• Atlas, D., 1989: The detection of low level windshear with airport surveillance radar. Preprints, Third Int. Conf. on the Aviation Weather System, Anaheim, CA, Amer. Meteor. Soc., 2124.

  • Cho, J. Y. N., 2005: Multi-PRI signal processing for the Terminal Doppler Weather Radar. Part II: Range–velocity ambiguity mitigation. J. Atmos. Oceanic Technol., 22, 15071519, doi:10.1175/JTECH1805.1.

    • Search Google Scholar
    • Export Citation
  • Cho, J. Y. N., 2009: Moving clutter spectral filter for Terminal Doppler Weather Radar. 34th Conf. on Radar Meteorology, Williamsburg, VA, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/34Radar/techprogram/paper_155381.htm.]

  • Cho, J. Y. N., , and Chornoboy E. S. , 2005: Multi-PRI signal processing for the Terminal Doppler Weather Radar. Part I: Clutter filtering. J. Atmos. Oceanic Technol., 22, 575582, doi:10.1175/JTECH1730.1.

    • Search Google Scholar
    • Export Citation
  • Chornoboy, E. S., 1993: Clutter filter design for multiple-PRT signals. Preprints, 26th Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 235237. [Available online at http://www.ll.mit.edu/mission/aviation/publications/publication-files/ms-papers/Chornoboy_1993_RM_MS-10254_WW-18698.pdf.]

  • Chornoboy, E. S., , and Weber M. E. , 1994: Variable-PRI processing for meteorologic Doppler radars. Record of the 1994 IEEE National Radar Conference, IEEE, 8590. [Available online at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=328103.]

  • Ding, C., , Pei D. , , and Salomaa A. , 1996: Chinese Remainder Theorem: Applications in Computing, Coding, Cryptography. World Scientific Publishing, 213 pp.

    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., , and Zrnić D. S. , 1993: Doppler Radar and Weather Observations. Academic Press, 562 pp.

  • Kalogiros, J., 2012: Least squares reconstruction of Doppler radar spectra for irregular PRT. J. Atmos. Oceanic Technol., 29, 17441756, doi:10.1175/JTECH-D-12-00026.1.

    • Search Google Scholar
    • Export Citation
  • Michelson, M., , Shrader W. W. , , and Wieler J. G. , 1990: Terminal Doppler Weather Radar. Microwave J., 33, 139148.

  • Taylor, J. W., Jr., , and Brunins G. , 1985: Design of a new airport surveillance radar (ASR-9). Proc. IEEE, 73, 284289, doi:10.1109/PROC.1985.13139.

    • Search Google Scholar
    • Export Citation
  • Torres, S. M., , Dubel Y. F. , , and Zrnić D. S. , 2004: Design, implementation, and demonstration of a staggered PRT algorithm for the WSR-88D. J. Atmos. Oceanic Technol., 21, 13891399, doi:10.1175/1520-0426(2004)021<1389:DIADOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trunk, G., , and Brockett S. , 1993: Range and velocity ambiguity reduction. Record of the 1993 IEEE National Radar Conference, IEEE Aerospace and Electronic Systems Society, 146149.

  • Weber, M. E., 1989: Dual-beam autocorrelation based wind estimates from airport surveillance radar signals. MIT Lincoln Laboratory Project Rep. ATC-167, DOT Rep. DOT/FAA/PS-89/5, 60 pp. [Available online at http://www.ll.mit.edu/mission/aviation/publications/publication-files/atc-reports/Weber_1989_ATC-167_WW-15318.pdf.]

  • Weber, M. E., 2002: ASR-9 weather systems processor (WSP) signal processing algorithms. MIT Lincoln Laboratory Project Rep. ATC-255, 53 pp. [Available online at http://www.ll.mit.edu/mission/aviation/publications/publication-files/atc-reports/Weber_2002_ATC-255_WW-15318.pdf.]

  • Weber, M. E., , and Noyes T. A. , 1988: Low-altitude wind shear detection with airport surveillance radars: Evaluation of 1987 field measurements. MIT Lincoln Laboratory Project Rep. ATC-159, DOT Rep. DOT/FAA/PS-88/10, 120 pp. [Available online at http://www.ll.mit.edu/mission/aviation/publications/publication-files/atc-reports/Weber_1988_ATC-159_WW-15318.pdf.]

  • Weber, M. E., , and Stone M. L. , 1995: Low altitude wind shear detection using airport surveillance radars. IEEE Aerosp. Electron. Syst. Mag., 10, 39, doi:10.1109/62.387970.

    • Search Google Scholar
    • Export Citation
  • Weber, M. E., , Cullen J. A. , , Troxel S. W. , , and Meuse C. A. , 1996: ASR-9 weather system processor (WSP): Wind shear algorithms performance assessment. MIT Lincoln Laboratory Project Rep. ATC-247, 42 pp. [Available online at http://www.ll.mit.edu/mission/aviation/publications/publication-files/atc-reports/Weber_1996_ATC-247_WW-15318.pdf.]

  • Wilson, F. W., , and Gramzow R. H. , 1991: The redesigned low level wind shear alert system. Preprints, Fourth Int. Conf. on Aviation Weather Systems, Paris, France, Amer. Meteor. Soc., 24–26.

  • Zrnić, D. S., 1977: Mean power estimation with a recursive filter. IEEE Trans. Aerosp. Electron. Syst., AES-13, 281289, doi:10.1109/TAES.1977.308396.

    • Search Google Scholar
    • Export Citation
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Enhanced Signal Processing Algorithms for the ASR-9 Weather Systems Processor

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  • 1 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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