99%

PiLoc: A self-calibrating participatory indoor localization system

Luo

Hong

Chan

2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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“…These are more or less available everywhere. The study presented in [19] indicates that a combination of FM and WiFi RSS measurements can be used to distinguish between different rooms with a high level of accuracy. This is collaborated by [20], which, however, also concludes that the RSS may vary significantly over time.…”

Section: Introductionmentioning

confidence: 99%

Indoor positioning using multi-frequency RSS with foot-mounted INS

Nilsson

Rantakokko

Skoglund

et al. 2014

2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Abstract-This paper presents a system which combines a zero-velocity-update-(ZUPT-)aided inertial navigation system (INS), using a foot-mounted inertial measurement unit (IMU), with opportunistic use of multi-frequency received signal strength (RSS) measurements. The system does not rely on maps or pre-collected data from surveys of the radio-frequency (RF) environment. Instead it builds its own database of collected RSS measurements during the course of the operation. New RSS measurements are continuously compared with the stored values in the database, and when the user returns to a previously visited area this can thus be detected. This enables loop-closures to be detected online and used for error drift correction. The system utilises a distributed particle simultaneous localization and mapping (DP-SLAM) algorithm which provides a flexible 2D navigation platform that can be extended with more sensors. The experimental results presented in this paper indicates that the developed RSS SLAM algorithm can, in many cases, significantly improve the positioning performance of a foot-mounted INS.

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“…Numerous RF-based approaches such as Wi-Fi [1], FM [2], [3], DVB-T [4], Bluetooth [5], ZigBee [6] and GSM [7] have already been studied, with each having its own advantages and disadvantages. Fingerprinting based approaches, especially using Wi-Fi, have been studied the most due to their lowcost, widespread availability of Wi-Fi infrastructure (especially in urban indoor scenarios), and good localisation accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous attempts have been made to automate the training phase using techniques such as crowdsourcing [12] and machine learning [13] but with limited success. Chen et al [2] showed that FM is a viable alternative to Wi-Fi signals, especially in indoor environments. FM signals when combined with Wi-Fi were shown to be complementary and to cancel each other's errors, improving the accuracy by up to 80%.…”

Section: Introductionmentioning

confidence: 99%

A smartphone-based indoor localisation system using FM and Wi-Fi signals

Mukhopadhyay

Rajput

Srirangarajan

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Indoor localisation has the potential to revolutionise the way people navigate indoors, similar to the tremendous impact that GPS has had on outdoor navigation. A number of solutions have been proposed for indoor localisation but most rely on specialised hardware or on the presence of a strong (access point) infrastructure. Many places do not have such infrastructure, thus limiting the use of these indoor localisation technologies. We propose a smartphone-based solution using FM and Wi-Fi signals that uses commercial off-the-shelf hardware which can be connected as and when required and thus addresses some of the potential privacy concerns. We show through our experiments that the proposed system can be used even in areas with low FM and Wi-Fi signal coverage. Our system achieves a mean localisation error of 2.84 m with a 90 th percentile error of 4.03 m. In addition, we show the robustness of our system in a realistic and challenging environment by using a 4 month old training database.

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FM-based indoor localization

Cited by 269 publications

References 20 publications

PiLoc: A self-calibrating participatory indoor localization system

PiLoc: A self-calibrating participatory indoor localization system

Indoor positioning using multi-frequency RSS with foot-mounted INS

A smartphone-based indoor localisation system using FM and Wi-Fi signals

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