Abstract
Autonomous driving is experiencing a monumental technological shift, making it one of the most highly discussed topics of research. With novel technologies, like advanced driver assistance systems (ADAS), the automotive industry is making significant advancements in both driving comfort and safety. ADAS rely heavily on Frequency Modulated Continuous Waveform (FMCW) radar sensors for speed and range estimation. However, this technology suffers from interference management problem and an inherent ghost target detection with few specific waveforms. This paper introduces a new technique that utilizes Orthogonal Frequency-Division Multiplexing (OFDM) waveform to overcome ghost detection and increase robustness against interference. OFDM leverages maximum length sequence (MLS) (i.e., m-sequence) and provides orthogonality between different transmitted waveforms. Also, incorporating an appropriate scrambled sequence to the proposed waveform further reduces the impact of interference and improves the reliability of target detection. Simulations were conducted to demonstrate proposed algorithm performance for different scenarios, like single- and multiple-neighbors. Furthermore, results were compared and analyzed relative to conventional FMCW in similar scenarios.
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