Adaptive spectral processing algorithm for staggered signals in weather radars

Arturo Collado Rosell; Juan Pablo Pascual; Javier Areta

Adaptive spectral processing algorithm for staggered signals in weather radars

View Fulltext

Author(s): Arturo Collado Rosell^{1, 2} ; Juan Pablo Pascual^{1, 2, 3} ; Javier Areta^{3, 4}
- Affiliations: 1: Instutito Balseiro - Universidad Nacional de Cuyo , Av. Bustillo, 9500, S.C. Bariloche , Argentina ;
  2: LIAT, Dto. de Ingeniería en Telecomunicaciones, GDTyPE, GAIyANN, CNEA , Av. Bustillo, 9500, S.C. Bariloche , Argentina ;
  3: Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Argentina ;
  4: Universidad Nacional de Río Negro , Anasagasti, 1463, S.C. Bariloche , Argentina
Source: Volume 14, Issue 11, November 2020, p. 1659 – 1670
DOI: 10.1049/iet-rsn.2020.0095 , Print ISSN 1751-8784, Online ISSN 1751-8792

Received 21/02/2020, Accepted 02/06/2020, Revised 01/06/2020, Published 03/06/2020

A spectral algorithm for processing staggered-pulse repetition time (SPRT) signals in weather radar is introduced. It includes new approaches for ground clutter filter and hydrometeor spectral moments estimation. The algorithm uses ideas similar to GMAP but applied to non-uniform sampled signals. This work is focused on staggered sequences 2/3, but can be extended to other staggered sequences. Monte Carlo experiments were used to evaluate the performance of the spectral moments estimators for simulated weather signal, in scenarios with and without the presence of ground clutter. When clutter is present, a study using different clutter-to-signal ratios was carried out, showing that the method can deal with a wide range of situations and is appropriate for implementation in real scenarios. A comparison against GMAP-TD was performed, showing similar estimation results for both algorithms and a fivefold processing speed improvement for the proposed method. The performance was also validated using real weather data RMA-12 from a radar located in San Carlos de Bariloche, Argentina. The proposed algorithm has an easy implementation and is a good candidate for real-time implementations.

