Multicomponent Signal Analysis Based on Polynomial Chirplet Transform
Multicomponent Signal Analysis Based on Polynomial Chirplet Transform
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DOI：
10.1109/tie.2012.2206331
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发表时间：
2013-09-01
影响因子：
7.7
通讯作者：
Meng, Guang
中科院分区：
文献类型：
--
作者：
Yang, Yang;Zhang, Wenming;Meng, Guang
线性调频小波变换(CT)可以有效地刻画单分量线性调频信号的瞬时频率。然而，CT不适合分析含有非线性调频分量的多分量信号。提出了一种基于多项式CT(PCT)的时频融合技术(TFPCT)来表征这类信号的时频结构。TFPCT依赖于这样一个事实，即PCT能够在时频分布(TFD)中沿着单分量信号的中频紧密地集中能量。对于多分量信号，TFPCT首先估计关于单个分量的适当系数，然后使用PCT产生一系列TFD。每个TFD沿一个组分的IF具有更好的能量集中。然后，为了减少不需要的分量的干扰并保留感兴趣的分量，对每个TFD进行过滤并分组为一幅图像。最后，TFPCT将这些TFD组合成最终的熔融TFD，使能量紧密地集中在所有元件的中频上。在数值多分量信号和蝙蝠回声定位信号上与几种传统的TFD方法进行了比较，验证了该方法的潜力和有效性。
Chirplet transform (CT) is effective in characterization of instantaneous frequency (IF) for monocomponent linear-frequency-modulated signal. However, the CT is not suitable to analyze multicomponent signal with nonlinear-frequency-modulated component. In this paper, a time-frequency fusion technique based on polynomial CT (PCT) (TFPCT) is proposed to characterize the time-frequency structure of such signals. The TFPCT relies on the fact that the PCT is able to concentrate the energy closely along the IF of the monocomponent signal in time-frequency distribution (TFD). For multicomponent signal, the TFPCT first estimates the proper coefficients with respect to individual component and, second, produces a series of the TFD using the PCT. Each TFD has better energy concentration along the IF of one component. Then, in order to reduce the interference of unwanted component and preserve the component of interest, each TFD is filtered and grouped as an image. At last, the TFPCT combines these TFDs to be an eventual fused TFD, which has the energy concentrating closely along the IF of all components. Comparison with several conventional TFD methods on both numerical multicomponent signal and bat echolocation signal validates the potential and the effectiveness of the proposed method.