Large dynamic range time-frequency signal analysis with application to helicopter Doppler radar data

S. Marple
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引用次数: 18

Abstract

Despite the enhanced time-frequency analysis (TFA) detailing capability of quadratic TFAs like the Wigner and Cohen representations, their performance with signals of large dynamic range (DNR in excess of 40 dB) is quite poor due to the inability to totally suppress the cross-term artifacts which typically are much stronger than the weakest signal components that they obscure. This paper presents one of two modifications of linear TFA to provide the enhanced detailing behavior of quadratic TFAs without introducing cross terms, making it possible to see the time-frequency detail of extremely weak signal components. The technique described is based on subspace-enhanced linear predictive extrapolation of the data within each analysis window to create a longer data sequence for conventional short-time Fourier transform (STFT) TFA. The other technique, based on formation of a special two-dimensional transformed data matrix analyzed by high-definition two-dimensional spectral analysis methods such as 2-D AR or 2-D minimum variance, is presented in a separate time-frequency textbook under the development editorship of B. Boashash (see Time-Frequency Signal Analysis and Processing, Prentice Hall, 2002).
大动态范围时频信号分析及其在直升机多普勒雷达数据中的应用
尽管像Wigner和Cohen表示这样的二次型TFA具有增强的时频分析(TFA)详细能力,但它们在大动态范围信号(DNR超过40 dB)中的性能相当差,因为它们无法完全抑制交叉项伪影,而交叉项伪影通常比它们掩盖的最弱信号成分强得多。本文提出了线性TFA的两种改进之一,以提供二次TFA的增强细节行为,而不引入交叉项,从而可以看到极弱信号成分的时频细节。所描述的技术是基于每个分析窗口内数据的子空间增强线性预测外推,为传统的短时傅里叶变换(STFT) TFA创建更长的数据序列。另一种技术,基于形成一个特殊的二维变换数据矩阵,通过高清二维频谱分析方法,如二维AR或二维最小方差分析,在B. Boashash的开发编辑下,在单独的时频教科书中提出(见时频信号分析与处理,Prentice Hall, 2002)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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