Clutter Suppression Method Of Iron Tunnel Using Cepstral Analysis For Automotive Radars

In this paper, we propose an efficient clutter suppression algorithm for automotive radar systems in iron-tunnel environments. In general, the clutters in iron tunnels makes it highly likely that automotive radar systems will fail to detect targets. In order to overcome this drawback, we first analyze the cepstral characteristic of the iron tunnel clutter to determine the periodic properties of the clutters in the frequency domain. Based on this observation, we suggest for removing the periodic components induced by the clutters in iron tunnels in the cepstral domain by using the cepstrum editing process. To verify the clutter suppression of the proposed method experimentally, we performed measurements by using 77 GHz frequency modulated continuous waveform radar sensors for an adaptive cruise control (ACC) system. Experimental results show that the proposed method is effective to suppress the clutters in iron-tunnel environments in the sense that it improves the early target detection performance for ACC significantly.