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Compressed Sensing Based Mixed Noise Cancellation in Passive Bistatic Radar

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Intelligent Computing Paradigm and Cutting-edge Technologies (ICICCT 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 9))

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Abstract

This paper proposes a unique compressed sensing based pathway to improve mixed noise cancellation in Passive Bistatic Radar (PBR). Mixed noise is considered as Additive White Gaussian Noise (AWGN) including Impulse Noise (IN). The proposed technique applies a best sparsifying basis that adapts to the structure of the problem and reduces the size of the measurement matrix drastically. According to simulation results, it has been confirmed that the proposed system gives higher state estimation capabilities as compared to the conventional LMS filtering techniques. Without loss of generality, the testing of the performance metric has been done over the FM signals. The paper explains the simulation methodology and the details.

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Author information

Authors and Affiliations

  1. Department of ECE, University College of Engineering, Osmania University, Hyderabad, Telangana, India

    D. Venu

  2. Department of ECE, Chaitanya Bharathi Institute of Technology and Science, Hyderabad, Telangana, India

    N. V. Koteswara Rao

Authors
  1. D. Venu
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  2. N. V. Koteswara Rao
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Corresponding author

Correspondence to D. Venu .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, Australia

    Lakhmi C. Jain

  2. CSIE Department, National Dong Hwa University, New Taipei City, Taiwan

    Sheng-Lung Peng

  3. Al-Balqa’ Applied University, Salt, Jordan

    Basim Alhadidi

  4. Department of Computer Science, Brainware University, Kolkata, West Bengal, India

    Souvik Pal

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Venu, D., Koteswara Rao, N.V. (2020). Compressed Sensing Based Mixed Noise Cancellation in Passive Bistatic Radar. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_39

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