面向多旋翼飞行器的γ谱仪关键技术研究

发布时间：2018-07-25 15:39

【摘要】：随着军用核武器、核材料及核技术扩散日益严重,以及核辐射事故和核恐怖事件特有的突发性,对社会安全和国家政治经济有极大威胁,具有核探测设备的多旋翼飞行器在核环境中的应用已经逐渐凸显出它的优势与不可估量的效果。本文围绕放射性物质探测、识别和处置问题,开展了理论与实际紧密结合的多旋翼飞行器γ谱仪关键技术研究工作。主要内容包括以下几方面:在研究数字谱仪原理和结果的基础上,分析得出多旋翼飞行器影响γ谱仪性能的主要因素及需要解决的关键技术问题。针对多旋翼飞行器平台,对传统核能谱仪数字处理方法进行分析、比较,并确定合成成形法、时间比较法、平均法为该平台的γ谱仪数字处理方法,为能谱分析奠定基础。针对传统谱线平滑方法在滤波器参数选择不当或平滑次数过多时引起谱线畸变的缺点,提出一种递归最小二乘法的能谱平滑方法。利用自适应滤波器原理,采用快速卡尔曼实现递归最小二乘法,并通过前向、后向预测器更新滤波器系数,实现能谱数据的平滑处理。通过实验将该方法与多项式最小二乘法、最小均方算法进行定性分析与比较,结果表明该方法能较好地降低能谱中的噪声,并能保持能谱的特征。针对传统神经网络在核素识别中训练效果弱,易陷入局部极小、收敛速度慢等问题,提出了基于概率神经网络的核素识别方法。平滑后的能谱数据通过希尔伯特-黄变换进行特征提取,将谱峰宽度、特征能量射线强度、峰面积等特征信息作为概率神经网络的输入,建立训练与测试样本从而进行核素识别。实验结果表明,此算法可以处理单一核素、混合核素以及未知核素的情况,可应用于安全监控、失控放射物探测等快速核素识别领域。最后,搭建了一套多旋翼飞行器γ谱仪关键技术测试平台,该平台具有前置放大、增益可调放大及数据采集等功能,对理论得出的最优化数字核信号处理方案的可行性与合理性进行了验证。为核事故突发情况下,使用具有核探测的多旋翼飞行器为核事故现场勘察和障碍排除奠定坚实的基础。
[Abstract]:With the increasing proliferation of military nuclear weapons, nuclear materials and nuclear technology, as well as the sudden occurrence of nuclear radiation accidents and nuclear terrorist incidents, they pose a great threat to social security and national political and economic development. The application of multi-rotor aircraft with nuclear detection equipment in nuclear environment has gradually highlighted its advantages and inestimable effects. This paper focuses on the problem of detection, identification and disposal of radioactive materials, and studies the key techniques of 纬 spectrometer of multi-rotary-wing aircraft, which are closely combined with theory and practice. The main contents are as follows: on the basis of studying the principle and results of the digital spectrometer, the main factors affecting the performance of the 纬 spectrometer and the key technical problems to be solved are obtained. This paper analyzes and compares the traditional digital processing methods of nuclear energy spectrometer for multi-rotor aircraft platform, and determines the synthetic shaping method, time comparison method and average method as the digital processing methods of 纬 -spectrometer of the platform, and lays a foundation for energy spectrum analysis. Aiming at the disadvantage of the traditional spectral line smoothing method which causes spectral line distortion when the filter parameters are not chosen properly or when the number of smoothing is too many a recursive least square method is proposed for spectral smoothing. Based on the principle of adaptive filter, the recursive least square method is implemented by using fast Kalman method, and the filter coefficients are updated by the forward and backward predictors to smooth the energy spectrum data. The method is qualitatively analyzed and compared with the polynomial least square method and the least mean square algorithm. The results show that the proposed method can reduce the noise in the energy spectrum and preserve the characteristics of the energy spectrum. In order to solve the problems of weak training effect, easy to fall into local minima and slow convergence rate of traditional neural network in nuclide recognition, a method of nuclide recognition based on probabilistic neural network is proposed. The smooth energy spectrum data are extracted by Hilbert-Huang transform, and the spectral peak width, characteristic energy ray intensity and peak area are used as the input of the probabilistic neural network. Establish training and test samples to identify nuclides. The experimental results show that the algorithm can deal with single nuclides, mixed nuclides and unknown nuclides, and can be used in the field of rapid nuclide recognition such as security monitoring, uncontrolled radionuclide detection and so on. Finally, a set of key technology test platform for multi-rotor aircraft gamma-ray spectrometer is built. The platform has the functions of preamplifier, gain adjustable amplification and data acquisition, etc. The feasibility and rationality of the optimized digital nuclear signal processing scheme are verified. It lays a solid foundation for nuclear accident site investigation and obstacle removal by using multi-rotor aircraft with nuclear detection.
【学位授予单位】：西南科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：V241;TL817.2

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