Vol. 87
Latest Volume
All Volumes
PIERL 123 [2024] PIERL 122 [2024] PIERL 121 [2024] PIERL 120 [2024] PIERL 119 [2024] PIERL 118 [2024] PIERL 117 [2024] PIERL 116 [2024] PIERL 115 [2024] PIERL 114 [2023] PIERL 113 [2023] PIERL 112 [2023] PIERL 111 [2023] PIERL 110 [2023] PIERL 109 [2023] PIERL 108 [2023] PIERL 107 [2022] PIERL 106 [2022] PIERL 105 [2022] PIERL 104 [2022] PIERL 103 [2022] PIERL 102 [2022] PIERL 101 [2021] PIERL 100 [2021] PIERL 99 [2021] PIERL 98 [2021] PIERL 97 [2021] PIERL 96 [2021] PIERL 95 [2021] PIERL 94 [2020] PIERL 93 [2020] PIERL 92 [2020] PIERL 91 [2020] PIERL 90 [2020] PIERL 89 [2020] PIERL 88 [2020] PIERL 87 [2019] PIERL 86 [2019] PIERL 85 [2019] PIERL 84 [2019] PIERL 83 [2019] PIERL 82 [2019] PIERL 81 [2019] PIERL 80 [2018] PIERL 79 [2018] PIERL 78 [2018] PIERL 77 [2018] PIERL 76 [2018] PIERL 75 [2018] PIERL 74 [2018] PIERL 73 [2018] PIERL 72 [2018] PIERL 71 [2017] PIERL 70 [2017] PIERL 69 [2017] PIERL 68 [2017] PIERL 67 [2017] PIERL 66 [2017] PIERL 65 [2017] PIERL 64 [2016] PIERL 63 [2016] PIERL 62 [2016] PIERL 61 [2016] PIERL 60 [2016] PIERL 59 [2016] PIERL 58 [2016] PIERL 57 [2015] PIERL 56 [2015] PIERL 55 [2015] PIERL 54 [2015] PIERL 53 [2015] PIERL 52 [2015] PIERL 51 [2015] PIERL 50 [2014] PIERL 49 [2014] PIERL 48 [2014] PIERL 47 [2014] PIERL 46 [2014] PIERL 45 [2014] PIERL 44 [2014] PIERL 43 [2013] PIERL 42 [2013] PIERL 41 [2013] PIERL 40 [2013] PIERL 39 [2013] PIERL 38 [2013] PIERL 37 [2013] PIERL 36 [2013] PIERL 35 [2012] PIERL 34 [2012] PIERL 33 [2012] PIERL 32 [2012] PIERL 31 [2012] PIERL 30 [2012] PIERL 29 [2012] PIERL 28 [2012] PIERL 27 [2011] PIERL 26 [2011] PIERL 25 [2011] PIERL 24 [2011] PIERL 23 [2011] PIERL 22 [2011] PIERL 21 [2011] PIERL 20 [2011] PIERL 19 [2010] PIERL 18 [2010] PIERL 17 [2010] PIERL 16 [2010] PIERL 15 [2010] PIERL 14 [2010] PIERL 13 [2010] PIERL 12 [2009] PIERL 11 [2009] PIERL 10 [2009] PIERL 9 [2009] PIERL 8 [2009] PIERL 7 [2009] PIERL 6 [2009] PIERL 5 [2008] PIERL 4 [2008] PIERL 3 [2008] PIERL 2 [2008] PIERL 1 [2008]
2019-08-30
Directional Adaptive MUSIC-Like Algorithm Under Symmetric α-Stable Distributed Noise
By
Progress In Electromagnetics Research Letters, Vol. 87, 29-37, 2019
Abstract
An algorithm named MUSIC-like algorithm was previously proposed as an alternative method to the MUltiple SIgnal Classification (MUSIC) algorithm for direction-of-arrival (DOA) estimation. Without requiring explicit model order estimation, it was shown to have robust performance particularly in low signal-to-noise ratio (SNR) scenarios. In this letter, the working principle of a relaxation parameter β, a parameter which was introduced into the formulation of the MUSIC-like algorithm, is provided based on geometrical interpretation. To illustrate its robustness, the algorithm will be examined under symmetric α-stable distributed noise environment. An adaptive framework is then developed and proposed in this letter to further optimize the algorithm. The proposed adaptive framework is compared with the original MUSIC-like, MUSIC, FLOM-MUSIC, and SSCM-MUSIC algorithms. A notable improvement in terms of targets resolvability of the proposed method is observed under different impulse noise scenarios as well as different SNR levels.
Citation
Narong Borijindargoon, and Boon Ng, "Directional Adaptive MUSIC-Like Algorithm Under Symmetric α-Stable Distributed Noise," Progress In Electromagnetics Research Letters, Vol. 87, 29-37, 2019.
doi:10.2528/PIERL19062605
References

1. Nikias, C. L. and M. Shao, Signal Processing with Alpha-stable Distributions and Applications, Wiley-Interscience, New York, N.Y., 1995.

