A RR-MWF Based Spectral Moments Estimation Algorithm for Airborne Weather Radar Echoes with Low SNR

The precipitation of CAT (Clear Air Turbulence) is lower than that of the convective induced turbulence, resulting echoes with low SNR are received by classic airborne weather radars. The error of the spectrum width estimation is large in low SNR scenarios where the widely used pulse pair processing (PPP) method for airborne weather radars is used, therefore the PPP method can’t be used for detecting CAT. A spectral moments estimation method based on reduced-rank multistage wiener filter (RR-MWF) is proposed for improving the estimation performance in low SNR scenarios. The proposed method is in essential a space-time adaptive processing algorithm, which can improve the SNR of radar echoes by integrating coherently both in the spatial and the temporal dimension. Considering that the CAT is a kind of distributed target, the adaptive RR-MWF weighted vector is newly constructed and the cost function used for estimating the spectral moments is deduced under the mean square error criterion, which are the main elements of the work. Experimental simulations and numerical analysis show that the RR-MWF estimator outmatches the PPP method in low SNRs, typically lower than 10dB. And RR-MWF estimator can be used to detect CAT.