Optimum sensor placement for source localization and monitoring from received signal strength
The problem of source localization has become increasingly important in recent years. In source localization, we are interested in estimating the location of a source using various relative position information. This research considers source localization using relative position information provided...
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| Format: | Others |
| Language: | English |
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University of Iowa
2010
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| Online Access: | https://ir.uiowa.edu/etd/822 https://ir.uiowa.edu/cgi/viewcontent.cgi?article=2007&context=etd |
| Summary: | The problem of source localization has become increasingly important in recent years. In source localization, we are interested in estimating the location of a source using various relative position information. This research considers source localization using relative position information provided by Received Signal Strength (RSS) values under the assumption of log-normal shadowing. We investigate an important aspect of source localization, namely, that of optimally placing sensors. Two specific issues are investigated. The first is one of source monitoring. In this, one must place sensors around a localized source in an optimum fashion subject to the constraint that sensors are at least a certain distance from the source. The second is sensor placement for source localization. In this problem, we assume that the source is uniformly distributed in a circular region. The sensors must be placed in the complement of a larger concentric circle, to optimally localize the source.
The monitoring problem is considered in N-dimensions. The localization problem is in 2-dimensions. The technical problem becomes one of investigating the underlying Fisher Information Matrix (FIM) for optimal monitoring and its expectation for optimal localization. The underlying problem then becomes one of placing sensors to maximize the determinant or the minimum eigenvalue of FIM (or its expectation) or minimize the trace of the inverse of the FIM (or its expectation). |
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