一种战场声目标识别的多特征提取算法  

A Multi-feature Extraction Algorithm for Battlefield Acoustic Target Recognition

作　　者：陈德宏[1] 徐舒[1] 沈浩[1] 

机构地区：[1]安徽工业大学电气与信息工程学院,安徽马鞍山243032

出　　处：《安徽工业大学学报（自然科学版）》2017年第2期164-171,共8页Journal of Anhui University of Technology（Natural Science）

基　　金：国家自然科学基金项目(61304066)

摘　　要：针对战场声目标识别目前广泛采用的单一分析域特征提取算法会使部分重要信息遗漏,识别率低的问题,分析战场声信号的产生机理,得出声信号中既含随机性的机械噪声,又含准周期性的空气动力噪声;基于此,提出一种小波包与离散谱分析相结合的多特征参数提取算法。该算法利用小波包变换提取非均匀频段的能量分布特征,将其与离散谱描述的时域准周期性特征组合,得到更全面反映目标信号特性的特征参数。实验结果表明,相比单一分析域特征提取算法,多特征提取算法应用于声识别的准确性和鲁棒性明显提高。In view of the problem that the widely used single analysis domain feature extraction algorithm in battlefield acoustic target recognition often gives rise to some important information missing, and low recognition,therefore, the generation mechanism of battlefield acoustic signal was analyzed. It can be concluded that acoustic signal includes both randomness machine noise and quasi-periodic aerodynamic noise, based on this, a multifeature extraction algorithm was proposed by combining wavelet packet analysis with discrete spectrum analysis.The energy distribution of non-uniform frequency bands was extracted by means of the wavelet packet transform,combined with quasi-periodic feature in the time domain described by the discrete spectrum, thus the feature parameter was obtained, which reflected the characteristic of the target signal more comprehensively. The experimental results show that the accuracy and robustness of the multi-feature extraction algorithm applied to acoustic recognition are improved obviously, compared with that of single analysis domain feature extraction algorithm.

关 键 词：战场声目标 特征提取 小波包分析 离散谱分析 

分 类 号：TP391.42[自动化与计算机技术—计算机应用技术]