Beamspace Adaptive Processing for Phased-Array Weather Radars

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Tuesday, 6 January 2015: 12:00 AM
132AB (Phoenix Convention Center - West and North Buildings)
Feng Nai, Univ. of Oklahoma, Norman, OK; and S. M. Torres and R. Palmer

Active phased-array radars can adaptively change the beam pattern to mitigate the detrimental effects of interfering signals. With this purpose, many adaptive beamforming algorithms have been proposed in the literature. Traditionally, these adaptive algorithms operate on the received signals from each antenna element, and their computational complexity makes them unattractive for real time implementation. More importantly, these algorithms were developed using a signal model consisting of discrete point targets, and applying these algorithms in the presence of distributed scatterers results in significantly biased reflectivity estimates. Furthermore, due to the adaptive nature of the beam pattern, reflectivity calibration becomes more difficult compared to the calibration process for a fixed beam pattern. Unlike traditional adaptive beamforming algorithms, beamspace processing operates on the received signals from a set of deterministic beams. Although motivated by the desire to reduce the computational complexity of adaptive beamforming algorithms, beamspace processing is perfectly suited for weather radars since it does not exhibit the abovementioned calibration problem in the presence of distributed targets. This paper presents an adaptive processing algorithm that operates in beamspace that solves the weather radar calibration problem and produces accurate estimates for the meteorological variables. In addition, compared to element-space adaptive beamforming algorithms, beamspace adaptive processing is computationally simpler and requires fewer samples to achieve comparable estimation accuracy, which makes it more attractive for real time implementation. A mathematical derivation of beamspace adaptive processing is given to provide an intuitive understanding of how the algorithm achieves interference nulling. Simulations are used to quantify the superior performance of the proposed algorithm compared to traditional Fourier and Capon beamforming on phased-array antenna systems, and also to conventional dish antenna systems. Finally, the performance of beamspace adaptive processing is illustrated by using severe storm data collected by the Atmospheric Imaging Radar (AIR), a proof-of-concept active phased-array radar developed by The University of Oklahoma's Advanced Radar Research Center.