Skip to main content
SpringerLink
Log in

Modifying Polyphase Codes to Mitigate Range Side-lobes in Pulse Compression Radar

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Range side-lobes suppression which is responsible for detection ambiguity is a challenging task in pulse compression (PC) radar. Targets generating weak signals tend to hide in undesired side-lobes creating ambiguity. In general, the side-lobes behaviour performance of received signals is observed by their correlation with other contemporary signals. In this paper, a new polyphase code is proposed that exhibits good auto-correlation and cross-correlation properties to reduce peak side-lobe level (PSL) and integrated side-lobe level (ISL). The proposed polyphase code named P43code is produced by combining left and right shifted versions of \(P4\) code. The amount of rotation depends upon the overall code length. The optimum rotation combination is the one that produces maximum side-lobes reduction. The proposed design is also applied to multiple-input multiple-output (MIMO) radar, where for the proposed \(P4_{3}\) code, orthogonal waveforms are used to minimize cross-interference. Using ambiguity function (AF), the impact of proposed code on the delay-Doppler plane is investigated. Simulation results demonstrate the superiority of the proposed \(P4_{3}\) code compared to existing alternatives.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

The data will made be available on reasonable request.

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. 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. 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 

  5. Guodong, J., Yunkai, D., Robert, W., Wei, W., Pei, W., Yajun, L., Zhimin, Z., & Zhang, Y. (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 

  6. 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 

  7. 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 

  8. 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 

  9. 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 

  10. 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 

  11. Wang, F., Pang, C., Li, Y., & Wang, X. (2018). Algorithms for designing unimodular sequences with high Doppler tolerance for simultaneous fully polarimetric radar. Sensors, 18(3), 905–914.

    Article  Google Scholar 

  12. 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 

  13. Bhamre, P., & Gupta, S. (2020). Constrained waveform designing for MIMO radar using Jaya optimization. Wireless Personal Communications, 111, 331–342.

    Article  Google Scholar 

  14. 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 

  15. Ge, P., Guolong, C., Karbasi, S. M., Kong, L., & Yang, J. (2016). A template fitting approach for cognitive unimodular sequence design. Signal Processing, 128, 360–368.

    Article  Google Scholar 

  16. 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 

  17. 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 

  18. Blunt, S. D., Jakabosky, J., Cook, M., Stiles, J., Seguin, S., & Mokole, E. L. (2014). Polyphase-coded FM (PCFM) radar waveforms, part II: Optimization. IEEE Transactions on Aerospace & Electronic System, 50(3), 2230–2241.

    Article  Google Scholar 

  19. He, H., Stoica, P., & Li, J. (2009). Designing unimodular sequence sets with good correlations; including an application to MIMO radar. IEEE Transactions on Signal Processing, 57(11), 4391–4405.

    Article  MathSciNet  Google Scholar 

  20. Kerahroodi, M. A., Aubry, A., Maio, A. D., Naghsh, M. M., & Hashemi, M. M. (2017). A coordinate-descent framework to design low PSL/ISL sequences. IEEE Transactions on Signal Processing, 65(22), 5942–5956.

    Article  MathSciNet  Google Scholar 

  21. 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 

  22. 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 

  23. 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 

  24. Leilei, X., Shanghai, 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 

  25. Najafabadi, H. E., Ataie, M., & Sabahi, M. F. (2017). Designing sequence with minimum PSL using Chebyshev distance and its application for chaotic MIMO radar waveform design. IEEE Transactions on Signal Processing, 65(3), 690–704.

    Article  MathSciNet  Google Scholar 

  26. Kerahroodi, M. A., Hashemi, M. M., & Naghsh, M. M. (2019). Designing sets of binary sequences for MIMO radar Systems. IEEE Transactions on Signal Processing, 67(13), 3347–3360.

    Article  MathSciNet  Google Scholar 

  27. 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 

  28. Cui, G., Li, H., & Rangaswamy, M. (2014). MIMO radar waveform design with constant modulus and similarity constraints. IEEE Transactions on Signal Processing, 62(2), 343–353.

    Article  MathSciNet  Google Scholar 

  29. Tan, P. S., Jakabosky, J., Stiles, J. M., & Blunt, S. D. (2019). Higher-order implementations of polyphase-coded FM radar waveforms. IEEE Transactions on Aerospace & Electronic System, 55(6), 2850–2870.

    Article  Google Scholar 

  30. 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 

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

    Article  Google Scholar 

  32. 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 

  33. 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 

  34. Zhe, X., Baixiao, C., & Minglei, Y. (2017). Transmitter polarization optimization with polarimetric MIMO radar for mainlobe interference suppression. Digital Signal Processing, 65, 19–26.

    Article  MathSciNet  Google Scholar 

  35. 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 

Download references

Author information

Authors and Affiliations

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

    Ankur Thakur & Davinder Singh Saini

Authors
  1. Ankur Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davinder Singh Saini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Davinder Singh Saini.

Ethics declarations

Conflicts of Interest

There is not any conflict of interest.

Code Availability

The code will made be available on reasonable request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thakur, A., Saini, D.S. Modifying Polyphase Codes to Mitigate Range Side-lobes in Pulse Compression Radar. Wireless Pers Commun 123, 693–707 (2022). https://doi.org/10.1007/s11277-021-09153-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09153-0

Keywords

Search

Navigation