References

1. 1)
  - 10. Sachidananda, M., Zrnic, D.: ‘An improved clutter filtering and spectral moment estimation algorithm for staggered PRT sequences’, J. Atmos. Oceanic Technol., 2002, 19, (12), pp. 2009–2019.
2. 2)
  - 29. Sachidananda, M., Zrnic, D.: ‘Ground clutter filtering dual-polarized, staggered PRT sequences’, J. Atmos. Oceanic Technol., 2006, 23, pp. 1114–1130.
3. 3)
  - 12. Moisseev, D.N., Nguyen, C.M., Chandrasekar, V.: ‘Clutter suppression for staggered PRT waveforms’, J. Atmos. Oceanic Technol., 2008, 25, (12), pp. 2209–2218.
4. 4)
  - 24. Welch, D.P.: ‘The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms’, IEEE Trans. Audio Electroacoust., 1967, AU-15, pp. 70–73.
5. 5)
  - 5. Groginsky, H.L., Glover, K.M.: ‘Weather radar canceller design’. 19th Conf. on Radar Meteorology, Miami Beach, Fl, 1980, pp. 192–198.
6. 6)
  - 27. Zrnić, D.S.: ‘Simulation of weather like Doppler spectra and signals’, J. Appl. Meteor., 1975, 14, (4), pp. 619–620.
7. 7)
  - 13. Nguyen, C.M., Chandrasekar, V.: ‘Gaussian model adaptive processing in time domain (GMAP-TD) for weather radars’, J. Atmos. Oceanic Technol., 2013, 30, pp. 2571–2584.
8. 8)
  - 7. Bachmann, S.M.: ‘Using the existing spectral clutter filter with the nonuniformly spaced time series data in weather radar’, IEEE Geosci. Remote Sens. Lett., 2008, 5, (3), pp. 400–403.
9. 9)
  - 19. Jenq, Y.C.: ‘Perfect reconstruction of digital spectrum from nonuniformly sampled signals’, IEEE Trans. Instrum. Meas., 1997, 46, (3), pp. 649–652.
10. 10)
  - 11. Torres, S., Bachmann, S., Zrnic, D.: ‘Signal design and processing techniques for WSR-88D ambiguity resolution – part 11: staggered PRT and updates to SZ-2 algorithm’, NOAA, National Severe Storms Laboratory, Norman, OK, 2007.
11. 11)
  - 21. Fang, M., Doviak, R.J., Melnikov, V.: ‘Spectrum width measured by WSR-88D: error sources and statistics of various weather phenomena’, J. Atmos. Oceanic Technol., 2004, 21, (6), pp. 888–904.
12. 12)
  - 4. Sachidananda, M., Zrnić, D.: ‘Clutter filtering and spectral moment estimation for Doppler weather radars using staggered pulse repetition time (PRT)’, J. Atmos. Oceanic Technol., 2000, 17, (3), pp. 323–331.
13. 13)
  - 16. Torres, S.M., Warde, D.A.: ‘Ground clutter mitigation for weather radars using the autocorrelation spectral density’, J. Atmos. Oceanic Technol., 2014, 31, (10), pp. 2049–2066.
14. 14)
  - 1. Doviak, R.J., Zrnić, D.S.: ‘Doppler radar and weather observations’ (Dover, USA, 2014).
15. 15)
  - 15. Siggia, A.D., Passarelli, R.E.: ‘Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation’. Proc. 3erd ERAD, Visby, Sweden, 2004, pp. 67–73.
16. 16)
  - 30. Nguyen, C.M., Chandrasekar, V.: ‘Polarimetric variables retrieval with clutter suppression for staggered PRT sequences’, J. Atmos. Oceanic Technol., 2015, 32, pp. 767–782.
17. 17)
  - 9. Rosell, A.C., Areta, J., Pascual, J.P.: ‘Ground clutter filtering for weather radar using staggered pulse repetition time’. 2018 Argentinian Biennial Congress (ARGENCON), San Miguel de Tucumán, 2018, pp. 1–6.
18. 18)
  - 28. Rosell, A.C., Pascual, J.P., Areta, J.: ‘Spectrum width correction for clutter mitigation in weather radar’. 2019 XVIII Workshop on Information Processing and Control (RPIC), Bahía Blanca, 2019, pp. 135–139.
19. 19)
  - 17. Hubbert, J.C., Dixon, M., Ellis, S.M.: ‘Weather radar ground clutter. Part II: real-time identification and filtering’, J. Atmos. Oceanic Technol., 2009, 26, pp. 1181–1197.
20. 20)
  - 6. Banjanin, Z.B., Zrnic, D.S.: ‘Clutter rejection for Doppler weather radars which use staggered pulses’, IEEE Trans. Geosci. Remote Sens., 1991, 29, (4), pp. 610–620.
21. 21)
  - 18. Ice, R.L., Rhoton, R.D., Krause, J.C., et al: ‘Automatic clutter mitigation in the WSR-88D, design, evaluation, and implementation’. 34th Conf. Radar Meteorology. P5.3, Williamsburg, VA, 2009.
22. 22)
  - 26. Porat, B.: ‘A course in digital signal processing’ (John Wiley & Sons, Inc., USA, 1997).
23. 23)
  - 8. Meymaris, G., Hubbert, J., Dixon, M.: ‘Alternative approaches to staggered PRT clutter filtering’. 34th Conf. on Radar Meteorology. P5.21, Williamsburg, VA, 2009.
24. 24)
  - 20. Torres, S., S, M., Zrnic, D.: ‘Signal design and processing techniques for WSR-88D ambiguity resolution – part 9: phase coding and staggered PRT’, NOAA, National Severe Storms Laboratory, Norman, OK, 2005.
25. 25)
  - 14. Warde, D.A., Torres, S.M.: ‘The autocorrelation spectral density for Doppler-weather-radar signal analysis’, IEEE Trans. Geosci. Remote Sens., 2013, 52, (1), pp. 508–518.
26. 26)
  - 3. Tahanout, M., Adane, A.E.H., Parent du Châtelet, J.: ‘An improved M-PRT technique for spectral analysis of weather radar observations’, IEEE Trans. Geosci. Remote Sens., 2015, 53, (10), pp. 5572–5582.
27. 27)
  - 2. Zrinc, D., Mahapatra, P.: ‘Two methods of ambiguity resolution in pulse Doppler weather radars’, IEEE Trans. Aerosp. Electron. Syst., 1985, AES-21, (4), pp. 470–483.
28. 28)
  - 25. Torres, S.: ‘Range and velocity ambiguity mitigation on the WSR-88D: performance of the staggered PRT algorithm’. 22nd Int. Conf. on Interactive Information and Processing Systems for Meteorology, Oceanography and Hydrology 9.9, Atlanta, GA, 2006.
29. 29)
  - 23. Hubbert, J.C., Dixon, M., Ellis, S.M.: ‘Weather radar ground clutter. Part I: identification, modeling, and simulation’, J. Atmos. Oceanic Technol., 2009, 26, pp. 1165–1180.
30. 30)
  - 22. Warde, D.A., Torres, S.M.: ‘Automatic detection and removal of ground clutter contamination on weather radars’. 34th Conf. on Radar Meteorology, P10.11, Williamsburg, VA, 2009.

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Adaptive spectral processing algorithm for staggered signals in weather radars

References

Related content