The spinning, wide bandwidth antenna remains the most cost-effective technique for finding the direction of arrival of emitters’ signals. The beam is broadest at the low edge of the monitored band, resulting in poor angular resolution at low frequencies. A parameter-free angular super-resolution algorithm is proposed to find the direction of arrival of signals impinging on a spinning antenna based system that does not require tuning by the user. The proposed algorithm was constructed by using the sparse Bayesian learning technique. Using Monte Carlo simulation, the performance of the proposed algorithm is evaluated and shows that it outperforms the iterative adaptive approach algorithm.

References

1. 1)
  - 2. Zhang, Y., Zhang, Y., Huang, Y., et al: ‘ML iterative superresolution approach for real-beam radar’. IEEE Radar Conf., Speech and Signal, Cincinnati, OH, 2014, pp. 1192–1196.
2. 2)
  - 19. Wipf, D.P., Rao, B.D.: ‘Sparse Bayesian learning for basis selection’, IEEE Trans. Signal Process., 2004, 52, (8), pp. 2153–2164 (doi: 10.1109/TSP.2004.831016).
3. 3)
  - 13. Alibrahim, F., Inggs, M.: ‘Biased estimators for spinning antenna DOA measurements’, Trans. Aerosp. Electron. Syst., 2016, 52, (3), pp. 1499–1513 (doi: 10.1109/TAES.2016.150637).
4. 4)
  - 17. Uttam, S., Goodman, N.A.: ‘Superresolution of coherent sources in real-beam data’, IEEE Trans. Aerosp. Electron. Syst., 2010, 46, (3), pp. 1557–1566 (doi: 10.1109/TAES.2010.5545210).
5. 5)
  - 1. Zhang, Y., Zhang, Y., Li, W., et al: ‘Angular superresolution for real beam radar with iterative adaptive approach’. IEEE Int. Geoscience and Remote Sensing Symp., Melbourne, 2013, pp. 3100–3103.
6. 6)
  - 7. Lenti, F., Nunziata, F., Migliaccio, M., et al: ‘Two dimensional TSVD to enhance the spatial resolution of radiometer data’, Trans. Geosci. Remote. Sens., 2014, 52, (5), pp. 2450–2458 (doi: 10.1109/TGRS.2013.2261303).
7. 7)
  - 8. Lenti, F., Nunziata, F., Estatico, C., et al: ‘On the spatial resolution enhancement of microwave radiometer data in Banach spaces’, Trans. Geosci. Remote. Sens., 2014, 52, (3), pp. 1834–1842 (doi: 10.1109/TGRS.2013.2255614).
8. 8)
  - 4. Zhang, Y., Zha, Y., Yang, J., et al: ‘An improved Richardson-Lucy algorithm for radar angular super-resolution’. IEEE Radar Conf., Cincinnati, OH, 2014, pp. 406–410.
9. 9)
  - 5. Zhang, Y., Li, W., Zhang, Y., et al: ‘A fast iterative adaptive approach for scanning radar angular superresolution’, J. Sel. Top. Appl. Earth Observ. Remote Sens., 2015, 8, (11), pp. 5336–5345 (doi: 10.1109/JSTARS.2015.2449090).
10. 10)
  - 6. Guan, J., Huang, Y., et al: ‘Maximum a posterior estimation based angular superresolution for scanning radar imaging’, Trans. Aerosp. Electron. Syst., 2014, 50, (3), pp. 2389–2398 (doi: 10.1109/TAES.2014.120555).
11. 11)
  - 3. Wipf, D.P., Rao, B.D.: ‘An empirical Bayesian strategy for solving the simultaneous sparse approximation problem’, IEEE Trans. Signal Process., 2007, 55, pp. 3704–3716 (doi: 10.1109/TSP.2007.894265).
12. 12)
  - 12. Gini, F., Greco, M., Farina, A.: ‘Multiple radar targets estimation by exploiting induced amplitude modulation’, Trans. Aerosp. Electron. Syst., 2003, 39, (4), pp. 1316–1332 (doi: 10.1109/TAES.2003.1261131).
13. 13)
  - 10. Zhang, Y., Jakobsson, A., Yang, J.: ‘Range-recursive IAA for scanning radar angular super-resolution’, Geosci. Remote. Sens. Lett., 2017, 14, (10), pp. 1675–1679 (doi: 10.1109/LGRS.2017.2728038).
14. 14)
  - 9. Zhang, S., Wan, Q., Wang, H.: ‘DOA estimation in mechanical scanning radar systems using sparse signal reconstruction methods’. Int. Conf. Wireless Communications, Networking and Mobile Computing, Wuhan, 2011, pp. 1–4.
15. 15)
  - 11. Zhang, Y., Zhang, Y., Huang, Y., et al: ‘Angular superresolution for scanning radar with improved regularized iterative adaptive approach’, Geosci. Remote. Sens. Lett., 2016, 13, (6), pp. 846–850 (doi: 10.1109/LGRS.2016.2550491).

Sparse Bayesian learning for spinning antenna DOA super-resolution

References

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