Rain Clutter Filtering from Radar Data with Discrete Wavelet Transform

Moving weather systems will have a nonzero Doppler response at rate at which the rain droplets are approaching the radar system. The complete data the radar collects contain the returns of both the target and the clutter. The signal processing block in a radar system uses filtering operations to extract the target information while suppressing the clutter. Typically the filters are designed based on Doppler Frequency using a Fourier filter bank. Instead of the frequency domain, the wavelet analysis allows the time-scale domain in processing. The filter bank in this study utilizes Discrete Wavelet Transform (DWT), DWT coefficients represent the results of a multi-resolution analysis of the radar signal. We study the operation of a DWT filter bank and a Fourier filter bank (FFT). Our experiments indicate that the Fourier filter bank filter the rain clutter very well. However, a DWT filter bank has different time resolution for different frequency ranges. With very heavy rain clutter affecting to the target signatures, our experiments indicate that the wavelet filter bank performs better than the Fourier filter bank. The experiments were performed in MATLAB environment and data is real radar rain clutter data from Finnish Air Force medium range air surveillance radar (low PRF). The objectives of this study were to develop a DWT based filtering system and to test it's operation in one situation of rain clutter and then to compare it's results to those from the FFT method.