Enhanced Signal Processing Algorithms for the ASR-9 Weather Systems Processor

John Y. N. Cho Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts

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Abstract

New signal processing algorithms for the Airport Surveillance Radar-9 (ASR-9) Weather Systems Processor (WSP) are introduced. The Moving Clutter Spectral Processing for Uneven-Sampled Data with Dealiasing (MCSPUDD) algorithm suite removes isolated moving clutter targets and corrects aliased velocity values on a per-range-gate basis. The spectral differencing technique is applied to the low- and high-beam data to produce a dual-beam velocity estimate that is more accurate than the current autocorrelation-lag-1-based approach. Comparisons with Terminal Doppler Weather Radar (TDWR) data show that estimate errors are reduced by 8%, 15%, and 15% for the low-, high-, and dual-beam velocities, respectively.

Corresponding author address: John Y. N. Cho, MIT Lincoln Laboratory, 244 Wood St., S1-539, Lexington, MA 02420-9185. E-mail: jync@ll.mit.edu

Abstract

New signal processing algorithms for the Airport Surveillance Radar-9 (ASR-9) Weather Systems Processor (WSP) are introduced. The Moving Clutter Spectral Processing for Uneven-Sampled Data with Dealiasing (MCSPUDD) algorithm suite removes isolated moving clutter targets and corrects aliased velocity values on a per-range-gate basis. The spectral differencing technique is applied to the low- and high-beam data to produce a dual-beam velocity estimate that is more accurate than the current autocorrelation-lag-1-based approach. Comparisons with Terminal Doppler Weather Radar (TDWR) data show that estimate errors are reduced by 8%, 15%, and 15% for the low-, high-, and dual-beam velocities, respectively.

Corresponding author address: John Y. N. Cho, MIT Lincoln Laboratory, 244 Wood St., S1-539, Lexington, MA 02420-9185. E-mail: jync@ll.mit.edu
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