Vehicle Signal Enhancement Using Packet Wavelet Transform and Nonlinear Noise Processing Techniques

摘要

The role of signal preprocessors such as normalization preprocessor channel effects inversion preprocessor de-noising preprocessor have become an important part of many vehicle identification systems in order to extend their utility and provide continued performance in the face of reduced SNR. This paper examines the role of developing a non-parametric vehicle signal enhancement preprocessor by employing packet wavelet transform (PWT) decomposition nonlinear noise processing methods and signal restoration via the inverse PWT signal reconstruction. This effort is part of a larger project aimed at developing an Integrated Vehicle Classification System Using Wavelet / Neural Network Processing of Acoustic/Seismic Emissions on a Windows PC performed under a Phase II SBIR for the US Army TACOM/ARDEC. This paper presents a systematized study of the application of PWT de-noising methods to acoustic combat vehicle signals. Using an acoustic combat vehicle signal segment a linear model in the form of an ARMA filter is developed which closely mimics the dynamics of the vehicle time- series. This deterministic baseline model is mixed with scaled white noise to produce noise-corrupted signals of various SNRs. The PWT sensitivities examined include basis function families (Daubechies Coiflet Symlets Beylkin Biorthogonal) basis function support and Packet Tree decomposition length. Nonlinear noise processing sensitivities examined include the major thresholding methods (Universal Stein's Unbiased Risk Estimate (SURE) Minimax and Hybrid) and threshold application (Hard or Soft thresholding). Sensitivity analysis of these methods to the acoustic vehicle signal enhancement are presented along with a discussion concerning the reduction of this scheme to a preprocessor for a real- time vehicle monitor.