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Mitigating peak side-lobe levels in pulse compression radar using classical orthogonal polynomials

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Abstract

Pulse compression radar is used to simultaneously avail the benefits of both short-duration and long-duration pulses. However, the matched filter output has high side-lobe levels along with the main-lobe. These undesired side-lobe levels should be diminished to avoid blind target detection at the radar receiver. This paper defines new radar waveforms to reduce peak side-lobe level (PSL) in pulse compression radar. The proposed waveforms are designed using classical orthogonal polynomials of different orders namely-Legendre, Associated Laguerre, and Chebyshev polynomials. The performance of various polynomials is examined by varying the optimizing parameter \(\gamma \) and the polynomial which offers maximum PSL reduction is chosen while maintaining constant envelope constraint. Further, the performance of the designed polynomial is observed on the delay-Doppler plane of radar ambiguity function (AF). Simulation outcomes show that the proposed 2nd order Chebyshev orthogonal polynomial (COP) gives higher PSL reduction and superior AF performance than the other polynomials and counterpart methods.

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References

  1. Skolnik, M. I. (2001). Introduction to Radar System (3rd ed.). McGraw-Hill.

    Google Scholar 

  2. Richards, M. A. (2014). Fundamentals of Radar Signal Processing. McGraw-Hill.

    Google Scholar 

  3. Blunt, S. D., & Mokole, E. L. (2016). Overview of radar waveform diversity. IEEE Transactions on Aerospace & Electronic System, 31(11), 2–42.

    Article  Google Scholar 

  4. 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 

  5. Kishore, T. R., & Rao, K. D. (2017). Automatic intrapulse modulation classification of advanced LPI radar waveforms. IEEE Transactions on Aerospace & Electronic System, 53(2), 901–914.

    Article  Google Scholar 

  6. Fan, W., Liang, J., Yu, G., So, H. C., & Guangshan, L. (2020). Minimum local peak sidelobe level waveform designs with correlation and/or spectral constraints. Signal Processing, 171, 107450.

    Article  Google Scholar 

  7. Gao, C., Teh, K. C., Liu, A., & Sun, H. (2016). Piecewise LFM waveform for MIMO radar. IEEE Transactions on Aerospace & Electronic System, 52(2), 590–602.

    Article  Google Scholar 

  8. 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 

  9. Xiao, L., Ling, K., Guolong, C., & Wei, Y. (2019). A low complexity coherent integration method for manoeuvring target detection. Digital Signal Processing, 49, 137–147.

    Google Scholar 

  10. Wang, Y. C., Dong, L., Xue, X., & Yi, K. C. (2012). On the design of constant modulus sequences with low correlation sidelobes levels. IEEE Communication Letters, 16(4), 462–465.

    Article  Google Scholar 

  11. Xianxiang, Y., Guolong, C., Zhang, T., & Kong, L. (2018). Constrained transmit beam pattern design for collocated MIMO radar. Signal Processing, 144, 145–154.

    Article  Google Scholar 

  12. Ping, P. P., Hui, L., Yixi, Z., Wei, Q., & Zhen, M. D. (2017). Range, radial velocity, and acceleration MLE using frequency modulation coded LFM pulse train. Digital Signal Processing, 60, 252–261.

    Article  Google Scholar 

  13. Zhao, K., Fangmin, H., Meng, J., Wu, H., & Zhang, L. (2021). Performance analysis of bit error rate of data link system under pulse LFM interference in time-varying rayleigh channel. Wireless Networks, 27, 1671–1681.

    Article  Google Scholar 

  14. Song, X., Willett, P., & Zhou, S. (2012). Range bias modeling for hyperbolic-frequency modulated waveforms in target tracking. IEEE Journal of Oceanic Engineering, 37(4), 670–679.

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Zhou, Y., & Jiang, T. (2013). Active point modification for sidelobe suppression with PAPR constraint in OFDM systems. Wireless Networks, 19, 1653–1663.

    Article  Google Scholar 

  17. 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 

  18. Thakur, A., & Saini, D. S. (2020). Bandwidth optimization and side-lobe levels reduction in PC radar using Legendre orthogonal polynomials. Digital Signal Processing, 101, 102705.

    Article  Google Scholar 

  19. Liu, X., Liu, J., Zhao, F., Xiaofeng, A., & Wang, G. (2017). An equivalent simulation method for pulse radar measurement in anechoic chamber. IEEE Geoscience & Remote Sensing Letters, 14(7), 1081–1085.

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Thakur, A., & Saini, D. S. (2020). Correlation processor based sidelobe suppression for polyphase codes in radar systems. Wireless Personal Communications, 115, 377–389.

    Article  Google Scholar 

  22. Zhang, Z., Ho, P. H., & Abdesselam, F. N. (2010). RADAR: A reputation-driven anomaly detection system for wireless mesh networks. Wireless Networks, 16, 2221–2236.

    Article  Google Scholar 

  23. 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 

  24. Sanandaji, N., & Soleimani, M. (2015). Pulse compression security enhancement as an electronic protection technique by exploiting a block cipher output as phase-code. IET Radar Sonar & Navigation, 4, 384–391.

    Article  Google Scholar 

  25. Najafabadi, H. E., Ataie, M., & Sabahi, M. F. (2017). Chebyshev chaotic polynomials for MIMO radar waveform generation. IET Radar Sonar & Navigation, 11(2), 330–340.

    Article  Google Scholar 

  26. Xu, C., Zhang, J., Zhou, Q., & Chen, S. (2019). Recognition of radar signals based on AF grids and geometric shape constraint. Signal Processing, 157, 30–44.

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  29. Zhen, L., Wenqiang, P., & Quan, L. Z. (2019). Minimax design of constant modulus MIMO waveforms for active sensing. IEEE Signal Processing Letters, 26(10), 1531–1535.

    Article  Google Scholar 

  30. Thakur, A., & Saini, D. S. (2021). MIMO radar sequence design with constant envelope and low correlation side-lobe levels. International Journal of Electronics & Communications, 136, 153769.

    Article  Google Scholar 

  31. Rius, M., Bolea, M., Mora, J., & Capmany, J. (2016). Chirped waveform generation with envelope reconfigurability for pulse compression radar. IEEE Photonics Technology Letters, 28(7), 748–751.

    Article  Google Scholar 

  32. Bolhasani, M., Mehrshahi, E., Ghorashi, S. A., & Alijani, M. A. (2019). Constant envelope waveform design to increase range resolution and SINR in correlated MIMO radar. Signal Processing, 163, 59–65.

    Article  Google Scholar 

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Correspondence to Davinder Singh Saini.

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Thakur, A., Saini, D.S. Mitigating peak side-lobe levels in pulse compression radar using classical orthogonal polynomials. Wireless Netw 28, 2889–2899 (2022). https://doi.org/10.1007/s11276-022-03002-z

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  • DOI: https://doi.org/10.1007/s11276-022-03002-z

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