基于先验估计的自适应Chirplet信号展开

该文提出一种新的时频表示方法--自适应线性调频小波(Chirplet)信号展开算法。算法基于信号本征空间,融参数的初值估计和精确估计于一体,利用匹配追踪算法将信号自适应地展开在高斯线性调频小波基函数集上。通过展开系数和基函数参数获得信号的时频分布,其时频聚集性、抗噪性和时频分辨率不仅优于一般的时频分布而且优于已有的自适应时频分布,可以更好地刻画信号的本质。应用数值仿真检验了算法的有效性和时频分布的优良性能。

Adaptive Chirplet Signal Expansion Based on Transcendental Estimation

In this paper, a new time-frequency representation method, adaptive signal expansion algorithm, is presented. The algorithm is based on that essential character of signal space, initial value estimation and precise resolution are obtained simultaneously. Signal is adaptively expanded to a sum of chirplet elementary functions by using match pursuit algorithm. Then, according to expansion coefficients and elementary function parameters, adaptive time frequency distribution is obtained. Its time frequency congregate, noise-reduction and time frequency resolution are not only better than the general time frequency distribution but also better than adaptive time frequency distribution reported and it is able to characterize the signal's nature exactly. The validity of the algorithm and the performance of adaptive time frequency distribution are tested by numerical simulations.