Abstract
The spectral moments (average Doppler frequency and Doppler spectral width) of radar echoes are closely related to the type of meteorological targets. Pulse Pair Processing (PPP) method is used to estimate the spectral width in the conventional airborne weather radar (WXR). However, the PPP method has a large estimation error when the signal-to-noise ratio (SNR) of the radar echoes is low in the scenarios clear air turbulence (CAT). Thus the current WXR cannot detect CAT effectively. The problem can be solved by introducing Space Time Adaptive Processing (STAP), in which the SNR is improved via space-time integration. However, the full-dimension optimum STAP suffers from the curse of dimensionality. In the paper, we reduce the dimension of the problem by random sampling, i.e. by projecting the data into a random d-dimensional subspace. Accordingly, it offers significant computational savings permitting possible real time solutions. The simulation results show that the random projected STAP estimator is better than PPP when the SNR is low. And it has lower computation load but only with minor performance degradation compared with the optimal STAP.
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