Details
| Original language | English |
|---|---|
| Title of host publication | IET Conference Proceedings |
| Publisher | Institution of Engineering and Technology |
| Pages | 312-317 |
| Number of pages | 6 |
| Volume | 2022 |
| Edition | 17 |
| ISBN (electronic) | 9781839537776 |
| Publication status | Published - 2022 |
| Event | 2022 International Conference on Radar Systems, RADAR 2022 - Edinburgh, Virtual, United Kingdom (UK) Duration: 24 Oct 2022 → 27 Oct 2022 |
Abstract
Differentiating targets from background noise is an essential task of radar signal processing. Typically, constant false alarm rate (CFAR) detectors, which estimate local noise characteristics to determine an adaptive threshold, are employed for this purpose. A commonly used variant for automotive radar applications is the ordered-statistic CFAR (OS-CFAR) due to its good performance in multi-target scenarios and near clutter edges. However, obtaining the order statistics is associated with computationally intensive sorting of the CFAR training data. With the rank-only implementation, an efficient OS-CFAR algorithm is given, which does not require to calculate the order statistics explicitly and thus removes the necessity of sorting. This has the drawback, that the local noise estimates are not calculated, which may be required in some applications, e.g. to compute the signal-to-noise ratio. In this work we propose a dedicated noise estimation stage as an extension to the rank-only OS-CFAR to compensate for this disadvantage. We show that by including the detection information, noise estimates of comparable or even better quality in the case of spectral regions containing targets can be obtained with minimal computational effort. Furthermore, an efficient FPGA-based implementation of this two-stage approach is presented and compared against other implementations of OS-CFAR.
Keywords
- AUTOMOTIVE RADAR, CFAR, FPGA, NOISE ESTIMATION, REAL-TIME PROCESSING
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
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IET Conference Proceedings. Vol. 2022 17. ed. Institution of Engineering and Technology, 2022. p. 312-317.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficient implementation of rank-only OS-CFAR with dedicated noise estimation
AU - Köhler, Daniel
AU - Meinl, Frank
AU - Blume, Holger
PY - 2022
Y1 - 2022
N2 - Differentiating targets from background noise is an essential task of radar signal processing. Typically, constant false alarm rate (CFAR) detectors, which estimate local noise characteristics to determine an adaptive threshold, are employed for this purpose. A commonly used variant for automotive radar applications is the ordered-statistic CFAR (OS-CFAR) due to its good performance in multi-target scenarios and near clutter edges. However, obtaining the order statistics is associated with computationally intensive sorting of the CFAR training data. With the rank-only implementation, an efficient OS-CFAR algorithm is given, which does not require to calculate the order statistics explicitly and thus removes the necessity of sorting. This has the drawback, that the local noise estimates are not calculated, which may be required in some applications, e.g. to compute the signal-to-noise ratio. In this work we propose a dedicated noise estimation stage as an extension to the rank-only OS-CFAR to compensate for this disadvantage. We show that by including the detection information, noise estimates of comparable or even better quality in the case of spectral regions containing targets can be obtained with minimal computational effort. Furthermore, an efficient FPGA-based implementation of this two-stage approach is presented and compared against other implementations of OS-CFAR.
AB - Differentiating targets from background noise is an essential task of radar signal processing. Typically, constant false alarm rate (CFAR) detectors, which estimate local noise characteristics to determine an adaptive threshold, are employed for this purpose. A commonly used variant for automotive radar applications is the ordered-statistic CFAR (OS-CFAR) due to its good performance in multi-target scenarios and near clutter edges. However, obtaining the order statistics is associated with computationally intensive sorting of the CFAR training data. With the rank-only implementation, an efficient OS-CFAR algorithm is given, which does not require to calculate the order statistics explicitly and thus removes the necessity of sorting. This has the drawback, that the local noise estimates are not calculated, which may be required in some applications, e.g. to compute the signal-to-noise ratio. In this work we propose a dedicated noise estimation stage as an extension to the rank-only OS-CFAR to compensate for this disadvantage. We show that by including the detection information, noise estimates of comparable or even better quality in the case of spectral regions containing targets can be obtained with minimal computational effort. Furthermore, an efficient FPGA-based implementation of this two-stage approach is presented and compared against other implementations of OS-CFAR.
KW - AUTOMOTIVE RADAR
KW - CFAR
KW - FPGA
KW - NOISE ESTIMATION
KW - REAL-TIME PROCESSING
UR - http://www.scopus.com/inward/record.url?scp=85174658611&partnerID=8YFLogxK
U2 - 10.1049/icp.2022.2336
DO - 10.1049/icp.2022.2336
M3 - Conference contribution
AN - SCOPUS:85174658611
VL - 2022
SP - 312
EP - 317
BT - IET Conference Proceedings
PB - Institution of Engineering and Technology
T2 - 2022 International Conference on Radar Systems, RADAR 2022
Y2 - 24 October 2022 through 27 October 2022
ER -