Detection of Clear Air Turbulence by Airborne Weather Radar using RR-MWF Method

Abstract

The precipitation of CAT (Clear Air Turbulence) is lower than that of the convective turbulence, resulting low SNR echoes are received by airborne weather radars in the detection of CAT. This will inevitably lead to poor performance on spectrum width estimation where pulse pair processing (PPP) method is used. To address this issue, an echo spectral moments estimation method based on reduced-rank multistage wiener filter (RR-MWF) is proposed by introducing space-time adaptive processing algorithm for airborne weather radar turbulence detection performance enhancement in low SNR scenarios. The proposed method inherits the capability of enhancing echo SNR by accumulating signals coherently, both in the spatial and the temporal dimensions. The adaptive RR-MWF weighted vector and cost function are constructed under the MSE (Mean Square Error) criterion, therefore the spectral moments can be accurately estimated for CAT, which is considered as one of the distributed weather targets. Numerical simulations show that the RR-MWF outmatches the PPP method when SNR is lower than 10dB, therefore demonstrating its effectiveness in low SNR scenarios.