Radiation Mapping: A Gaussian Multi-Kernel Weighting Method for Source Investigation in Disaster Scenarios Article Swipe
Songbai Zhang
,
Qi Liu
,
Jie Chen
,
Yujin Cao
,
Guoqing Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25154736
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25154736
Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R2) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25154736
- https://www.mdpi.com/1424-8220/25/15/4736/pdf?version=1753969934
- OA Status
- gold
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412931422
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412931422Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25154736Digital Object Identifier
- Title
-
Radiation Mapping: A Gaussian Multi-Kernel Weighting Method for Source Investigation in Disaster ScenariosWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-07-31Full publication date if available
- Authors
-
Songbai Zhang, Qi Liu, Jie Chen, Yujin Cao, Guoqing WangList of authors in order
- Landing page
-
https://doi.org/10.3390/s25154736Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/25/15/4736/pdf?version=1753969934Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/25/15/4736/pdf?version=1753969934Direct OA link when available
- Concepts
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Ground-penetrating radar, Kriging, Computer science, Gaussian process, Weighting, Gaussian function, Remote sensing, Gaussian, Algorithm, Physics, Radar, Machine learning, Geology, Acoustics, Telecommunications, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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