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Enhanced Detection of Doppler-Spread Targets for FMCW Radar.

Authors :
Zhang, Wei
Li, Huiyong
Sun, Guohao
He, Zishu
Source :
IEEE Transactions on Aerospace & Electronic Systems. Aug2019, Vol. 55 Issue 4, p2066-2078. 13p.
Publication Year :
2019

Abstract

Frequency-modulated continuous waveform signals are of great interest in automotive radar, perimeter radar, and wide-area surveillance radar applications. For automotive radars and wide-area surveillance radars, which generally utilize high Doppler frequency resolution for target recognition, the important potential target such as a walking person cannot be viewed as a point-like target due to different velocities of the reflection points of a pedestrian. The echoes of such targets have extended Doppler spectrum, and they appear as horizontal lines in the range-Doppler plane, rather than a single point considered in the traditional cases. However, the existing approaches do not make full use of the Doppler-spread characteristic to enhance the detection performance of pedestrians. In this paper, we will show how this characteristic can be used to enhance the detection performance for Doppler-spread targets. The main goal of this paper is pedestrian detection in wide-surveillance radar applications and the proposed approach can also be applied in automotive radar applications. Motivated by the Hough transform, the energies of most Doppler bins corresponding to the target will be accumulated to enhance the detection performance. The questions of how many and which cells should be accumulated in practical applications are discussed in detail. The proposed approach is based on the ordered statistical-constant false alarm rate but tailored to Doppler-spread targets. The detection performance of this algorithm is derived analytically, and verified via practical wide-area surveillance radar signals. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189251
Volume :
55
Issue :
4
Database :
Academic Search Index
Journal :
IEEE Transactions on Aerospace & Electronic Systems
Publication Type :
Academic Journal
Accession number :
137987803
Full Text :
https://doi.org/10.1109/TAES.2019.2925433