2013
DOI: 10.1186/1687-1499-2013-276
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A simple iterative positioning algorithm for client node localization in WLANs

Abstract: The ability to determine in real-time the geographic location of client nodes is an important tool in wireless networks, allowing instantaneous mobile tracking, implementation of location-aware services and also efficient channel and power allocation planning. Among existing classical cooperative localization techniques for wireless networks, the maximum likelihood estimator (MLE) is theoretically the best. However, the gradient-based algorithms that are commonly used for maximum likelihood estimation are quit… Show more

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Cited by 3 publications
(8 citation statements)
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“…However, EBF and DBF algorithms can use any metric described in the literature. To estimate the distance to the sending node, the receiving node can use the RSSI (received signal strength indicator) [ 28 ].…”
Section: Simulation Resultsmentioning
confidence: 99%
“…However, EBF and DBF algorithms can use any metric described in the literature. To estimate the distance to the sending node, the receiving node can use the RSSI (received signal strength indicator) [ 28 ].…”
Section: Simulation Resultsmentioning
confidence: 99%
“…as our new observables [4] . While it would have been preferable to work with the original data from a theoretical standpoint, several considerations lead to the preference of time-averaged data, most notably: (1) comparison with other algorithms present in the literature, where the data model assumes only one sample per link, i.e.…”
Section: Time-averaged Rss Measuresmentioning
confidence: 97%
“…The wide spread of telecommunication systems has led to the pervasiveness of radiofrequency (RF) signals in almost every environment of daily life. Knowledge of the location of mobile devices is required or beneficial in many applications [1], and numerous localization techniques have been proposed over the years [1,2,3,4]. Techniques based on the received signal strength (RSS) are the preferred option when low cost, simplicity and technology obliviousness are the main requirements.…”
Section: Introductionmentioning
confidence: 99%
“…Noticing that, inward searches refine the localization result iteratively, which may be energy intensive [15,19]. A more accurate result demands a smaller increment of searching radius, which demands more search rounds and gives rise to more energy consumption of communication and computation [20].…”
Section: Inward Searchesmentioning
confidence: 99%
“…is used to determine the extent of power loss between the transmitting and receiving nodes [14,15]. In this formula, dBm represents the mean received power at the receiver node, dBm 0 represents the power measured at the reference distance 0 (usually 0 = 1 m), is a zero-mean Gaussian random variable caused by the shadowing, and is the path loss factor.…”
Section: Problem Formulationmentioning
confidence: 99%