The challenge of polarimetric coupling in phased array weather radars is explained, along with the resulting requirements. For the first time, state-of-the-art mitigation techniques on the system, hardware and software level are discussed together along with their achievements and shortcomings. An outlook is made toward future developments and applications, including integrated weather sensing and communications.

The impact of cross-polar coupling and a finite available number of echo samples on the data quality of polarimetric weather radar data is investigated. A system model and a two-step simulation approach are proposed to simulate realistic time series of the received weather radar signals. The proposed simulation methodology is applied to a rainfall scenario to illustrate the data quality of two widely used weather radar measurement schemes. Through simulation of the bounds within which 90% of the resulting estimates fall, it is demonstrated that the finite sample size introduces deviations that limit the data quality, even in designs with low cross-polar coupling.

The problem of aliasing in precipitation Doppler spectrum with uniform pulse repetition time is addressed. This work focuses on de-aliasing such Doppler spectra using non-uniform sampling techniques, namely, Log-periodic and Periodic non-uniform sampling. These techniques reduce the ambiguous main lobes caused by aliasing (by going beyond the observable frequency limit) into ambiguous sidelobes that are distinguishable from the original spectra. The SNR is further enhanced by using an Iterative Adaptive Approach (IAA) algorithm. The performance of Doppler moment estimation is presented after applying the IAA algorithm on simulated precipitation-like radar echoes. However, the ambiguous sidelobe suppression is highly dependent on the spectral width of the Doppler spectra.

A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To compensate for this, surveillance radars that offer full spatial coverage are used, but the determination of the best detection and classification approach to be applied on the resulting data is still an open challenge. In this paper a framework is proposed that combines deep learning approaches from computer vision, specifically the You Only Look Once (YOLO) network, with data from the moving surveillance radar produced by Robin Radar Systems B.V. This framework allows to jointly detect and label targets based on range-Doppler images generated in real-time. The method is validated on experimental data, with preliminary results on a small dataset showing precision, recall, mean average precision (mAP@0.5) and Area Under Curve (AUC) of over 99%