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Chaotic waveform for optimal joint radar communication systems

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  • Pappu, Chandra S.
  • Carroll, Thomas L.

Abstract

Due to the increased spectrum congestion, there has been a tremendous interest in radar and communication devices operating on a shared platform. Therefore, the waveform design for joint radar communication (JRC) systems has recently gained significant attention. Despite several efforts, most state-of-the-art waveforms are far from achieving ideal radar and communication performance. To address this issue, we design a family of chaotic maps whose output yields waveforms with optimal JRC features. We adopt the antipodal chaos shift keying (ACSK) scheme, where the segments of the chaotic signal are multiplied by ±1 to encode the binary information. This ACSK waveform is further used for the JRC system transmission. Once the synchronization between the receiver and transmitter is established, the information is recovered by cross-correlating the received and the synchronized waveforms. This technique yields bit error rates (BER) similar to the theoretical values of the BPSK waveform. The same transmitted waveform results in a thumb-tack ambiguity function which is essential for high-resolution radar imaging. The variance analysis of the ambiguity function shows that the proposed waveform is comparable to the noise-modulated waveforms. We also show that the multiplexed ACSK waveforms yield similar optimal behavior. Furthermore, we demonstrate the potential of the family of chaotic maps for multiple-input, multiple-output (MIMO) JRC systems.

Suggested Citation

  • Pappu, Chandra S. & Carroll, Thomas L., 2023. "Chaotic waveform for optimal joint radar communication systems," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:chsofr:v:169:y:2023:i:c:s0960077923001625
    DOI: 10.1016/j.chaos.2023.113261
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    References listed on IDEAS

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    1. Pappu, Chandra S. & Carroll, Thomas L., 2021. "Quasi-FM Waveform Using Chaotic Oscillator for Joint Radar and Communication Systems," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
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    Cited by:

    1. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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