Abstract
The aircraft passive sonic detects and identification technology as a traditional means of reconnaissance is an important component of airborne early-warning system. Using the sound wave what the acoustic targets produces in the rate process, to identify the targets is the basic task of passive acoustic detection system. This study using modern signal processing method studies the wavelet transform feature information extraction method of target audio signals. Based on the two kinds of battlefields targets about the audio spectrum characteristics, using the characteristic pick-up arithmetic of the wavelet decomposition measure detail signal domain energy which is based on wavelet theorystudy using this algorithm obtains lower-dimensional feature vector.
Highlights
The aircraft sonic feature target recognition belongs to the category of pattern recognition, as shown in Fig. 1, which shows, pattern recognition system mainly divided into data acquisition, pretreatment, feature extraction and selection, classification and decisionmaking four parts
In the aircraft passive sonic feature recognition system, target recognition is key to the feature extraction and classifier design
This study focused on two kinds of aircraft, attacking helicopters and a certain type of fighters, using the wavelet analysis method
Summary
The armed helicopter target audio characteristic analysis: The causes of the armed helicopter noise are very complicated. Fighters noise signal is from a low frequency to high frequency broadband signal, the main energy concentrated in the low frequency (500 Hz below), for medium low continuous spectrum, within 800 Hz the low frequency is obviously characteristic peak. (a) The helicopter 10 a layer of wavelet analysis reflect the characteristics of the signal frequency domain, and can be well give its time domain of the description, by using wavelet transform extracted features has stability. If the different scale of signal decomposition for energy out, can according to scale these to an order form feature vector for identify with This is the basic principle which based on wavelet transform to extract the multi-scale space energy characteristics.
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