Multipulse Processing Algorithm for Improving Mean Velocity Estimation in Weather Radar

In this article, we present a novel algorithm termed multipulse processing (MPP) for improving mean Doppler velocity estimation in weather radar applications. It can be used for both staggered pulse repetition time (PRT) and uniform-PRT sequences. Essentially, MPP consists of finding a particular zero of a functional composed of data autocorrelation estimates at multiple lags. To select the proper zero, an initial Doppler velocity estimate is required. Therefore, MPP can be considered as an estimation refinement stage. Its advantage lies in the fact that it uses the complete information contained in the radar signal autocorrelation. After a theoretical analysis, we compare the performance of MPP against other well-established methods of similar complexity and the Cramér–Rao lower bound, by means of Monte Carlo simulations using synthetic data. We show that the proposed estimator offers the lowest root-mean-square error (RMSE) at low signal-to-noise ratio (SNR) situations for a wide range of spectral widths. Finally, we evaluate the MPP algorithm performance using real data measured by the RMA Argentinian weather radar. The results of tests performed are consistent with those of Monte Carlo simulations and validate the proposed method.