Abstract
Radar technology is used for many applications of advanced driver assistance systems (ADASs) and is considered as one of the key technologies for highly automated driving (HAD). An overview of conventional automotive radar processing is presented and critical use cases are pointed out in which conventional processing is bound to fail due to limited frequency resolution. Consequently, a flexible framework for computationally efficient high-resolution frequency estimation is presented. This framework is based on decoupled frequency estimation in the Fourier domain, where high-resolution processing can be applied to either the range, relative velocity, or angular dimension. Real data obtained from series-production automotive radar sensor are presented to show the effectiveness of the presented approach.
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