Assessment of the Benefits of Rapid Scanning for an MPAR/SENSR System

In order to improve radar coverage for both weather and aircraft surveillance across the United States, a new nationwide network of radars has been under investigation in the form of a Multifunction Phased Array Radar (MPAR) and a Spectrum Efficient National Surveillance Radar (SENSR). This MPAR/SENSR network would combine the capability of currently separate radar systems such as NEXRAD, TDWR, ASR-9/11, and ARSR-4 into one system at a single operating frequency. In addition to combining tasks spread across a wide range of operating frequencies into a single spectrum band, one major obstacle for this project is balancing the disparate requirements of each radar functionality. For example, short-range aircraft tracking must be updated every 4.8 s, while weather observations are subject to a 60-s update time requirement. An all-digital phased array radar has been proposed as a method of meeting these goals, which can leverage electronic beam steering and beam multiplexing to improve update times while minimizing sacrifices to data quality.

In this study, multiple scanning strategies which meet or exceed the preliminary performance requirements (PPRs) for the MPAR/SENSR systems are developed and tested. During the development of these scanning strategies, careful attention has been paid to tradeoffs in spatial resolution, temporal sampling, and data quality. An assessment of the benefits of each scanning method is obtained via multiple radar simulators. The outputs of these radar simulators are analyzed in a quantitative fashion—i.e., comparing times for tornado vortex/debris signature (TVS/TDS) detection and data quality estimates—and in a qualitative sense, by comparing radar output from various scanning strategies. Additionally, preliminary results from an adaptive scanning strategy using a cost function to maximize data quality are shown. This method utilizes estimates of radar moment errors to re-allocate pulses along each radar radial to maximize the number of locations with acceptable error estimates, similar in principle to an adaptive beam spoiling method outlined in Weber et al. (2017). These results are combined to assess feasibility and tradeoffs of various scanning strategies within an MPAR/SENSR framework.