Publications

Nonlinear frequency modulated (NLFM) pulse compression waveforms have become a mainstream methodology for radars across multiple sectors and missions, including weather observation, target tracking, and target detection. NLFM affords the ability to generate a low-sidelobe autocorrelation function and matched filter while avoiding aggressive amplitude modulation, resulting in more power incident on the target. This capability can lead to significantly lower system design costs due to the possibility of sensitivity gains on the order of 3 dB or more compared with traditional, amplitude-modulated linear frequency modulated (LFM) waveforms. Generation of an optimal NLFM waveform, however, can be an arduous task, and may involve complex optimization and non-closed-form solutions. For a multimission or cognitive radar, which may utilize a wide combination of frequencies, pulse lengths, and amplitude modulations (among other factors), this could lead to an extremely large waveform table for selection. This paper takes a neural network approach to this problem by optimizing a set of over 100 waveforms spanning a wide space and using the results to interpolate the waveform possibilities to a higher resolution. A modified form of a previous NLFM method is combined with a four-hidden-layer neural network to show the integrated and peak range sidelobes of the generated waveforms across the model training space. The results are applicable to multi-mission and cognitive radars that need precise waveform specifications in rapid succession. The expected waveform generation times are addressed and quantified, and the potential applicability to multi-mission and cognitive radars is discussed.

Terminal Doppler Weather Radars (TDWRs) provide near-ground wind shear detection that is critical for aircraft safety at 46 airports across the United States. These systems are part of the larger network of 510 weather and aircraft surveillance radars owned and operated by government agencies in the continental United States. As the TDWR and other radar systems approach their engineering design life cycles, the Federal Aviation Administration (FAA), National Weather Service (NWS), and Department of Defense (DoD) are considering potential replacement systems (OFCM 2006; Weber et al. 2007). One option under consideration that would maintain the current airspace coverage is a replacement network of 334 Multifunction Phased Array Radars (MPARs) (Weber et al. 2007). The MPAR network described by Weber et al. (2007) would include two classes of systems: A high-resolution, full-scale version with an 8-m diameter antenna, and a lower-resolution terminal version with a 4-m diameter antenna, termed Terminal MPAR, or TMPAR. As the proposed TMPAR design has lower azimuthal beam resolution and less sensitivity than TDWRs, it is crucial to determine the impacts of that design on the detection of low-altitude wind shear. The design of the SPY-1A PAR, a research radar at the National Weather Radar Test Bed in Norman, Oklahoma (Zrnić et al. 2007), makes it a good proxy for examining the potential wind shear detection performance of the TMPAR. Therefore, in spring 2012, the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory organized and executed the MPAR Wind Shear Experiment (WSE) in collaboration with the FAA, NOAA's NWS Radar Operations Center, the University of Oklahoma Advanced Radar Research Center (OU ARRC), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). The primary objective of the MPAR WSE was to collect low-altitude observations with the SPY-1A PAR (hereafter, PAR) for comparison with observations from the nearby Oklahoma City (OKC) TDWR. Of particular interest is comparison of MIT LL wind shear detection algorithm performance using data from these two radars; this analysis is reported in Cho et al. (2013). Data were also collected from other radars in central Oklahoma to facilitate basic research on microbursts and other wind-producing storms. This paper provides an overview of the MPAR WSE and observed wind shear events.