摘要
针对信号稀疏分解中常用的匹配追踪算法运算耗时较长、分解不够准确等问题,将结合混沌映射与局部搜索思想的改进果蝇优化算法应用于匹配追踪中,以提高信号稀疏分解的速度与准确度。将该算法应用于超声回波仿真信号和换热管污垢超声检测实验信号的处理,并与其他几种常用匹配追踪算法进行比较。结果表明该改进果蝇优化匹配追踪算法,在提高信号稀疏分解速度的同时,获得了较好的降噪与信息提取效果,对超声检测信号的处理具有较重要意义。
Aiming at the problems of common matching pursuit algorithms in signal sparse decomposition, such as time-consuming calculation and inaccurate decomposition, this article proposes a new improved fruit fly optimization algorithm （FOA） , which combines the chaotic mapping and local searching, is applied in matching pursuit to improve the speed and accuracy of the signal sparse decomposition. The new method was applied both in the processing of ul- trasonic echo simulation signal and the heat exchanger fouling ultrasonic detection signal, and compared with several other common matching pursuit algorithms. The results show that the improved fruit fly optimization matching pursuit algorithm can increase the speed of the signal sparse decomposition and obtain better de-noising and information ex- tracting effect at the same time. This method is of great importance to the processing of ultrasonic detection signals.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2013年第9期2068-2073,共6页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51176028)
吉林省自然科学基金(201115181)资助项目
关键词
超声检测
果蝇优化
匹配追踪
混沌映射
ultrasonic detection
fruit fly optimization
matching pursuit
chaotic mapping