2. Schmidt, R. O., "Multiple emitter location and signal parameter estimation," IEEE Trans. Antennas Propag., Vol. 34, No. 3, 276-280, Mar. 1986.
doi:10.1109/TAP.1986.1143830

3. Kaveh, M. and A. Barabell, "The statistical performance of the MUSIC and the Minimum-Norm algorithms in resolving plane waves in noise," IEEE Trans. Acoust., Speech, Signal Process., Vol. 34, No. 2, 331-341, Apr. 1986.
doi:10.1109/TASSP.1986.1164815

4. Patole, S. M., M. Torlak, D. Wang, and M. Ali, "Automotive radars: A review of signal processing techniques," IEEE Signal Process. Mag., Vol. 34, No. 2, 22-35, Mar. 2017.
doi:10.1109/MSP.2016.2628914

5. Wan, L., X. Kong, and F. Xia, "Joint range-doppler-angle estimation for intelligent tracking of moving aerial targets," IEEE Internet Things J., Vol. 5, No. 3, 1625-1636, Jun. 2018.
doi:10.1109/JIOT.2017.2787785

6. Kim, J. M., O. K. Lee, and J. C. Ye, "Compressive MUSIC: Revisiting the link between compressive sensing and array signal processing," IEEE Trans. Inf. Theory, Vol. 58, No. 1, 278-301, Jan. 2012.
doi:10.1109/TIT.2011.2171529

7. Davies, M. E. and Y. C. Eldar, "Rank awareness in joint sparse recovery," IEEE Trans. Inf. Theory, Vol. 58, No. 2, 1135-1146, Feb. 2012.
doi:10.1109/TIT.2011.2173722

8. Lee, K., Y. Bresler, and M. Junge, "Subspace methods for joint sparse recovery," IEEE Trans. Inf. Theory, Vol. 58, No. 6, 3613-3641, Jun. 2012.
doi:10.1109/TIT.2012.2189196

9. Lee, O., J. Kim, Y. Bresler, and J. C. Ye, "Diffuse optical tomography using generalized MUSIC algorithm," IEEE Int. Symp. Biomed. Imag., 1142-1145, Jun. 2011.

10. Scholz, B., "Towards virtual electrical breast biopsy: Space-frequency MUSIC for trans-admittance data," IEEE Trans. Med. Imag., Vol. 21, No. 6, 588-595, Jun. 2002.
doi:10.1109/TMI.2002.800609

11. Borijindargoon, N., B. P. Ng, and S. Rahardja, "MUSIC-like algorithm for source localization in electrical impedance tomography," IEEE Trans. Ind. Electron, Vol. 66, No. 6, 4661-4671, Jun. 2019.
doi:10.1109/TIE.2018.2863196

12. Adali, T. and S. S. Haykin, Adaptive Signal Processing: Next Generation Solutions, John Wiley & Sons, Hoboken, New Jersey, 2010.
doi:10.1002/9780470575758

13. Liu, T. H. and J. M. Mendel, "A subspace-based direction finding algorithm using fractional lower order statistics," IEEE Signal Process. Mag., Vol. 49, No. 8, 1605-1613, Aug. 2001.
doi:10.1109/78.934131

14. Tsakalides, P. and C. L. Nikias, "The robust covariation-based MUSIC (ROC-MUSIC) algorithm for bearing estimation in impulsive noise environments," IEEE Signal Process. Mag., Vol. 44, No. 7, 1623-1633, Jul. 1996.
doi:10.1109/78.510611

15. Visuri, S., H. Oja, and V. Koivunen, "Subspace-based direction-of-arrival estimation using nonparametric statistics," IEEE Signal Process. Mag., Vol. 49, No. 9, 2060-2073, Sept. 2001.
doi:10.1109/78.942634

16. Ying, Z. and B. P. Ng, "MUSIC-like DOA estimation without estimating the number of sources," IEEE Trans. Signal Process., Vol. 58, No. 3, 1668-1676, Mar. 2010.
doi:10.1109/TSP.2009.2037074

17. Reddy, V. V., B. P. Ng, and A. W. H. Khong, "Insights into MUSIC-like algorithm," IEEE Trans. Signal Process., Vol. 61, No. 10, 2551-2556, May 2013.
doi:10.1109/TSP.2013.2251337

18. Lim, H. S., B. P. Ng, and V. V. Reddy, "Generalized MUSIC-Like array processing for underwater environments," IEEE J. Ocean. Eng., Vol. 42, No. 1, 124-134, Jan. 2017.

19. Capon, J., "High-resolution frequency-wavenumber spectrum analysis," Proceedings of the IEEE, Vol. 57, No. 8, 1408-1418, Aug. 1969.
doi:10.1109/PROC.1969.7278