Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery (2402.15681v1)

Published 24 Feb 2024 in cs.IT, eess.SP, and math.IT

Abstract: This paper investigates the problem of noncoherent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector (MMV) model for noncoherent DOA estimation based on a low-rank and sparse recovery optimization problem. Moreover, we develop two different practical strategies to obtain sparse arrays and subarrays: i) the subarrays are generated from a main sparse array geometry (Type-I sparse array), and ii) the sparse subarrays that are directly designed and grouped together to generate the whole sparse array (Type-II sparse array). Numerical results demonstrate that the proposed MMV model can benefit from multiple data records and that Type-II sparse noncoherent arrays are superior in performance for DOA estimation

Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. D. Malioutov, M. Çetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Transactions on Signal Processing, vol. 53, no. 8, pp. 3010–3022, 2005.
  2. Y. D. Zhang, M. G. Amin, and B. Himed, “Sparsity-based DOA estimation using co-prime arrays,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 3967–3971, 2013.
  3. P. Pal and P. P. Vaidyanathan, “A novel array structure for directions-of-arrival estimation with increased degrees of freedom,” in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, no. 2.   IEEE, 2010, pp. 2606–2609.
  4. L. Wang, R. C. de Lamare, and M. Haardt, “Direction finding algorithms based on joint iterative subspace optimization,” IEEE Transactions on Aerospace and Electronic Systems, vol. 50, no. 4, pp. 2541–2553, October 2014.
  5. S. F. B. Pinto and R. C. de Lamare, “Multistep knowledge-aided iterative esprit: Design and analysis,” IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 5, pp. 2189–2201, 2018.
  6. C.-L. Liu and P. P. Vaidyanathan, “Super Nested Arrays: Linear Sparse Arrays With Reduced Mutual Coupling—Part I: Fundamentals,” IEEE Transactions on Signal Processing, vol. 64, no. 15, pp. 3997–4012, aug 2016.
  7. C.-l. Liu and P. P. Vaidyanathan, “Super nested arrays: Sparse arrays with less mutual coupling than nested arrays,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, mar 2016, pp. 2976–2980. [Online]. Available: http://ieeexplore.ieee.org/document/7472223/
  8. W. S. Leite and R. C. de Lamare, “List-Based OMP and an Enhanced Model for DOA Estimation With Nonuniform Arrays,” IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 6, pp. 4457–4464, 2021.
  9. P. Pal and P. P. Vaidyanathan, “Nested arrays: A novel approach to array processing with enhanced degrees of freedom,” IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 4167–4181, 2010.
  10. ——, “Nested arrays in two dimensions, Part I: Geometrical Considerations,” IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4694–4705, 2012.
  11. L. Qiu, Y. Cai, R. C. de Lamare, and M. Zhao, “Reduced-rank doa estimation algorithms based on alternating low-rank decomposition,” IEEE Signal Processing Letters, vol. 23, no. 5, pp. 565–569, May 2016.
  12. L. Qiu, Y. Cai, R. C. de Lamare, and M. Zhao, “Reduced-Rank DOA Estimation Algorithms Based on Alternating Low-Rank Decomposition,” IEEE Signal Processing Letters, vol. 23, no. 5, pp. 565–569, may 2016. [Online]. Available: http://ieeexplore.ieee.org/document/7433376/
  13. X. Wang, Z. Yang, J. Huang, and R. C. de Lamare, “Robust two-stage reduced-dimension sparsity-aware stap for airborne radar with coprime arrays,” IEEE Transactions on Signal Processing, vol. 68, pp. 81–96, 2020.
  14. W. Shi, Y. Li, and R. C. de Lamare, “Novel sparse array design based on the maximum inter-element spacing criterion,” IEEE Signal Processing Letters, vol. 29, pp. 1754–1758, 2022.
  15. X. Sheng, D. Lu, Y. Li, and R. C. de Lamare, “Enhanced misc-based sparse array with high udofs and low mutual coupling,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 2, pp. 972–976, 2024.
