access icon free Low complexity portable MIMO radar system for the characterisation of complex environments at high resolution

This study presents the concept, design, and operation of a low-cost and lightweight multiple-input multiple-output (MIMO) radar system, dedicated to the characterisation of complex volumetric environments such as snow, forest, sand etc. using synthetic aperture radar tomographic focusing. The system consists of six transmitting and six receiving antennas, operated in a multi-static configuration, resulting in an equivalent mono-static vertical virtual array. Antenna positions have been set considering a trade-off between coupling effects, imaging ambiguities due to spectral folding and vertical resolution. The system also contains radio frequency signal generation and acquisition blocks and has been developed and operated at the IETR laboratory of the University of Rennes 1. An element-orthogonal frequency division multiplexing technique is proposed that avoids the use of switches for selecting transmission and reception channels, using a specific carrier for each transmit channel, and spectral analysis of each receive channel, allowing the 36 MIMO channels to be retrieved. The use of a time-domain back-projection algorithm for imaging in the elevation direction provides results that confirm the potential of the system for the characterisation of complex media.

Inspec keywords: receiving antennas; synthetic aperture radar; spectral analysis; wireless channels; radar signal processing; radar imaging; MIMO communication; MIMO radar; antenna arrays; OFDM modulation

Other keywords: element-orthogonal frequency division multiplexing technique; acquisition blocks; radio frequency signal generation; complex volumetric environments; antenna positions; low complexity portable MIMO radar system; 36 MIMO channels; receiving antennas; complex media; trade-off between coupling effects; multiple-input multiple-output radar system; transmit channel; complex environments; vertical resolution; equivalent mono-static; multistatic configuration; synthetic aperture radar tomographic focusing

Subjects: Radio links and equipment; Radar equipment, systems and applications; Modulation and coding methods; Optical, image and video signal processing; Other topics in statistics; Antenna arrays

