This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Targets Location for Automotive Radar Based on Compressed Sensing in Spatial Domain
Technical Paper
2018-01-1621
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Millimeter wave automotive radar is one of the most important sensors in the Advanced Driver Assistance System (ADAS) and autonomous driving system, which detects the target vehicles around the ego vehicle via processing transmitted and echo signals. However, the sampling rate of classical radar signal processing methods based on Nyquist sampling theorem is too high and the resolution of range, velocity and azimuth can’t meet the requirement of highly autonomous driving, especially azimuth. In spatial domain, targets are sparse distribution in the detection range of automotive radar. To solve these problems, the algorithm for targets location based on compressed sensing for automotive radar is proposed in this paper. Besides, the feasibility of the algorithm is verified through the simulation experiments of traffic scene. The range-doppler-azimuth model can be used to estimate the distance, velocity and azimuth of the target accurately. Compared with the classical radar algorithm, it can improve the distance resolution and distinguish adjacent targets. In terms of angle estimation, compared with MUSIC (Multiple Signal Classification) algorithm, the snapshot required for the compressed sensing is less, and it can achieve better angle resolution.
Recommended Content
Authors
Topic
Citation
Chen, Z., Yin, Y., Bai, J., Huang, L. et al., "Targets Location for Automotive Radar Based on Compressed Sensing in Spatial Domain," SAE Technical Paper 2018-01-1621, 2018, https://doi.org/10.4271/2018-01-1621.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 |
Also In
References
- Bilik , I. , Bialer , O. , Villeval , S. et al. Automotive MIMO Radar for Urban Environments IEEE Radar Conference 2016 10.1109/RADAR.2016.7485215
- Yin , Y. , Bi , X. , Huang , L. et al. The Application of Compressed Sensing in Automotive Radar Signal Processing for the Target Location SAE Technical Paper 2017-01-1973 2017 10.4271/2017-01-1973
- Candes , E. , Braun , N. , and Wakin , M. Sparse Signal and Image Recovery from Compressive Samples 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007 (ISBI 2007) 2007 976 979
- Candes , E. and Romberg , J. Sparsity and Incoherence in Compressive Sampling Inverse Problems 23 3 969 2007
- Candes , E.J. and Tao , T. Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies? IEEE Transactions on Information Theory 52 12 5406 5425 2006
- Candès , E.J. and Wakin , M.B. An Introduction to Compressive Sampling IEEE Signal Processing Magazine 25 2 21 30 2008
- Herman , M.A. and Strohmer , T. High-Resolution Radar via Compressed Sensing IEEE Transactions on Signal Processing 57 6 2275 2284 2009
- Lutz , S. , Ellenrieder , D. , Walter , T. et al. On Fast Chirp Modulations and Compressed Sensing for Automotive Radar Applications 2014 15th International Radar Symposium (IRS) 2014 1 6
- Dang , V. and Kilic , O. Joint DoA-Range-Doppler Tracking of Moving Targets Based on Compressive Sensing 2014 IEEE Antennas and Propagation Society International Symposium (APSURSI) 2014 141 142
- Dang , V.Q. 2015
- Rossi , M. , Haimovich , A.M. , and Eldar , Y.C. Spatial Compressive Sensing for MIMO Radar IEEE Transactions on Signal Processing 62 2 419 430 2014
- Rossi , M. , Haimovich , A.M. , and Eldar , Y.C. Spatial Compressive Sensing in MIMO Radar with Random Arrays 2012 46th Annual Conference on Information Sciences and Systems (CISS) 2012 1 6