  16. R. C. de Lamare and R. Sampaio-Neto, “Adaptive reduced-rank processing based on joint and iterative interpolation, decimation, and filtering,” IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2503–2514, 2009.
  17. R. Fa, R. C. de Lamare, and L. Wang, “Reduced-rank stap schemes for airborne radar based on switched joint interpolation, decimation and filtering algorithm,” IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 4182–4194, 2010.
  18. R. de Lamare and R. Sampaio-Neto, “Adaptive reduced-rank mmse filtering with interpolated fir filters and adaptive interpolators,” IEEE Signal Processing Letters, vol. 12, no. 3, pp. 177–180, 2005.
  19. Y. Cai, R. C. de Lamare, B. Champagne, B. Qin, and M. Zhao, “Adaptive reduced-rank receive processing based on minimum symbol-error-rate criterion for large-scale multiple-antenna systems,” IEEE Transactions on Communications, vol. 63, no. 11, pp. 4185–4201, 2015.
  20. R. C. de Lamare and R. Sampaio-Neto, “Reduced-rank adaptive filtering based on joint iterative optimization of adaptive filters,” IEEE Signal Processing Letters, vol. 14, no. 12, pp. 980–983, 2007.
  21. ——, “Reduced-rank space–time adaptive interference suppression with joint iterative least squares algorithms for spread-spectrum systems,” IEEE Transactions on Vehicular Technology, vol. 59, no. 3, pp. 1217–1228, 2010.
  22. ——, “Adaptive reduced-rank equalization algorithms based on alternating optimization design techniques for mimo systems,” IEEE Transactions on Vehicular Technology, vol. 60, no. 6, pp. 2482–2494, 2011.
  23. R. Fa and R. C. De Lamare, “Reduced-rank stap algorithms using joint iterative optimization of filters,” IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 3, pp. 1668–1684, 2011.
  24. R. de Lamare, “Adaptive reduced-rank lcmv beamforming algorithms based on joint iterative optimisation of filters,” Electronics Letters, vol. 44, pp. 565–567(2), April 2008. [Online]. Available: https://digital-library.theiet.org/content/journals/10.1049/el_20080627
  25. N. Song, R. C. de Lamare, M. Haardt, and M. Wolf, “Adaptive widely linear reduced-rank interference suppression based on the multistage wiener filter,” IEEE Transactions on Signal Processing, vol. 60, no. 8, pp. 4003–4016, 2012.
  26. N. Song, W. U. Alokozai, R. C. de Lamare, and M. Haardt, “Adaptive widely linear reduced-rank beamforming based on joint iterative optimization,” IEEE Signal Processing Letters, vol. 21, no. 3, pp. 265–269, 2014.
  27. W. Zhang, R. C. de Lamare, C. Pan, M. Chen, J. Dai, B. Wu, and X. Bao, “Widely linear precoding for large-scale mimo with iqi: Algorithms and performance analysis,” IEEE Transactions on Wireless Communications, vol. 16, no. 5, pp. 3298–3312, 2017.
  28. Z. Yang, R. C. de Lamare, and X. Li, “l1subscript𝑙1l_{1}italic_l start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT -regularized stap algorithms with a generalized sidelobe canceler architecture for airborne radar,” IEEE Transactions on Signal Processing, vol. 60, no. 2, pp. 674–686, 2012.
  29. L. Wang, “Constrained adaptive filtering algorithms based on conjugate gradient techniques for beamforming,” IET Signal Processing, vol. 4, pp. 686–697(11), December 2010. [Online]. Available: https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2009.0243
  30. Z. Yang, “Sparsity-aware space–time adaptive processing algorithms with l1-norm regularisation for airborne radar,” IET Signal Processing, vol. 6, pp. 413–423(10), July 2012. [Online]. Available: https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2011.0254
  31. Y. Zhaocheng, R. C. de Lamare, and W. Liu, “Sparsity-based stap using alternating direction method with gain/phase errors,” IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 6, pp. 2756–2768, 2017.
