Improvements in the Compression Filter and Calibration Factor of the Progressive Pulse Compression Technique

Progressive pulse compression (PPC) was introduced to mitigate the need for a fill pulse in pulse-compression-based radar systems. It provides a method for recovering signals in the blind-range region created by the transmission of relatively long pulses. However, the initial implementation of PPC has limitations that need to be addressed for it to be more useful for meteorological applications. The proposed updated algorithm, named herein PPC+, brings significant improvements to mitigate these limitations. The methodology of PPC+ is similar to that of PPC, except that it uses a set of improved pulse compression filters. The improved compression filters are designed based on an amplitude modulation approach and are generated by multiplying the original filter by a range-dependent window. The window can be divided into two sections, the first part has a number of nulled samples used for mitigating the main lobe migration, and the remaining portion is a number of tapered samples to alleviate the “shoulder” effect from range sidelobes. Also, in contrast to PPC, the calibration factor used in PPC+ is further tuned to account for the tapering used in the improved compression filters. The PPC+ technique has been tested using data collected with PX-1000, a polarimetric X-band transportable solid-state radar system designed and operated by the Advanced Radar Research Center (ARRC) at The University of Oklahoma, and it is implemented and operational on that system (data available at https://radarhub.arrc.ou.edu). This technique has also been implemented on Horus, a fully digital phased array radar recently completed at the ARRC.

引入了渐进脉冲压缩（PPC），以减轻基于脉冲压缩的雷达系统中填充脉冲的需求PPC的初始实现需要解决，以使其对气象应用更有用。这些局限性。PPC+的方法与PPC的方法相似，除非它使用一组改进的脉冲压缩过滤器。依赖窗口可以分为两个部分，第一部分有许多用于减轻主叶的无效样本，其余部分是许多锥度与PPC相比，可以减轻范围的“肩膀”效应，并进一步调整了PPC+的校准因子，以考虑使用PPC+技术的逐渐变化。 PX-1000，一种由俄克拉荷马大学高级雷达研究中心（ARRC）设计和运营的偏光X波段可传输的固态雷达系统，它是在该系统上实施并运行（可在https://radarhub.arrc.ou.edu上获得数据）。