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Detection of a Real Sinusoid in Noise using Differential Evolution Algorithm

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Harmony Search and Nature Inspired Optimization Algorithms

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 741))

Abstract

Detection of sinusoidal signals embedded in noise is a pertinent problem in applications such as radar and sonar, communication systems and defense, to name a few. This paper, describes the detection of a real sinusoid in additive white Gaussian noise (AWGN) using the Differential Evolution Algorithm (DE). The performance of DE is evaluated for different sampling rates and also for different signal-to-noise ratios (SNR). The proposed DE which combines two DE strategies enhances the detection performance compared to the original DE algorithm. We show that the detection performance of the proposed algorithm is superior to previously reported methods, especially at low SNR.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India

    Gayathri Narayanan

  2. Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Bangalore, India

    Dhanesh G. Kurup

Authors
  1. Gayathri Narayanan
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  2. Dhanesh G. Kurup
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Corresponding author

Correspondence to Gayathri Narayanan .

Editor information

Editors and Affiliations

  1. School of Engineering and Technology, BML Munjal University, Gurgaon, Haryana, India

    Neha Yadav

  2. Department of Sciences and Humanities, National Institute of Technology, Srinagar, Uttarakhand, India

    Anupam Yadav

  3. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

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Narayanan, G., Kurup, D.G. (2019). Detection of a Real Sinusoid in Noise using Differential Evolution Algorithm. In: Yadav, N., Yadav, A., Bansal, J., Deep, K., Kim, J. (eds) Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_8

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