  32. S. D. Somasundaram, N. H. Parsons, P. Li, and R. C. de Lamare, “Reduced-dimension robust capon beamforming using krylov-subspace techniques,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 1, pp. 270–289, 2015.
  33. R. C. de Lamare and R. Sampaio-Neto, “Sparsity-aware adaptive algorithms based on alternating optimization and shrinkage,” IEEE Signal Processing Letters, vol. 21, no. 2, pp. 225–229, 2014.
  34. H. Ruan and R. C. de Lamare, “Robust adaptive beamforming using a low-complexity shrinkage-based mismatch estimation algorithm,” IEEE Signal Processing Letters, vol. 21, no. 1, pp. 60–64, 2014.
  35. ——, “Robust adaptive beamforming based on low-rank and cross-correlation techniques,” IEEE Transactions on Signal Processing, vol. 64, no. 15, pp. 3919–3932, 2016.
  36. ——, “Distributed robust beamforming based on low-rank and cross-correlation techniques: Design and analysis,” IEEE Transactions on Signal Processing, vol. 67, no. 24, pp. 6411–6423, 2019.
  37. S. Fortunati, R. Grasso, F. Gini, M. S. Greco, and K. LePage, “Single-snapshot DOA estimation by using Compressed Sensing,” EURASIP Journal on Advances in Signal Processing, no. 1, p. 120, 2014.
  38. Y. Pati, R. Rezaiifar, and P. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, vol. 1.   IEEE Comput. Soc. Press, 1993, pp. 40–44.
  39. T. Blumensath and M. E. Davies, “Iterative hard thresholding for compressed sensing,” Applied and Computational Harmonic Analysis, vol. 27, no. 3, pp. 265–274, 2009. [Online]. Available: http://dx.doi.org/10.1016/j.acha.2009.04.002
  40. S. Xu, R. C. de Lamare, and H. V. Poor, “Distributed compressed estimation based on compressive sensing,” IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1311–1315, 2015.
  41. J.-F. Gu, W.-P. Zhu, and M. Swamy, “Minimum redundancy linear sparse subarrays for direction of arrival estimation without ambiguity,” in 2011 IEEE International Symposium of Circuits and Systems (ISCAS).   IEEE, may 2011, pp. 390–393. [Online]. Available: http://ieeexplore.ieee.org/document/5937584/
  42. A. M. Elbir, S. Mulleti, R. Cohen, R. Fu, and Y. C. Eldar, “Deep-Sparse Array Cognitive Radar,” 2019 13th International Conference on Sampling Theory and Applications, SampTA 2019, pp. 1–4, 2019.
  43. C. M. S. See and A. B. Gershman, “Direction-of-arrival estimation in partly calibrated subarray-based sensor arrays,” IEEE Transactions on Signal Processing, vol. 52, no. 2, pp. 329–338, feb 2004.
  44. A. Swindlehurst, P. Stoica, and M. Jansson, “Exploiting arrays with multiple invariances using MUSIC and MODE,” IEEE Transactions on Signal Processing, vol. 49, no. 11, pp. 2511–2521, 2001.
  45. D. Rieken and D. Fuhrmann, “Generalizing music and mvdr for multiple noncoherent arrays,” IEEE Transactions on Signal Processing, vol. 52, pp. 2396–2406, 9 2004. [Online]. Available: http://ieeexplore.ieee.org/document/1323249/
  46. T. Tirer and O. Bialer, “Direction of arrival estimation for non-coherent sub-arrays via joint sparse and low-rank signal recovery,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2021-June, pp. 4395–4399, 2021.
  47. M. Grant and S. Boyd, “CVX: Matlab software for disciplined convex programming, version 2.2,” http://cvxr.com/cvx, Mar. 2020.
  48. A. Moffet, “Minimum-redundancy linear arrays,” IEEE Transactions on Antennas and Propagation, vol. 16, no. 2, pp. 172–175, mar 1968.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now