• Barton, D. K., 1988: Modern Radar System Analysis. Artech House, 612 pp.

  • Benner, W. E., Torok G. S. , Weber M. E. , and Emanuel M. , 2009: Progress of multifunction phased array radar (MPAR) program. Preprints, 25th Int. Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, San Antonio, TX, Amer. Meteor. Soc., 8B.3. [Available online at http://ams.confex.com/ams/pdfpapers/148009.pdf].

    • Search Google Scholar
    • Export Citation
  • Billeter, D. R., 1989: Multifunction Array Radar. Artech House, 216 pp.

  • Butler, J. M., 1998: Tracking and control in multi-function radar. Ph.D. thesis, University College London, 227 pp.

  • Capraro, G. T., Farina A. , Griffiths H. , and Wicks M. C. , 2006: Knowledge-based radar signal and data processing: A tutorial review. IEEE Signal Process. Mag., 23 , 1829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carbone, R. E., Carpenter M. J. , and Burghart C. D. , 1985: Doppler radar sampling limitations in convective storms. J. Atmos. Oceanic Technol., 2 , 357361.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crane, R. K., 1979: Automatic cell detection and tracking. IEEE Trans. Geosci. Electron., 17 , 250262.

  • Crum, T. D., Saffle R. E. , and Wilson J. W. , 1998: An update on the NEXRAD program and future WSR-88D support to operations. Wea. Forecasting, 13 , 253262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Forsyth, D. E., and Coauthors, 2005: Progress report on the National Weather Radar Testbed (phased-array) becomes operational. Preprints, 21st Int. Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, San Diego, CA, Amer. Meteor. Soc., 19.5. [Available online at http://ams.confex.com/ams/pdfpapers/85609.pdf].

    • Search Google Scholar
    • Export Citation
  • Gini, F., and Rangaswamy M. , 2008: Knowledge-Based Radar Detection, Tracking, and Classification. John Wiley and Sons, 265 pp.

  • Haykin, S., 2006: Cognitive radar: A way of the future. IEEE Signal Process. Mag., 23 , 3040.

  • Heinselman, P. L., Priegnitz D. L. , Manross K. L. , Smith T. M. , and Adams R. W. , 2008: Rapid sampling of severe storms by the National Weather Radar Testbed phased-array radar. Wea. Forecasting, 23 , 808824.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnston, J. T., MacKeen P. L. , Witt A. , Mitchell E. D. , Stumpf G. J. , Eilts M. D. , and Thomas K. W. , 1998: The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13 , 263276.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, R. R., and Steadham R. M. , 2004: WSR-88D algorithm comparisons of VCP 11 and new VCP 12. Preprints, 20th Int. Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Seattle, WA, Amer. Meteor. Soc., 12.7. [Available online at http://ams.confex.com/ams/pdfpapers/69402.pdf].

    • Search Google Scholar
    • Export Citation
  • Manners, D. M., 1990: ART an adaptive radar testbed. IEE Colloquium on Real-Time Management of Adaptive Radar Systems, London, UK, Siemens Plessey Radar, 1–7, [Available online at http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4907].

    • Search Google Scholar
    • Export Citation
  • McLaughlin, D., and Coauthors, 2009: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Amer. Meteor. Soc., 90 , 17971817.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miranda, S., Baker C. J. , Woodbridge K. , and Griffiths H. D. , 2006: Knowledge-based resource management for multifunction radar: A look at scheduling and task prioritization. IEEE Signal Process. Mag., 23 , 6676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miranda, S., Baker C. J. , Woodbridge K. , and Griffiths H. D. , 2007: Fuzzy logic approach for prioritisation of radar tasks and sectors of surveillance in multifunction radar. IET Radar Sonar Navig., 1 , 131141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Polger, P. D., Goldsmith B. S. , and Bocchierri R. C. , 1994: National Weather Service warning performance based on the WSR-88D. Bull. Amer. Meteor. Soc., 75 , 203214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., Richardon S. , Straka J. M. , Markowski P. M. , and Blanchard D. O. , 2000: The association of significant tornadoes with a baroclinic boundary on 2 June 1995. Mon. Wea. Rev., 128 , 174191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reinoso-Rondinel, R., Torres S. , and Yu T-Y. , 2010: Task prioritization on phased-array radar scheduler for adaptive weather sensing. Preprints, 26th Int. Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Atlanta, GA, Amer. Meteor. Soc., 14B.6. [Available online at http://ams.confex.com/ams/pdfpapers/164429.pdf].

    • Search Google Scholar
    • Export Citation
  • ROC, 2007: WSR-88D system specification. WSR-88D Radar Operations Center Rep. OWY55, 164 pp. [Available from NOAA FOIA Office, Public Reference Facility (OFA56), 1315 East West Hwy. (SSMC3), Room 10730, Silver Spring, MD 20910].

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., 1987: Objective method for analysis and tracking of convective cells as seen by radar. J. Atmos. Oceanic Technol., 4 , 422434.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sabatini, S., and Tarantino T. , 1994: Multifunction Array Radar: System Design and Analysis. Artech House, 271 pp.

  • Serafin, R. J., and Wilson J. W. , 2000: Operational weather radar in the United States: Progress and opportunity. Bull. Amer. Meteor. Soc., 81 , 501518.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, K. M., and Sutter D. , 2005: WSR-88D radar, tornado warning, and tornado casualties. Wea. Forecasting, 20 , 301310.

  • Skolnik, M. I., 2001: An Introduction to Radar Systems. McGraw-Hill, 772 pp.

  • Stafford, W. K., 1990: Real time control of multifunction electronically scanned adaptive radar (MESAR). Preprints, IEE Colloquium on Real Time Management of Adaptive Radar Systems, London, UK, Siemens Plessey Radar, 1–5. [Available online at http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4907].

