Abstract | ||
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The problem is to estimate the number of radioactive point sources in a specified area and to estimate their parameters (locations and magnitudes), using measurements collected by a low-cost Geiger-Muller counter. The measurements are Poisson distributed with the mean proportional to the radiation field intensity. The radiation field represents a superposition of background radiation and the source contributions subjected to the inverse distance squared attenuation. The solution is based on an information gain driven search which comprises a sequential Bayesian estimator coupled with a sensor/observer control unit. The control unit directs the observer(s) to move to new locations and acquire measurements that maximise the information gain in the Renyi divergence sense. The performance of the proposed information driven search, including a comparison with a unform search along a predefined path, is studied by simulations. A successful application of the proposed technique to experimental datasets, recently collected in the field trials, verifies the measurement model and the theoretical considerations. |
Year | DOI | Venue |
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2010 | 10.1016/j.sigpro.2009.10.006 | Signal Processing |
Keywords | Field | DocType |
radiation field,point source,sequential monte carlo estimation,gamma radiation,information gain,control unit,radioactive sources,particle filter,field trial,background radiation,proposed information,proposed technique,unform search,sensor management,observer control unit,radiation field intensity,nuclear search,sequential monte carlo,poisson distribution | Monte Carlo method,Control theory,Particle filter,Point source,Control unit,Background radiation,Poisson distribution,Observer (quantum physics),Sequential estimation,Mathematics | Journal |
Volume | Issue | ISSN |
90 | 4 | Signal Processing |
Citations | PageRank | References |
18 | 1.23 | 13 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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Branko Ristic | 1 | 711 | 62.37 |
Mark Morelande | 2 | 25 | 2.33 |
Ajith Gunatilaka | 3 | 59 | 6.91 |