NDLI: Optimising sensor placement for site-specific RSSD geolocation systems

Content Provider	IEEE Xplore Digital Library
Author	Hindmarch, I. Beach, M. Nix, A.R. Thomas, P.
Copyright Year	2015
Description	Author affiliation: Commun. Syst. & Networks Group, Univ. of Bristol, Bristol, UK (Hindmarch, I.; Beach, M.; Nix, A.R.; Thomas, P.)
Abstract	Geolocation of wireless emitters using Received Signal Strength Difference (RSSD) and site-specific propagation models offers the advantages of increased accuracy and operation with non-cooperative emitters (where emitted power level or positional information such as GPS from the emitter is unavailable) over more commonplace Received Signal Strength (RSS) systems. An important factor influencing the accuracy of such systems is the physical arrangement of the sensors, making optimising the layout of sensors prior to deployment highly desirable. This paper introduces the novel Uniqueness metric for evaluating receiver layouts based on inter-point RSSD with a site-specific propagation model. Two optimization techniques (Monte Carlo optimization and an implementation of a genetic algorithm) are applied to the metric and evaluated in a case study that incorporates two different propagation environments (urban and rural) compared against an existing dilution of precision based metric. The results of the simulation demonstrate that the uniqueness metric combined with the genetic algorithm provide the best overall performance in both environments tested.
Starting Page	1
Ending Page	5
File Size	1946713
Page Count	5
File Format	PDF
e-ISBN	9781479989430
DOI	10.1109/LAPC.2015.7366108
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-02
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Received Signal Strength Difference (RSSD) Geology Geolocation Buildings sensor placement Receivers Prediction algorithms Localization Optimization Genetic algorithms
Content Type	Text
Resource Type	Article

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