On indoor position location with wireless LANs

Prasithsangaree, Phongsak; Krishnamurthy, P.; Chrysanthis, Panos K.

doi:10.1109/pimrc.2002.1047316

Cited by 304 publications

(169 citation statements)

References 4 publications

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“…Unpredictable variations may affect signal strength, produced by simple environmental changes and factors, i.e. the number of people present or moving around the building, the walls, floors, ceilings or other architectural constraints [11], or simply the position of the user when taking the measurement. Even a simple weather condition change, may cause significant variations [12].…”

Section: A -Main Principlementioning

confidence: 99%

Why is fingerprint-based indoor localization still so hard?

Viel

Asplund

2014

2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS)

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-Wireless indoor localization systems and especially signal strength fingerprinting techniques have been the subject of significant research efforts in the last decades. However, most of the proposed solutions require a costly site-survey to build the radio map which can be used to match radio signatures with specific locations. We investigate a novel indoor localization system that addresses the data collection problem by progressively and semi-autonomously creating a radio-map with limited interaction cost. Moreover, we investigate how spatiotemporal and hardware properties-based variations can affect the RSSI values collected and significantly influence the resulting localization. We show the impact of these fluctuations on our system and discuss possible mitigations.

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Section: A -Main Principlementioning

confidence: 99%

Why is fingerprint-based indoor localization still so hard?

Viel

Asplund

2014

2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS)

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“…Transmitter position estimation methods using TOA, TDOA, or AOA often provide erroneous results because of the first two limitations. Therefore, location fingerprinting approaches are the most promising approach for indoor applications (Prasithsangaree et al, 2002). Under LOS conditions, multipath fading can be addressed by identifying the first strong peak of the received signal.…”

Section: Introductionmentioning

confidence: 99%

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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“…With numerous new Wi-Fi nodes being added daily, the global Wi-Fi coverage is continuously growing. Recent tests have shown that indoor positioning with Wi-Fi RSS based Positioning Systems (WPS) can achieve positioning accuracy of 1 to 4 meter indoors and 10-40 m in the outdoor environment (Prasithsangaree et al, 2002;Doherty et al, 2001;Hightower and Borriello, 2001).…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning System Using Artificial Neural Network

Mehmood¹

2010

Journal of Computer Science

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Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS) has become very useful and popular in recent years. A number of Location Based Services (LBS) have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS) using Artificial Neural Networks (ANN), which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP), hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method for collecting the training data can help in addressing the noise and interference, which are one of the major factors affecting the accuracy of IPS.

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On indoor position location with wireless LANs

Cited by 304 publications

References 4 publications

Why is fingerprint-based indoor localization still so hard?

Why is fingerprint-based indoor localization still so hard?

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Indoor Positioning System Using Artificial Neural Network

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