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Sensors 2018, 18(11), 3784; https://doi.org/10.3390/s18113784

Robust Radiation Sources Localization Based on the Peak Suppressed Particle Filter for Mixed Multi-Modal Environments

1
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
2
Zhejiang Province Environmental Radiation Monitoring Center, Hangzhou 310000, China
*
Author to whom correspondence should be addressed.
Received: 10 October 2018 / Revised: 28 October 2018 / Accepted: 30 October 2018 / Published: 5 November 2018
(This article belongs to the Section Physical Sensors)
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Abstract

This paper addresses a detection problem where sparse measurements are utilized to estimate the source parameters in a mixed multi-modal radiation field. As the limitation of dimensional scalability and the unimodal characteristic, most existing algorithms fail to detect the multi-point sources gathered in narrow regions, especially with no prior knowledge about intensity and source number. The proposed Peak Suppressed Particle Filter (PSPF) method utilizes a hybrid scheme of multi-layer particle filter, mean-shift clustering technique and peak suppression correction to solve the major challenges faced by current existing algorithms. Firstly, the algorithm realizes sequential estimation of multi-point sources in a cross-mixed radiation field by using particle filtering and suppressing intensity peak value, while existing algorithms could just identify single point or spatially separated point sources. Secondly, the number of radioactive sources could be determined in a non-parametric manner as the fact that invalid particle swarms would disperse automatically. In contrast, existing algorithms either require prior information or rely on expensive statistic estimation and comparison. Additionally, to improve the prediction stability and convergent performance, distance correction module and configuration maintenance machine are developed to sustain the multimodal prediction stability. Finally, simulations and physical experiments are carried out in aspects such as different noise level, non-parametric property, processing time and large-scale estimation, to validate the effectiveness and robustness of the PSPF algorithm. View Full-Text
Keywords: particle filter; Bayesian estimation; radiation sources localization; nonparametric estimation; multi-modality maintenance particle filter; Bayesian estimation; radiation sources localization; nonparametric estimation; multi-modality maintenance
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Gao, W.; Wang, W.; Zhu, H.; Huang, G.; Wu, D.; Du, Z. Robust Radiation Sources Localization Based on the Peak Suppressed Particle Filter for Mixed Multi-Modal Environments. Sensors 2018, 18, 3784.

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