Advances in Automotive Radar A framework on computationally efficient high-resolution frequency estimation

被引：146

作者：

Engels, Florian ^{[1
]}

Heidenreich, Philipp ^{[1
,2
,3
,4
]}

Zoubir, Abdelhak M. ^{[2
,5
,6
,7
]}

Jondral, Friedrich K. ^{[8
,9
]}

Wintermantel, Markus ^{[1
,10
]}

机构：

[1] Continental Adapt Distance Control Syst GmbH, Lindau, Germany

[2] Tech Univ Darmstadt, Signal Proc Grp, Darmstadt, Germany

[3] Gen Motors Europe, Zurich, Switzerland

[4] Opel AG, Automot Act Safety Applicat, Russelsheim, Germany

[5] Queensland Univ Technol, Brisbane, Qld 4001, Australia

[6] Curtin Univ Technol, Telecommun, Perth, WA 6845, Australia

[7] Tech Univ Darmstadt, Signal Proc, Darmstadt, Germany

[8] Karlsruhe Inst Technol, Commun Engn Lab, D-76021 Karlsruhe, Germany

[9] Karlsruhe Inst Technol, Dept Elect Engn & Informat Technol, D-76021 Karlsruhe, Germany

[10] Univ Stuttgart, Inst Network & Syst Theory, Stuttgart, Germany

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2017年 / 34卷 / 02期

关键词：

MAXIMUM-LIKELIHOOD; TRACKING; DECADES;

D O I：

10.1109/MSP.2016.2637700

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：36 / 46

页数：11

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