References

    1. 1)
      • 11. Tebaldini, S.: ‘Single and multipolarimetric SAR tomography of forested areas: a parametric approach’, IEEE Trans. Geosci. Remote Sens., 2010, 48, (5), pp. 23752387.
    2. 2)
      • 29. Klare, J., Saalmann, O., Wilden, H., et al: ‘First experimental results with the imaging MIMO radar MIRA-CLE X’. 2010 8th European Conf. on Synthetic Aperture Radar (EUSAR) VDE, Aachen, Germany, 2010, pp. 14.
    3. 3)
      • 5. Ferretti, A., Prati, C., Rocca, F.: ‘Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry’, IEEE Trans. Geosci. Remote Sens., 2000, 38, (5), pp. 22022212.
    4. 4)
      • 26. Ulander, L.M.H., Monteith, A.R., Soja, M.J., et al: ‘Multiport vector network analyzer radar for tomographic forest scattering measurements’, IEEE Geosci. Remote Sens. Lett., 2018, 15, (12), pp. 18971901.
    5. 5)
      • 31. Kolmonen, V.-M., Kivinen, J., Vuokko, L., et al: ‘5.3-GHz MIMO radio channel sounder’, IEEE Trans. Instrum. Meas., 2006, 55, (4), pp. 12631269.
    6. 6)
      • 3. Bamler, R., Hartl, P.: ‘Synthetic aperture radar interferometry’, Inverse Probl., 1998, 14, (4), p. R1.
    7. 7)
      • 1. Massonnet, D., Souyris, J.C.: ‘Imaging with synthetic aperture radar’, in ‘Engineering sciences: electrical engineering’ (CRC Press, USA, 2008).
    8. 8)
      • 6. Ferretti, A., Prati, C., Rocca, F.: ‘Permanent scatterers in SAR interferometry’, IEEE Trans. Geosci. Remote Sens., 2001, 39, (1), pp. 820.
    9. 9)
      • 22. Yitayew, T.G., Ferro-Famil, L., Eltoft, T., et al: ‘Tomographic imaging of Fjord ice using a very high resolution ground-based SAR system’, IEEE Trans. Geosci. Remote Sens., 2016, 55, (2), pp. 698714.
    10. 10)
      • 2. Ulaby, F., Dobson, M.C., Álvarez-Pérez, J.L.: ‘Handbook of radar scattering statistics for terrain’ (Artech House, USA, 2019).
    11. 11)
      • 10. Reigber, A., Moreira, A.: ‘First demonstration of airborne SAR tomography using multibaseline l-band data’, IEEE Trans. Geosci. Remote Sens., 2000, 38, (5), pp. 21422152.
    12. 12)
      • 9. Ferro-Famil, L., Pottier, E.: ‘SAR imaging using coherent modes of diversity: SAR polarimetry, interferometry and tomography’ in Baghdadi, N., Zribi, M., et alMicrowave remote sensing of land surface’ (Elsevier, USA, 2016), pp. 67147.
    13. 13)
      • 12. Ferro-Famil, L., Huang, Y., Pottier, E.: ‘Principles and applications of polarimetric SAR tomography for the characterization of complex environments’. VIII Hotine-Marussi Symp. on Mathematical Geodesy, Switzerland, 2015, pp. 243255.
    14. 14)
      • 23. Yitayew, T.G., Ferro-Famil, L., Eltoft, T., et al: ‘Fjord ice imaging using a multifrequency ground-based tomographic SAR system'’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2017, 10, (10), pp. 44574468.
    15. 15)
      • 8. Ferro-Famil, L., Pottier, E.: ‘Radar polarimetry basics and selected earth remote sensing applications’ in Sidiropoulos, N.D., Gini, F., Chellappa, R., et al (Eds.): ‘Academic press library in signal processing’ (Elsevier, USA, 2014), 2, pp. 11191244.
    16. 16)
      • 34. Tonelli, G., Marchetti, F., Gargano, L., et al: ‘A DDS/PLL architecture for highly stable local oscillators’. 2014 Int. Radar Conf. (Radar), Lille, France, 2014, pp. 16.
    17. 17)
      • 33. Zhao, Z.Y., Li, X.Y., Chang, W. G.: ‘LFM–CW signal generator based on hybrid DDS–PLL structure’, Electron. Lett., 2013, 49, (6), pp. 391393.
    18. 18)
      • 20. Morrison, K., Bennett, J.: ‘Tomographic profiling – technique for multi-incidence-angle retrieval of the vertical SAR backscattering profiles of biogeophysical targets’, IEEE Trans. Geosci. Remote Sens., 2013, 52, (2), pp. 13501355.
    19. 19)
      • 19. Morrison, K., Rott, H., Nagler, T., et al: ‘The SARAlps-2007 measurement campaign on X- and Ku-band backscatter of snow’. 2007 IEEE Int. Geoscience and Remote Sensing Symp., Barcelona, Spain, 2007, pp. 12071210.
    20. 20)
      • 7. Cloude, S.R., Papathanassiou, K.P.: ‘Polarimetric SAR interferometry’, IEEE Trans. Geosci. Remote Sens., 1998, 36, (5), pp. 15511565.
    21. 21)
      • 13. Tebaldini, S., Nagler, T., Rott, H., et al: ‘Imaging the internal structure of an alpine glacier via l-band airborne SAR tomography’, IEEE Trans. Geosci. Remote Sens., 2016, 54, (12), pp. 71977209.
    22. 22)
      • 24. Dinh, H.T.M., Tebaldini, S., Rocca, F., et al: ‘Ground-based array for tomographic imaging of the tropical forest in p-band’, IEEE Trans. Geosci. Remote Sens., 2013, 51, (8), pp. 44604472.
    23. 23)
      • 27. Nagler, T., Rott, H.: ‘Retrieval of wet snow by means of multitemporal SAR data’, IEEE Trans. Geosci. Remote Sens., 2000, 38, (2), pp. 754765.
    24. 24)
      • 15. Ji, Y., Han, H., Lee, H.: ‘Construction and application of tomographic SAR system based on GB–SAR system’. 2014 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Québec City, QC, Canada, 2014, pp. 18911894.
    25. 25)
      • 30. Alkhateeb, A., Nam, Y.-H., Zhang, J., et al: ‘Massive MIMO combining with switches’, IEEE Wirel. Commun. Lett., 2016, 5, (3), pp. 232235.
    26. 26)
      • 35. Tebaldini, S., Rocca, F., Mariotti d'Alessandro, M., et al: ‘Phase calibration of airborne tomographic SAR data via phase center double localization’, IEEE Trans. Geosci. Remote Sens., 2015, 54, (3), pp. 17751792.
    27. 27)
      • 32. Im, Y.-T., Lee, J.-H., Park, S.-O.: ‘A DDS and PLL-based X-band FMCW radar system’. 2011 IEEE MTT-S Int. Microwave Workshop Series on Intelligent Radio for Future Personal Terminals (IMWSIRFPT), Daejeon, Republic of Korea, 2011, pp. 12.
    28. 28)
      • 17. Ferro-Famil, L., Tebaldini, S., Davy, M., et al: ‘3D SAR imaging of the snowpack at X- and Ku-band: results from the AlpSAR campaign’. EUSAR 2014; 10th European Conf. on Synthetic Aperture Radar VDE, Berlin, Germany, 2014, pp. 14.
    29. 29)
      • 18. Rekioua, B., Davy, M., Ferro-Famil, L.: ‘Snowpack characterization using SAR tomography-experimental results of the AlpSAR campaign’. 2015 European Radar Conf. (EuRAD), Paris, France, 2015, pp. 3336.
    30. 30)
      • 4. Ferretti, A., Monti-Guarnieri, A., Prati, C., et al: ‘InSAR principles – guidelines for SAR interferometry processing and interpretation’, ESA Train. Manual, 2007, 19, (1), pp. A3A40.
    31. 31)
      • 21. Frey, O., Werner, C.L., Wiesmann, A.: ‘Tomographic profiling of the structure of a snow pack at X-/Ku-band using SnowScat in SAR mode’. 2015 European Radar Conf. (EuRAD), Paris, France, 2015, pp. 2124.
    32. 32)
      • 25. Albinet, C., Koleck, T., Toan, T. L., et al: ‘First results of AfriScat, a tower-based radar experiment in African forest’. 2015 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Milan, Italy, 2015, pp. 53565358.
    33. 33)
      • 28. Lievens, H., Demuzere, M., Marshall, H.-P., et al: ‘Snow depth variability in the northern hemisphere mountains observed from space’, Nat. Commun., 2019, 10, (1), pp. 112.
    34. 34)
      • 14. Harkati, L., Avrillon, S., Ferro-Famil, L.: ‘C-band 2 × 2 MIMO multi-carrier tomographic radar for complex environment volumetric imaging’. 2018 IEEE Conf. on Antenna Measurements & Applications (CAMA), Västerås, Sweden, 2018, pp. 14.
    35. 35)
      • 16. Rekioua, B., Davy, M., Ferro-Famil, L., et al: ‘Snowpack permittivity profile retrieval from tomographic SAR data’, C. R. Phys., 2017, 18, (1), pp. 5765.
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