Multi-Pulse Second-Trip Echo Suppression of Weather Radar Based on Hybrid SZ Phase Coding

‍ ‍Doppler weather radar is subject to the “Doppler Dilemma” which can introduce severe second-trip echo interference in observational data. Modern solid-state weather radars typically employ a combination of long- and short-pulse waveforms to balance detection range and sensitivity. However， second-trip echoes can originate from long and short pulses， resulting in more complex echo aliasing interference. This phenomenon can lead to misjudgments of target positions and intensities， significantly degrading the spectral moment estimation performance of weather radars. While existing research widely employs phase coding techniques to suppress second-trip echo interference， these approaches generally fail to address the challenges posed by combined long/short-pulse transmissions. This paper proposes a hybrid phase coding-based method for second-trip echo suppression， specifically designed to mitigate interference from multiple transmitted pulse types. First， a signal model is established to represent the mixed interference caused by aliased long- and short-pulse second-trip echoes in Doppler weather radar. Then， a hybrid phase coding sequence is developed， incorporating a specific relative phase difference pattern， $\mathrm{\pi }{i}^{\mathrm{2}}/\mathrm{8}$， based on the SZ（16/64） coding criterion. This sequence is applied to phase-modulate the combined long/short-pulse transmission signals. By optimizing the relative phase differences， the method enables complete demodulation of valid echoes while introducing coding cross terms into the interference components. Further， these cross terms conform to the SZ coding structure， thereby achieving optimal suppression of interference within the SZ coding framework. Finally， equivalent experiments using real weather radar data quantitatively validate the proposed method’s effectiveness. Results show a significant improvement in spectral moment estimation performance， demonstrated by a substantial reduction in the root mean square error （RMSE） of estimation outcomes. This confirms the method’s ability to enhance the accuracy and reliability of weather radar observations.