
For FullText PDF, please login, if you are a member of IEICE,
or go to Pay Per View on menu list, if you are a nonmember of IEICE.

Distance Estimation Based on Statistical Models of Received Signal Strength
Masahiro FUJII Yuma HIROTA Hiroyuki HATANO Atsushi ITO Yu WATANABE
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E99A
No.1
pp.199203 Publication Date: 2016/01/01 Online ISSN: 17451337
DOI: 10.1587/transfun.E99.A.199 Type of Manuscript: Special Section LETTER (Special Section on Wideband Systems) Category: Keyword: distance estimation, received signal strength, statistical model, mean square error,
Full Text: PDF(1MB)>>
Summary:
In this letter, we propose a new distance estimation method based on statistical models of a Received Signal Strength (RSS) at the receiver. The conventional distance estimator estimates the distance between the transmitter and the receiver based on the statistical average of the RSS when the receiver obtains instantaneous RSS and an estimate of the hyperparameters which consists of the path loss exponent and so on. However, it is wellknown that instantaneous RSS does not always correspond to the average RSS because the RSS varies in accordance with a statistical model. Although the statistical model has been introduced for the hyperparameters estimation and the localization system, the conventional distance estimator has not yet utilized it. We introduce the statistical model to the distance estimator whose expected value of the estimate corresponds to true distance. Our theoretical analysis establishes that the proposed distance estimator is preferable to the conventional one in order to improve accuracy in the expected value of the distance estimate. Moreover, we evaluate the Mean Square Error (MSE) between true distance and the estimate. We provide evidence that the MSE is always proportional to the square of the distance if the estimate of the hyperparameters is ideally obtained.

