In this paper, a system-specific phased-array radar system simulator was developed, based on a time-domain modeling and simulation method, mainly for system performance evaluation of the future Spectrum-Efficient National Surveillance Radar (SENSR). The goal of the simulation study was to establish a complete data quality prediction method based on specific radar hardware and electronics designs. The distributed weather targets were modeled using a covariance matrix-based method. The data quality analysis was conducted using Next-Generation Radar (NEXRAD) Level-II data as a basis, in which the impact of various pulse compression waveforms and channel electronic instability on weather radar data quality was evaluated. Two typical weather scenarios were employed to assess the simulator’s performance, including a tornado case and a convective precipitation case. Also, modeling of some demonstration systems was evaluated, including a generic weather radar, a planar polarimetric phased-array radar, and a cylindrical polarimetric phased-array radar. Corresponding error statistics were provided to help multifunction phased-array radar (MPAR) designers perform trade-off studies.

An application of machine intelligence technique for the identification of micro‐Doppler features from an airborne pulsed‐Doppler radar sensor is developed. The key challenges for surveillance mode are the dynamic nature of the wind farm clutters, short‐CPI length, and lack of prior information on the specific wind turbine (WT) in the site. The micro‐Doppler spectrum segments based on short CPIs are used as the fundamental feature vectors for detection and classification. Both supervised and unsupervised approaches, including artificial neural network and random forest, are applied to airborne plan position indicator scan outputs. A simulator for airborne pulsed‐Doppler radar operation over wind farm is used with realistic WT scattering signatures, platform motion impacts as well as the terrain clutter impacts. Based on the clutter identification result, the feasibility of detecting small moving targets in the presence of WT clutter is discussed.

This letter combines a time-domain modeling and simulation method for evaluating the impacts of system modules on polarimetric data quality of phased array weather radars with both theoretical analysis and actual measurements. In the presented phased array radar system simulator (PASIM), the distributed weather returns are modeled by randomly distributed scatterers, and Next-Generation Radar (NEXRAD) Level-II data or user-defined weather scenarios are utilized as weather truth fields. Based on a specially designed patch element, a dual-polarization phased array mobile demonstration system (Ten Panel Demonstrator or TPD) is simulated. In addition, the biases of differential reflectivity, copolar correlation coefficient, and differential phase along beam direction away from the broadside in principal plane and nonprincipal plane are used as data quality metrics. Moreover, theoretical analysis, system simulation, and actual TPD proof of concept measurements in a stratiform precipitation and a convective precipitation are presented, respectively, then similarities and discrepancies between simulations and measurements are compared and explained.

As a critical aviation support sensor, radar altimeters face imminent challenges due to the interference caused by the frequency overlap with the developing 5G telecommunications networks. This paper addresses the critical challenge posed by 5G interference with radar altimeter signals, which is crucial for maintaining aviation safety. It introduces a novel machine learning (ML) framework to preserve altimeter accuracy in the presence of 5G signals. This framework first classifies signals into pure or interfered categories, then applies regression models to predict altitudes when interference is detected, effectively quantifying and mitigating the interference impact. Distinguished by using real 5G signals from a Norman, Oklahoma base station, the approach offers a realistic evaluation and demonstrates the ML framework’s effectiveness in real-world conditions. Using various advanced ML models, the methodology showcases how critical aviation instruments can be safeguarded against the challenges posed by emerging telecommunications technologies, ensuring air travel safety and efficiency.