Improved multi-resolution method for MLE-based localization of radiation sources

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

doi:10.23919/icif.2017.8009626

Cited by 14 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the nine source-separation experiments conducted using a survey pattern (see Section III-B), APSL reconstructed two discrete sources in six of the eight runs that had a non-zero true separation. In these six survey runs (19,21,23,25,27,29), the reconstructed separations were within 15 cm of the true separation. The minimum true separation for which this level of accuracy was achieved was 76 cm (run 19), with an average and standard deviation reconstructed separation over 10 random seeds of 82 ± 2 cm.…”

Section: A Apsl Reconstructionsmentioning

confidence: 82%

“…In previous work [16], we proposed Additive Point Source Localization (APSL), a sparse parametric image reconstruction algorithm, as an alternative to ML-EM, MAP-EM, and several other previous methods [12], [17]- [25]. APSL is proposed for sparse 3D scenarios with multiple point sources and unknown backgrounds, a situation where previous methods may have limited utility due to algorithmic assumptions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources With a Sparse Parametric Algorithm

Vavrek

Hellfeld

Bandstra

et al. 2020

IEEE Trans. Nucl. Sci.

View full text Add to dashboard Cite

We present an experimental demonstration of Additive Point Source Localization (APSL), a sparse parametric imaging algorithm that reconstructs the 3D positions and activities of multiple gamma-ray point sources. Using a handheld gamma-ray detector array and up to four 8 µCi 137 Cs gamma-ray sources, we performed both source-search and source-separation experiments in an indoor laboratory environment. In the majority of the source-search measurements, APSL reconstructed the correct number of sources with position accuracies of ∼20 cm and activity accuracies (unsigned) of ∼20%, given measurement times of two to three minutes and distances of closest approach (to any source) of ∼20 cm. In source-separation measurements where the detector could be moved freely about the environment, APSL was able to resolve two sources separated by 75 cm or more given only ∼60 s of measurement time. In these source-separation measurements, APSL produced larger total activity errors of ∼40%, but obtained source separation distances accurate to within 15 cm. We also compare our APSL results against traditional Maximum Likelihood-Expectation Maximization (ML-EM) reconstructions, and demonstrate improved image accuracy and interpretability using APSL over ML-EM. These results indicate that APSL is capable of accurately reconstructing gamma-ray source positions and activities using measurements from existing detector hardware.

show abstract

Section: A Apsl Reconstructionsmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources With a Sparse Parametric Algorithm

Vavrek

Hellfeld

Bandstra

et al. 2020

IEEE Trans. Nucl. Sci.

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

“…The area of radiation field mapping and source localization has been studied through a set of contributions such as [2][3][4][5][6]. Among them, maximum likelihood estimation is utilized in [4,6], numerical adjoints in conjuction to a bayesian formulation are used in [2], while a particle filter is employed in [3]. These methods focus on the problem of estimating discrete sources and tend to assume the availability of a large number of spatially distributed measurements -typically provided using an array of fixed sensors.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Distributed 3D Radiation Field Estimation for Nuclear Environment Characterization

Mascarich

Petris

Nguyen

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

This paper contributes a method designed to enable autonomous distributed 3D nuclear radiation field mapping. The algorithm uses a single radiation sensor and a sequence of spatially distributed and robotically acquired radiation measurements across a discretized 3D grid to derive a radiation gradient. The derived gradient is probabilistically propagated to unknown components of the map to further guide a curiosity-driven path planner by identifying the next most radiologically informative point given available information.To demonstrate the method, we develop a resilient micro flying robot capable of autonomous GPS-denied navigation that integrates a Thallium-doped Cesium Iodide (CsI(Tl)) scintillator and Silicon Photomultiplier (SiPm) combined with custom-built pulse counting circuitry. A set of experimental studies is presented inside an indoor facility within which actual radioactive uranium ore sources have been distributed.

show abstract

“…A visual example of an MLE localization is shown in Figure 8. Note that the likelihood function in (3) is a simplification of the logarithm of the joint Poisson probability for the likelihood of the measurements at each individual detector [11].…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Brooks

et al. 2018

Proceedings of International Symposium on Sensor Networks, Systems and Security

Self Cite

View full text Add to dashboard Cite

Many systems are partially stochastic in nature. We have derived datadriven approaches for extracting stochastic state machines (Markov models) directly from observed data. This chapter provides an overview of our approach with numerous practical applications. We have used this approach for inferring shipping patterns, exploiting computer system side-channel information, and detecting botnet activities. For contrast, we include a related data-driven statistical inferencing approach that detects and localizes radiation sources. IntroductionMarkov models have been widely used for detecting patterns [33,36,7,4,24,25,15,17]. The premise behind a Markov models is that the current state only depends on the previous state and that transition probabilities are stationary. This makes Markov models versatile, as this is a direct result of the causal world we live in. Often,

show abstract

Improved multi-resolution method for MLE-based localization of radiation sources

Cited by 14 publications

References 16 publications

Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources With a Sparse Parametric Algorithm

Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources With a Sparse Parametric Algorithm

Autonomous Distributed 3D Radiation Field Estimation for Nuclear Environment Characterization

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Contact Info

Product

Resources

About