Using the SPARC Simulator to Study Data Quality and Adaptive Scanning for SENSR

The goal of the Spectrum Efficient National Surveillance Radar (SENSR) program is to replace both the weather and aircraft surveillance capabilities of the current networks of federally-owned, fixed, ground-based radar systems to free at least 30 MHz of bandwidth in L band for auction by 2024. Multiple agencies are assessing the feasibility of reallocating L-band radars to another band while addressing cross-agency mission efficiency. One possible candidate for meeting the demanding SENSR requirements is a multifunction phased-array radar (MPAR). As part of the SENSR program, we are studying the effects of phased-array antennas on both data quality and adaptive scanning using sophisticated simulations. The Signal Processing and Radar Characteristics (SPARC) simulator was developed to study the impact of diverse signal processing techniques and radar system characteristics on the radar variables required to support the weather-surveillance mission of the National Weather Service (NWS). This simulator ingests archived fields of weather radar data to produce dual-polarization IQ time-series data while incorporating the effects of user-defined sampling and processing parameters. For example, we are using the SPARC simulator to study the data quality impacts of antenna radiation patterns with varying sidelobe levels both in azimuth and elevation. We also plan on exploring the effects of range sidelobes and spatial sampling. In addition, we are using the SPARC simulation in an adaptive weather sensing framework to examine the impacts of adaptive scanning. For example, we have been exploring the data quality effects of spoiling the beam on transmit and forming multiple digital receive beams, a mechanism that would help reduce scan times to address possible load-shedding scenarios. All of these simulations depend on choosing appropriate archived data cases from the National Centers for Environmental Information (NCEI). Results that capture the current state of our SENSR research will be presented at the conference.