基于粒子激光散射极化的空气微粒检测与识别方法研究

The research work has been finished on time. In this project, we promoted the theory to the high-sensitive particle detection and recognition by means of sensor array combined with multi-laser receptions and intelligent signal processing the first time, in the way of the researching on single particle's scattering intensity and the characterization of polarization angle. According to the laser scattering polarization phenomenon aroused by the particle movement in the air sampling pipes, we firstly put forward an emulation and modelization technique on the polarization character of the laser scattering and the dynamic distribution model of laser intensity by means of Monte Carlo theory. Otherwise, In this project, we have studied the recognition, signal processing algorithm and pattern recognition base on wavelet fuzzy neural network on laser scattering polarization signals. At last, we developed a sample system which can distinguish some material up to 90% rightly.

本项目根据单个粒子激光散射极化激励，利用激光接收器阵列和智能信号处理技术研究空气粒子检测与识别方法。分析不同粒子的激光散射极化特性，建立粒子激光散射和极化的数学模型；采用蒙特卡罗方法模拟粒子激光散射空间分布模式；提出空气粒子的检测和识别方法；构造实验系统。它将为空气质量检测、烟雾识别和火灾探测提供一套先进有效的方法。