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Correlation Processor Based Sidelobe Suppression for Polyphase Codes in Radar Systems

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Abstract

Peak sidelobe level (PSL), integrated sidelobe level (ISL) and signal to noise ratio (SNR) are the main performance indices in pulse compression radar. Pulse compression is useful in providing two contradicting requirements, namely, higher transmitted power required for longer pulse transmission, and smaller range resolution which is a desirable characteristic of smaller pulse transmission. The sidelobe suppression method proposed in this paper is based on the correlation processor that uses the P4 polyphase codes. P4 codes are known for their better Doppler tolerant property. Sidelobes are undesirable as jammers or noise present in sidelobes may interfere with the desired target. The proposed method has been implemented with six different windows namely Hamming, Hanning, Blackman, Nuttall, Blackman–Nuttall, and Blackman–Harris and results show that PSL value of − 275.6 dB and ISL value of − 152.98 dB are achieved without much degradation in SNR value. Ambiguity function is derived for the designed sequence and the results are compared with the Frank, Barker and P4 code in the delay-Doppler plane. Results show that the proposed method provides significant performance improvement compared to existing sidelobe suppression methods.

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References

  1. Skolnik, M. I. (2001). Introduction to radar system (3rd ed.). New York: McGraw-Hill.

    Google Scholar 

  2. Richards, M. A. (2014). Fundamentals of radar signal processing. New York: McGraw-Hill.

    Google Scholar 

  3. Saini, D. S., & Upadhyay, M. (2009). Multiple rake combiners and performance improvement in 3G and beyond WCDMA systems. IEEE Transactions on Vehicular Technology, 58(7), 3361–3370.

    Article  Google Scholar 

  4. Leilei, X., Shenghua, Z., & Hongwei, L. (2018). Simultaneous optimization of radar waveform and mismatched filter with range and delay-Doppler sidelobes suppression. Digital Signal Processing, 83, 346–358.

    Article  Google Scholar 

  5. Thakur, A., & Talluri, S. R. (2018). Comparative analysis on pulse compression with classical orthogonal polynomials for optimized time-bandwidth product. Ain Shams Engineering Journal, 9(4), 1791–1797.

    Article  Google Scholar 

  6. Jian, G., Shuntian, L., & Yiduo, G. (2019). A robust angle estimation method for bistatic MIMO radar about non-stationary random noise. Wireless Personal Communications, 106(2), 439–450.

    Article  Google Scholar 

  7. Mahafza, B. R. (2013). Radar systems analysis and design using MATLAB. Boca Raton: Chapman and Hall/CRC CRC Press LLC.

    MATH  Google Scholar 

  8. Atilio, G., Daniel, C., Jessica, S., & Monteiro, P. P. (2018). Research challenges, trends and applications for future joint radar communications systems. Wireless Personal Communications, 100(1), 81–96.

    Article  Google Scholar 

  9. Chengjie, L., Lidong, Z., Anhong, X., & Zhongqiang, L. (2017). Blind separation of weak object signals against the unknown strong jamming in communication systems. Wireless Personal Communications, 97(3), 4265–4283.

    Article  Google Scholar 

  10. Guodong, J., Yunkai, D., Robert, W., Wei, W., Yongwei, Z., Yajun, L., et al. (2019). Mitigating range ambiguities with advanced nonlinear frequency modulation waveform. IEEE Geoscience & Remote Sensing Letters, 16(8), 1230–1234.

    Article  Google Scholar 

  11. Guodong, J., Yunkai, D., Robert, W., Wei, W., Pei, W., Yajun, L., et al. (2019). An advanced nonlinear frequency modulation waveform for radar imaging with low sidelobe. IEEE Transactions on Geoscience & Remote Sensing, 57(8), 6155–6168.

    Article  Google Scholar 

  12. Ghavamirad, R., & Sebt, M. A. (2019). Sidelobe level reduction in ACF of NLFM waveform. IET Radar & Sonar Navigation, 13(1), 74–80.

    Article  Google Scholar 

  13. Joshi, A., & Saini, D. S. (2017). GA-PTS using novel mapping scheme for PAPR reduction of OFDM signals without SI. Wireless Personal Communications, 92(2), 639–651.

    Article  Google Scholar 

  14. Saini, D. S., & Balyan, V. (2016). An efficient multicode design for real time QoS support in OVSF based CDMA networks. Wireless Personal Communications, 90(4), 1799–1810.

    Article  Google Scholar 

  15. Thakur, A., & Talluri, S. R. (2017). A novel pulse compression technique for side-lobe reduction using woo filter concepts. In IEEE international conference communication on signal processing (pp. 1086–1090).

  16. Zakeri, B., Zahabi, M., & Alighale, S. (2012). Sidelobes level improvement by using a new scheme used in microwave pulse compression radars. Progress in Electromagnetics Research Letters, 30, 81–90.

    Article  Google Scholar 

  17. Alighale, S., & Zakeri, B. (2014). An excellent reduction in sidelobe level for P4 code by using of a new pulse compression scheme. International Journal of Electronics, 101(10), 1458–1466.

    Article  Google Scholar 

  18. Tao, Y., Bo, Q., Ying, W., Guoyong, W., Wenying, L., Lang, B., et al. (2018). Weighted discriminator function based unambiguous tracking method for dual-frequency constant envelope modulated signals. Wireless Personal Communications, 103(3), 1895–1925.

    Article  Google Scholar 

  19. Thakur, A., Talluri, S. R., & Panigrahi, R. K. (2019). Sidelobe reduction in pulse compression having better range resolution. Computers & Electrical Engineering, 74, 520–532.

    Article  Google Scholar 

  20. Zhang, L., Yang, B., & Luo, M. (2017). Joint delay and doppler shift estimation for multiple targets using exponential ambiguity function. IEEE Transactions on Signal Processing, 65(8), 2151–2163.

    Article  MathSciNet  MATH  Google Scholar 

  21. Farhan, Q. A., & Adly, T. F. (2015). Doppler tolerant and detection capable polyphase code sets. IEEE Transactions on Aerospace & Electronic System, 51(2), 1123–1135.

    Article  Google Scholar 

  22. Zhang, J., Shi, C., Qiu, X., & Wu, Y. (2016). Shaping radar ambiguity function by L-phase unimodular sequence. IEEE Sensors Journal, 16(14), 5648–5659.

    Article  Google Scholar 

  23. Lei, Z., Ming, L., Zheng, L., & Runqing, C. (2016). Range-spread target detection based on the matched ambiguity function. IET Radar Sonar & Navigation, 10(7), 1213–1219.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Chandigarh College of Engineering and Technology, Chandigarh, 160019, India

    Ankur Thakur & Davinder Singh Saini

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  1. Ankur Thakur
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  2. Davinder Singh Saini
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Correspondence to Davinder Singh Saini.

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Thakur, A., Saini, D.S. Correlation Processor Based Sidelobe Suppression for Polyphase Codes in Radar Systems. Wireless Pers Commun 115, 377–389 (2020). https://doi.org/10.1007/s11277-020-07576-9

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