The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Automotive Radar — From First Efforts to Future Systems
Open accessAbstract
Although the beginning of research on automotive radar sensors goes back to the 1960s, automotive radar has remained one of the main drivers of innovation in millimeter wave technology over the past two decades. Today, millions of sensors are produced each year, which was made possible by inexpensive and mature millimeter wave technology. The technology maturity, in turn, enables research to be carried out on systems that are considerably more complex and powerful than was possible just a few years ago. The focus of research has thus shifted from purely hardware-oriented and device-level topics to sophisticated millimeter wave systems and RF signal processing topics. This opens up new research topics such as digital modulation schemes, radar networks, radar imaging, and machine learning. In this review paper, we sketch the path from the very beginning through the state of the art with sophisticated multiple-input multiple-output (MIMO) antenna arrays and mature assembly and interconnect concepts to today's key research topics of automotive radar.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
MIMO radar: an idea whose time has come
Fishler, E., Haimovich, A., Blum, R., Chizhik, D., Cimini, L., & Valenzuela, R. (2004) · Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509) · doi:10.1109/nrc.2004.1316398 · Cited by 1,453
Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band
Hasch, J., Topak, E., Schnabel, R., Zwick, T., Weigel, R., & Waldschmidt, C. (2012) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2011.2178427 · Cited by 1,372
Self-citationA Review on Deep Learning Techniques Applied to Semantic Segmentation
García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., & García‐Rodríguez, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.06857 · Cited by 991
Baraniuk, R., & Steeghs, P. (2007) · 2007 IEEE Radar Conference · doi:10.1109/radar.2007.374203 · Cited by 889
Kim, Y., & Moon, T. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2491329 · Cited by 647
Frequency Diverse MIMO Techniques for Radar
Sammartino, P. F., Baker, C. J., & Griffiths, H. D. (2013) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2013.6404099 · Cited by 440
Spatial Compressive Sensing for MIMO Radar
Rossi, M., Haimovich, A. M., & Eldar, Y. C. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2289875 · Cited by 269
Antenna Concepts for Millimeter-Wave Automotive Radar Sensors
Menzel, W. S., & Möbius, A. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2184729 · Cited by 265
Self-citationHuman walking estimation with radar
van Dorp, P., & Groen, F. C. A. (2003) · IEE Proceedings - Radar, Sonar and Navigation · doi:10.1049/ip-rsn:20030568 · Cited by 261
Metadata
- Authors
- Waldschmidt, C., Hasch, J., & Menzel, W.
- Venue
- IEEE Journal of Microwaves
- Published
- 2021-01
- Volume
- 1
- Issue
- 1
- Pages
- 135-148
- ISSN
- 2692-8388 (ISSN-L)
- OpenAlex
- W3118293846
- Cited by
- 580
- References
- 38
- FWCI
- 51.20
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Automotive Radar — From First Efforts to Future Systems. https://app.cochat.ai/papers/10.1109/jmw.2020.3033616
- The paper
- Waldschmidt, C., Hasch, J., & Menzel, W. (2021). Automotive Radar — From First Efforts to Future Systems. IEEE Journal of Microwaves. https://doi.org/10.1109/jmw.2020.3033616
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
