小波分析理论及其在雷达信号处理中的应用

依据所处理信号类型的不同,对小波变换与短时傅立叶变换进行了比较,说明小波变换适用于非平稳信号和奇异信号的处理.简要介绍了连续小波变换、离散小波变换和小波包理论的基本内容,讨论了小波变换实际应用中的采样和快速算法等几个关键问题.结合矢量量化技术,给出了一种用小波变换对星载SAR(Synthetic Aperture Radar)原始数据进行压缩的方法,结果表明,与传统的方法相比,由该方法压缩后的原始数据生成的SAR图像具有更大的信噪比.同时对小波变换用于雷达目标识别和SAR图像分类的性能进行了初步分析.

Wavelet Analysis Theory and Application in Radar Signal Processing

According to the features of signal to be processed, the wavelet transform is compared with short-time Fourier transform. It indicates that the wavelet transform is suitable for the processing of nonstationary and singular signal. The continuous wavelet transform, discrete wavelet transform and wavelet packet theory are reviewed in brief. Some key problems are discussed, such as sampling and fast algorithm which are essential to the application of wavelet transform. Finally, a data compression method is presented that compresses the raw SAR (Synthetic Aperture Radar) data by means of wavelet transform and vector quantizer. Compared with the traditional method, the result shows that the SAR image generated from compressed raw data has better SNR(Signal to Noise Ratio), and meanwhile the performance of wavelet transform in radar target identification and SAR image classification are also discussed.