ArticleSensors (Basel, Switzerland)2019
Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Enhanced FFT-Root-MUSIC Algorithm Based on Signal Reconstruction via CEEMD-SVD for Joint Range and Velocity Estimation for FMCW Radar.Sensors (Basel, Switzerland) · 2024Article
- High-Resolution Doppler and Azimuth Estimation and Target Detection in HFSWR: Experimental Study.Sensors (Basel, Switzerland) · 2022Article
- A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar.Sensors (Basel, Switzerland) · 2022Article
- FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area.Sensors (Basel, Switzerland) · 2022Article
- High-Efficiency Super-Resolution FMCW Radar Algorithm Based on FFT Estimation.Sensors (Basel, Switzerland) · 2021Article
- An Elaborated Signal Model for Simultaneous Range and Vector Velocity Estimation in FMCW Radar.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
A low-complexity joint range and Doppler frequency-modulated continuous wave (FMCW) radar algorithm based on the number of targets is proposed in this paper. This paper introduces two low-complexity FMCW radar algorithms, that is, region of interest (ROI)-based and partial discrete Fourier transform (DFT)-based algorithms. We find the low-complexity condition of each algorithm by analyzing the complexity of these algorithms. From this analysis, it is found that the number of targets is an important factor in determining complexity. Based on this result, the proposed algorithm selects a low-complexity algorithm between two algorithms depending the estimated number of targets and thus achieves lower complexity compared two low-complexity algorithms introduced. The experimental results using real FMCW radar systems show that the proposed algorithm works well in a real environment. Moreover, central process unit time and count of float pointing are shown as a measure of complexity.
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