Performance enhancement of MPM DoA estimation technique using wavelet De-noising filter

  • Authors

    • Amr Hussein Tanta University
    • Sameh Napoleon Tanta University
    • Haidy Eldawy Tanta University
    2016-06-28
    https://doi.org/10.14419/ijet.v5i3.6126
  • Direction of Arrival (DoA), wavelet de-noising, signal-to-noise ratio (SNR), wavelet de-noising matrix pencil method (WDMPM)
  • Direction-of-arrival (DoA) estimation is now an imperative part in many radar applications and localization techniques. There are numerous algorithms that have been studied in the previous decades for DoA, for example: MUSIC, ESPRIT, and Matrix Pencil Method (MPM), which are subspace super resolution methods. MPM is one of the most commonly used subspace based techniques. It is generally utilized for DoA estimation because of its effortlessness and high resolution contrasted with other subspace techniques. But, it suffers from performance deterioration under low Signal-to-Noise Ratio (SNR) conditions. This paper, explores the possibility of utilizing the wavelet de-noising technique to intercept the degradation in the performance of MPM under different SNR values. Wavelet De-noising is intended to remove noise or distortion from signals while retaining the original quality of the signal. The simulation results indicate that the Daubechies wavelet (db12) at 5 levels of decomposition is the most suitable wavelet for de-noising the signals under test. Also, the results show that the proposed wavelet de-noising matrix pencil method (WDMPM) outperforms the traditional MPM.

    Performance Enhancement of MPM DoA Estimation Technique using Wavelet De-noising Filter
  • References

    1. [1] Nuri Yilmazer, Tapan K Sarkar, and Magdalena Salazar-Palma. Doa estimation using matrix pencil and esprit methods using single and multiple snapshots. In Electromagnetic Theory (EMTS), 2010 URSI International Symposium on, pages 215–218. IEEE, 2010.

      [2] Muhammad Faisal Khan. Improved Matrix Pencil Methods for Parameters Estimation of Plane Wave Signals. PhD thesis, Pakistan Institute of Engineering & Applied Sciences, 2011.

      [3] Salvatore Calcagno, Fabio La Foresta, and Mario Versaci. Independent component analysis and discrete wavelet transform for artifact removal in biomedical signal processing. American Journal of Applied Sciences, 11(1):57, 2014.

      [4] Priyanka Khatwani and Archana Tiwari. A survey on different noise removal techniques of eeg signals. International Journal of Advanced Research in Computer and Communication Engineering, 2(2):1091–1095, 2013.

      [5] Gyanendra Singh, Kiran Savita, Shivkumar Yadav, and Vaibhav Purwar. Design of adaptive noise canceller using lms algorithm. International Journal of Advanced Technology & Engineering Research (IJATER), 3(3):85–89, 2013.

      [6] Yanbo Xue, JinkuanWang, and Zhigang Liu. A novel improved music algorithm by wavelet denoising in spatially correlated noises. In Communications and Information Technology, 2005. ISCIT 2005. IEEE International Symposium on, volume 1, pages 515–518. IEEE, 2005.

      [7] XJ Mao and HH Pan. An improved doa estimation algorithm based on wavelet operator. Journal of Communications, 8(12), 2013.

      [8] David L Donoho and Jain M Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3):425–455, 1994.

      [9] David L Donoho. De-noising by soft-thresholding. Information Theory, IEEE Transactions on, 41(3):613–627, 1995.

      [10] Jie Zhang, Dengshan Huang, Ping Huang, and Jun Kang. Estimation doas of the coherent sources based on svd. In Signal Processing Systems (ICSPS), 2010 2nd International Conference on, volume 3, pages V3–174. IEEE, 2010.

      [11] Hongliang Duan, Dengshan Huang, Liyang Zhou, Haihua Chen, and Jinglin Shi. A decorrelation algorithmbased on virtual array extension. In Communications and Networking in China (CHINACOM), 2012 7th International ICST Conference on, pages 348–351. IEEE, 2012.

      [12] Nuri Yilmazer, Raul Fernandez-Recio, and Tapan K Sarkar. Matrix pencil method for simultaneously estimating azimuth and elevation angles of arrival along with the frequency of the incoming signals. Digital Signal Processing, 16(6):796–816, 2006.

      [13] Liwei Jing, Pei Liang, Cao Maoyong, and Sun Nongliang. Super-resolution time of arrival estimation for indoor geolocation based on ieee 802.11 a/g. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, pages 6612–6615. IEEE, 2008.

      [14] Ying-Chang Liang, Yonghong Zeng, Edward CY Peh, and Anh Tuan Hoang. Sensing-throughput tradeoff for cognitive radio networks. Wireless Communications, IEEE Transactions on, 7(4):1326–1337, 2008.

      [15] Jean-Paul G Gallaire and Akbar M Sayeed. Wavelet-based empirical wiener ï¬ltering. In Time-Frequency and Time-Scale Analysis, 1998. Proceedings of the IEEE-SP International Symposium on, pages 641–644. IEEE, 1998.

      [16] Rami Cohen. Signal denoising using wavelets. Project Report, Department of Electrical Engineering Technion, Israel Institute of Technology, Haifa, 2012.

      [17] Ramesh Kumar and Prabhat Patel. Signal denoising with interval dependent thresholding using dwt and swt. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN, I:2278–3075, 2012.

      [18] Zhao Hong-tu and Yan Jing. The wavelet decomposition and reconstruction based on the matlab. In Proc. of the Third Int. Symposium on Electronic Commerce and Security Workshops (ISECS 2010), China, 2010.

      [19] Robert J Barsanti and Jordon Gilmore. Comparing noise removal in the wavelet and fourier domains. In System Theory (SSST), 2011 IEEE 43rd Southeastern Symposium on, pages 163–167. IEEE, 2011.

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    Hussein, A., Napoleon, S., & Eldawy, H. (2016). Performance enhancement of MPM DoA estimation technique using wavelet De-noising filter. International Journal of Engineering & Technology, 5(3), 66-69. https://doi.org/10.14419/ijet.v5i3.6126