Comparison Of Meteorological Radar Signal Detectability With Noncoherent And Spectral-Based Processing

Detection of meteorological radar signals is often carried out using power averaging with noise subtraction either in the time domain or the spectral domain. This paper considers the relative signal processing gain of these two methods, showing a clear advantage for spectral-domain processing when normalized spectral width is less than similar to 0.1. A simple expression for the optimal discrete Fourier transform (DFT) length to maximize signal processing gain is presented that depends only on the normalized spectral width and the time-domain weighting function. The relative signal processing gain between noncoherent power averaging and spectral processing is found to depend on a variety of parameters, including the radar wavelength, spectral width, available observation time, and the false alarm rate. Expressions presented for the probability of detection for noncoherent and spectral-based processing also depend on these same parameters. Results of this analysis show that DFT-based processing can provide a substantial advantage in signal processing gain and probability of detection, especially when the normalized spectral width is small and when a large number of samples are available. Noncoherent power estimation can provide superior probability of detection when the normalized spectral width is greater than similar to 0.1, especially when the desired false alarm rate exceeds 10%.