A source-attractor approach to network detection of radiation sources

Wu, Chase Q.; Berry, Mark L.; Grieme, Kayla M.; Sen, Satyabrata; Rao, Nageswara S. V.; Brooks, Richard; Cordone, Guthrie

doi:10.1109/mfi.2016.7849520

Cited by 1 publication

(2 citation statements)

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“…Benefitting from short latency, small size and low prices, non-directional radiation detectors possess wider applications than directional types, in aspects of sensor network monitoring and robot exploration missions [7]- [11]. Based on non-directional sensors, various formulations have been studied extensively for the multi-source localization problem [11]- [20]. A commonly utilized method is to establish complex probabilistic models for multiple sources, then Expectation Maximization (EM) or Maximum Likelihood Estimation (MLE) can be employed for parameter optimization [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

“…As referenced in [19]- [23], the multi-source identification problem can also be completed through the strength mapping procedure. In literature [19] and [20], exploring region is directly divided into a large quantity of grid cells, and dense sampling is carried out for individual grid. Then the mapping methods are proceeded by data aggregation and spatial correlation techniques, e.g., Gaussian Processes and kernel functions.…”

Section: Introductionmentioning

confidence: 99%

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Improved Dynamic Optimization of PSPF-Based Sources Estimation in Local Multi-Modal Radiation Field

et al. 2019

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This paper presents a novel Gaussian Processes-Peak Suppression Particle Filter (GP-PSPF) method with adaptive weighting corrections, so as to identify sources in the multi-modal radiation field under some tough conditions, e.g. spatially sparse measurements and sources with large strength differences. As the radiation cumulative effect and ambiguous source number, most existing methods fail to localize the hotspots clustered in narrow regions, and PSPF scheme overcomes these difficulties through multilayer structure and peak-suppressed correction. In contrast to our earlier work, the proposed algorithm mainly focuses on more severe and practicable conditions, as well as accuracy and robustness improvement. Firstly measurement biases are adopted as the correction feedback through Gaussian Processes technique, and then strength deviation for each particle can be inferred and utilized in two dynamic modules. The dynamic peak-suppressed correction is implemented to achieve more accurate estimations, while the location correction focuses on the solution of location dilemmas, consisting of redundant source identification and less swarm clustering. In addition, scaling adaptation policy and sequential swarm reordering are specially conceived and developed for more stable and accurate optimization. Finally, extensive simulations and physical experiment are conducted under above-mentioned intractable situations, validating the accuracy improvement and practical effectiveness of the algorithm. INDEX TERMS Multiple sources localization, particle filter, multi-modal radiation field, dynamic weighting correction, Gaussian processes.

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