摘要
针对航空γ能谱测量仪中多路NaI(Tl)探测器中的不同噪声水平,采用加权最小二融合估算法对能谱信息进行提取。该算法通过对NaI(Tl)探测器测量的数据进行在线方差学习,及时调整各探测器的权系数。将该方法用于航空能谱仪在标准模型上采集的数据来进行分析,可以看出,其单秒测量得到的误差范围有所改善,测量结果的准确度有所提高。
Background： Airborne gamma-ray spectrometry is made up of multiple NaI（T1） detector, but the detectors have the different level of noise. Purpose： Aimed at reducing the affection of noise, a weighted least square fusion estimation algorithm is presented to extract the Spectra Characteristics. Methods： The method doesn＇t need any prior knowledge on the detector, but carries on the variance estimated on-line these data and timely adjust weights of various fusion sensors in order to make the mean-square error of fusion results least all the time. Results： It is used to process the date come from the standard model, the results show that measurement error range of ^40K decreased from 17% to 12%. Conclusions： The result has shown that the method can dramatically decrease the error and improve the accuracy.
出处
《核技术》
CAS
CSCD
北大核心
2013年第2期32-35,共4页
Nuclear Techniques
基金
国家自然科学基金(40904054)
核地球物理学天然γ场地研究(40774063)
国家863计划重大项目(2006AA06A207)资助
关键词
航空γ能谱仪
加权最小二乘法
数据融合
方差估计
Airborne gamma-ray spectrometry, Weighted least square estimation, Data fusion, Variance estimation