    • Search Google Scholar
    • Export Citation
  • Steadham, R. M., 2008: 2008 National Weather Service field study. Part 1: Volume coverage pattern usage. Radar Operations Center, 28 pp. [Available from WSR-88D Radar Operations Center, 120 David L. Boren Blvd., Norman, OK 73072].

    • Search Google Scholar
    • Export Citation
  • Steadham, R. M., Brown R. A. , and Wood V. T. , 2002: Prospects for faster and denser WSR-88D scanning strategies. Preprints, 18th Int. Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Orlando, FL, Amer. Meteor. Soc., J3.16. [Available online at http://ams.confex.com/ams/annual2002/techprogram/paper_25318.htm].

    • Search Google Scholar
    • Export Citation
  • Torres, S. M., and Zrnić D. , 2003: Whitening in range to improve weather radar spectral moment estimates. Part I: Formulation and simulation. J. Atmos. Oceanic Technol., 20 , 14331448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torres, S. M., Curtis C. D. , and Cruz J. R. , 2004: Pseudowhitening of weather radar signals to improve spectral moment and polarimetric variable estimates at low signal-to-noise ratio. IEEE Trans. Geosci. Remote Sens., 42 , 941949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vannicola, V. C., Slaski L. K. , and Genello G. J. , 1993: Knowledge-based resource allocation for multifunction radars. Signal and Data Processing of Small Targets, V. C. Vannicola, L. K. Slaski, and G. J. Genello Jr., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 1954), 410–425.

    • Search Google Scholar
    • Export Citation
  • Wolfson, M., and Meuse C. A. , 1993: Quantifying airport terminal area weather surveillance requirements. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 47–49.

    • Search Google Scholar
    • Export Citation
  • Wood, V. T., and Chrisman J. N. , 2009: Impacts of the automated volume scan evaluation and termination (AVSET) on the WSR-88D velocity-azimuth display (VAD) wind profile (VWP). Preprints, 34th Conf. on Radar Meteorology, Williamsburg, VA, Amer. Meteor. Soc., P4.1. [Available online at http://ams.confex.com/ams/pdfpapers/154847.pdf].

    • Search Google Scholar
    • Export Citation
  • Wray, M., 1992: Software architecture for real time control of the radar beam within MESAR. Preprints, 92nd Int. Radar Conf., Brighton, UK, Siemens Plessey Systems, 38–41. [Available online at http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4767].

    • Search Google Scholar
    • Export Citation
  • Yu, T-Y., Orescanin M. B. , Curtis C. D. , Zrnić D. S. , and Forsyth D. E. , 2007: Beam multiplexing using the phased-array weather radar. J. Atmos. Oceanic Technol., 24 , 616626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., and Coauthors, 2007: Agile beam phased-array radar for weather observations. Bull. Amer. Meteor. Soc., 88 , 17531766.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 308 123 2
PDF Downloads 292 133 0

Multifunction Phased-Array Radar: Time Balance Scheduler for Adaptive Weather Sensing

View More View Less
  • 1 School of Electrical and Computer Engineering, and Atmospheric Radar Research Center, University of Oklahoma, Norman, Oklahoma
  • | 2 School of Electrical and Computer Engineering, and Atmospheric Radar Research Center, and Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
Restricted access

Abstract

Phased-array radars (PARs) have the capability of instantaneously and dynamically controlling beam position on a pulse-by-pulse basis, which allows a single radar to perform multiple functions, such as tracking multiple storms or weather and aviation surveillance. Moreover, these tasks can be carried out with different update times to achieve the goal of better characterizing and forecasting the storms of interest. However, these tasks usually compete for finite radar resources, and scheduling algorithms are often needed to address resource contention. To capitalize on the PAR capabilities, an algorithm based on the concept of time balance (TB) is developed for adaptive weather sensing. Two quality measures are introduced to quantify the gain of adaptive sensing relative to standard scanning patterns used by the Weather Surveillance Radar-1988 Doppler (WSR-88D). A simulation experiment is performed to demonstrate the advantages of adaptive sensing and to test and verify the performance of the TB scheduling algorithm. It is shown that the gain of adaptive sensing can be realized by the TB scheduler; that is, storms of interest can be revisited more frequently within a relatively short period time compared to conventional scanning.

Corresponding author address: Ricardo Reinoso-Rondinel, Room 5900, 120 David L. Boren Blvd., Atmospheric Radar Research Center, University of Oklahoma, Norman, OK 73072-7307. Email: rein3@ou.edu

Abstract

Phased-array radars (PARs) have the capability of instantaneously and dynamically controlling beam position on a pulse-by-pulse basis, which allows a single radar to perform multiple functions, such as tracking multiple storms or weather and aviation surveillance. Moreover, these tasks can be carried out with different update times to achieve the goal of better characterizing and forecasting the storms of interest. However, these tasks usually compete for finite radar resources, and scheduling algorithms are often needed to address resource contention. To capitalize on the PAR capabilities, an algorithm based on the concept of time balance (TB) is developed for adaptive weather sensing. Two quality measures are introduced to quantify the gain of adaptive sensing relative to standard scanning patterns used by the Weather Surveillance Radar-1988 Doppler (WSR-88D). A simulation experiment is performed to demonstrate the advantages of adaptive sensing and to test and verify the performance of the TB scheduling algorithm. It is shown that the gain of adaptive sensing can be realized by the TB scheduler; that is, storms of interest can be revisited more frequently within a relatively short period time compared to conventional scanning.

Corresponding author address: Ricardo Reinoso-Rondinel, Room 5900, 120 David L. Boren Blvd., Atmospheric Radar Research Center, University of Oklahoma, Norman, OK 73072-7307. Email: rein3@ou.edu

Save