volume | PIER Journals

Abstract

With a constantly increasing number of cars equipped with 77 GHz automotive radar, the performance degrading effects of crosstalk are becoming a rising threat to radar-enabled automated driving functions. Since interference is sensitive to slight changes of temporal and spatial conditions of the scenario, meaningful measurements are hard to conduct which is why simulations are an important supplement. In this paper, a simulation model is introduced that estimates the distribution of the reduction of the detection range of automotive radars due to multiple interferers focusing on stochastic temporal conditions. The underlying system model calculates the direction- and timing-dependent influence of one single interferer on the detection range of the host radar. The model is kept simple, making it suitable for Monte Carlo methods, which allow the indispensable statistical evaluation of the broadly spread results. Finally, a method is presented that transfers multiple statistics regarding single interferers into a single environment. The computing time of the simulation grows linearly with the number of interfering radars, so the effects of vast numbers of interferers can be studied using this simulation model. Statistical evaluations of the detection performance degradation of a front-mounted radar in sample highway scenarios, containing up to ten interfering radar sensors, are performed in this paper.

1. Winner, H., S. Hakuli, F. Lotz, and C. Singer, Sensors for DAS, Springer, 2016.

2. Oprisan, D. and H. Rohling, "Analysis of mutual interference between automotive radar systems," International Radar Symposium (IRS), 83-90, Berlin, Germany, 2005. Google Scholar

3. Brooker, G. M., "Mutual interference of millimeter-wave radar systems," IEEE Trans. Electromagn. Compat., Vol. 49, No. 1, 170-181, 2007.
doi:10.1109/TEMC.2006.890223 Google Scholar

4. Goppelt, M. and H.-L. Blöcher, "Automotive radar - Investigation of mutual interference mechanisms," Advances in Radio Science, Vol. 8, 55-60, 2010.
doi:10.5194/ars-8-55-2010 Google Scholar

5. Schipper, T., et al. "Simulative prediction of the interference potential between radars in common road scenarios," IEEE Trans. Electromagn. Compat., Vol. 57, No. 3, 322-328, 2015.
doi:10.1109/TEMC.2014.2384996 Google Scholar

6. Schipper, T., et al. "A simulator for multi-user automotive radar scenarios," IEEE MTT-S Int. Conf. Microwaves Intell. Mobility, 1-4, 2015. Google Scholar

7. Al-Hourani, A., R. J. Evans, S. Kandeepan, B. Moran, and H. Eltom, "Stochastic geometry methods for modeling automotive radar interference," IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 2, 333-344, 2017.
doi:10.1109/TITS.2016.2632309 Google Scholar

8. Munari, A., L. Simić, and M. Petrova, "Stochastic geometry interference analysis of radar network performance," IEEE Communications Letters, Vol. 22, No. 11, 2362-2365, 2018.
doi:10.1109/LCOMM.2018.2869742 Google Scholar

9. Terbas, D., F. Laghezza, F. Jansen, A. Filippi, and J. Overdevest, "Radar to radar interference in common traffic scenarios," 16th European Radar Conference (EuRAD), 177-180, 2019. Google Scholar

10. Skaria, S., A. Al-Hourani, R. J. Evans, K. Sithamparanathan, and U. Parampalli, "Interference mitigation in automotive radars using pseudo-random cyclic orthogonal sequences," Sensors, Vol. 19, 4459, 2019.
doi:10.3390/s19204459 Google Scholar

11. Hahmann, K., S. Schneider, and T. Zwick, "Evaluation of probability of interference-related ghost targets in automotive radars," 2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), 1-4, 2018. Google Scholar

12. Hahmann, K., S. Schneider, and T. Zwick, "Estimation of the influence of incoherent interference on the detection of small obstacles with a DBF radar," 2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), 1-4, 2019. Google Scholar

13. Schipper, T., "Modellbasierte analyse des interferenzverhaltens von Kfz-Radaren,", Ph.D. dissertation, Karlsruhe Institiute of Technology, 2017. Google Scholar

14. Richards, M., J. Scheer, and W. Holm, "The radar range equation," Principles of Modern Radar, SciTech, Raleigh, NY, 2010. Google Scholar

15. Harris, F. J., "On the use of windows for harmonic analysis with the discrete Fourier transform," Proceedings of the IEEE, Vol. 66, No. 1, 51-83, 1978.
doi:10.1109/PROC.1978.10837 Google Scholar

16. Fischer, C., H.-L. Blöcher, J. Dickmann, and W. Menzel, "Robust detection and mitigation of mutual interference in automotive radar," 2015 16th International Radar Symposium (IRS), 143-148, 2015.
doi:10.1109/IRS.2015.7226239 Google Scholar

17. Fischer, C., M. Goppelt, H.-L. Blöcher, and J. Dickmann, "Minimizing interference in automotive radar using digital beamforming," Advances in Radio Science, Vol. 9, 45-49, 2011.
doi:10.5194/ars-9-45-2011 Google Scholar

18. Bechter, J., K. Eid, F. Roos, and C. Waldschmidt, "Digital beamforming to mitigate automotive radar interference," 2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), 1-4, 2016. Google Scholar

19. Bechter, J., M. Rameez, and C. Waldschmidt, "Analytical and experimental investigations on mitigation of interference in a DBF MIMO Radar," IEEE Transactions on Microwave Theory and Techniques, Vol. 65, No. 5, 1727-1734, 2017.
doi:10.1109/TMTT.2017.2668404 Google Scholar

20. Bartlett, M., "Smoothing periodograms from time-series with continuous spectra," Nature, Vol. 161, 686-687, 1948.
doi:10.1038/161686a0 Google Scholar

21. Bosq, D. and H. T. Nguyen, "Basic probability background," A Course in Stochastic Processes, Springer, Dordrecht, 1996. Google Scholar

Dr. Konstantin Hahmann

Dr. Stefan Schneider

Dr. Thomas Zwick