Robust Radiation Sources Localization Based on the Peak Suppressed Particle Filter for Mixed Multi-Modal Environments.
Robust Radiation Sources Localization Based on the Peak Suppressed Particle Filter for Mixed Multi-Modal Environments.
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基于峰值抑制粒子滤波器的混合多模态环境的鲁棒辐射源定位
DOI：
10.3390/s18113784
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发表时间：
2018-11-05
期刊：
影响因子：
--
通讯作者：
Du Z
中科院分区：
文献类型：
--
作者：
Gao W;Wang W;Zhu H;Huang G;Wu D;Du Z
本文研究了在混合多模辐射场中利用稀疏测量估计源参数的检测问题。由于维度可伸缩性和单峰特性的限制，现有的大多数算法都不能检测出聚集在狭窄区域内的多点信源，特别是在没有关于强度和信源数目的先验知识的情况下。提出的峰值抑制粒子滤波(PSPF)方法利用多层粒子滤波、均值漂移聚类技术和峰值抑制校正的混合方案来解决现有算法面临的主要挑战。该算法首先利用粒子滤波和抑制强度峰值的方法实现了交叉混合辐射场中多点源的序贯估计，而现有算法只能识别单点或空间分离的点源。其次，由于无效粒子群会自动消散，放射源的数量可以以非参数的方式确定。相比之下，现有算法要么需要先验信息，要么依赖昂贵的统计估计和比较。此外，为了提高预测的稳定性和收敛性能，开发了距离校正模块和配置维护机来维持多峰预测的稳定性。最后，从不同的噪声水平、非参数特性、处理时间和大规模估计等方面进行了仿真和物理实验，验证了PSPF算法的有效性和鲁棒性。
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.
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影响因子：
1.8
作者：
Deb, Budhaditya
通讯作者：
Deb, Budhaditya
DOI：
10.1093/jicru/ndm020
发表时间：
2007-06-01
期刊：
Journal of the ICRU
影响因子：
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作者：
Deluca, Paul M
通讯作者：
Deluca, Paul M
DOI：
10.1016/j.nima.2015.01.037
发表时间：
2015-06-01
影响因子：
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作者：
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通讯作者：
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影响因子：
6.1
作者：
Mayhorn, Christopher B.;McLaughlin, Anne Collins
通讯作者：
McLaughlin, Anne Collins
DOI：
10.1109/34.1000236
发表时间：
2002-05-01
影响因子：
23.6
作者：
Comaniciu, D;Meer, P
通讯作者：
Meer, P