scholarly journals A Low-Complexity Joint TOAs and AOAs Parameter Estimator Using Dimension Reduction for FMCW Radar Systems

2018 ◽  
Vol 24 (4) ◽  
Author(s):  
Sangdong Kim ◽  
Yeonghwan Ju ◽  
Jonghun Lee
Keyword(s):  
Dimension Reduction ◽  
Low Complexity ◽  
Parameter Estimator ◽  
Fmcw Radar ◽  
Radar Systems

scholarly journals Low-Complexity 2D-MUSIC for Joint Range and Angle Estimation of Frequency Modulated Continuous-Wave Radar

2021 ◽  
Vol 21 (5) ◽  
pp. 399-405
Author(s):  
Yongchul Jung ◽  
Seunghyeok Lee ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Continuous Wave ◽  
Low Complexity ◽  
Processing Technique ◽  
Angle Estimation ◽  
Multiple Signal Classification ◽  
Fmcw Radar ◽  
Radar Systems ◽  
Wave Radar ◽  
Preprocessing Technique ◽  
Joint Range

A pre-processing technique is proposed to reduce the complexity of two-dimensional multiple signal classification (2D-MUSIC) for the joint range and angle estimation of frequency-modulated continuous-wave (FMCW) radar systems. By using the central symmetry of the angle steering vector from a uniform linear array (ULA) antenna and the linearity of the beat signal in the FMCW radar, this preprocessing technique transforms 2D-MUSIC from complex values into real values. To compare the computational complexity of the proposed algorithm with the conventional 2D-MUSIC, we measured the CPU processing time for various numbers of snapshots, and the evaluation results indicated that the 2D-MUSIC with the proposed pre-processing technique is approximately three times faster than the conventional 2D-MUSIC.

scholarly journals Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 51 ◽  
Author(s):  
Bong-seok Kim ◽  
Sangdong Kim ◽  
Youngseok Jin ◽  
Jonghun Lee
Keyword(s):  
Continuous Wave ◽  
Region Of Interest ◽  
Doppler Frequency ◽  
Low Complexity ◽  
Process Unit ◽  
Central Process ◽  
Fmcw Radar ◽  
Radar Systems ◽  
Lower Complexity ◽  
Joint Range

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.

scholarly journals Low-Complexity-Based RD-MUSIC with Extrapolation for Joint TOA and DOA at Automotive FMCW Radar Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sangdong Kim ◽  
Bongseok Kim ◽  
Jonghun Lee
Keyword(s):  
High Resolution ◽  
Continuous Wave ◽  
Low Complexity ◽  
Fmcw Radar ◽  
Multiple Parameters ◽  
Extrapolation Algorithm ◽  
Radar Systems ◽  
Multiple Vehicles ◽  
Resolution Algorithm ◽  
On The Road

Low-complexity-based reduced-dimension–multiple-signal classification (RD-MUSIC) is proposed with extrapolation for joint time delay of arrivals (TOA) and direction of arrivals (DOA) at automotive frequency-modulated continuous-wave (FMCW) radar systems. When a vehicle is driving on the road, the automotive FMCW radar can estimate the position of multiple other vehicles, because it can estimate multiple parameters, such as TOA and DOA. Over time, the requirement of the accuracy and resolution parameters of automotive FMCW radar is increasing. To accurately estimate the parameters of multiple vehicles, such as range and angle, it is difficult to use a low-resolution algorithm, such as the two-dimensional fast Fourier transform. To improve parameter estimation performance, high-resolution algorithms, such as the 2D-MUSIC, are required. However, the conventional high-resolution methods have a high complexity and, thus, are not applicable to a real-time radar system for a vehicle. Therefore, in this work, a low-complexity RD-MUSIC with extrapolation algorithm is proposed to have a resolution similar to that of a high-resolution algorithm to estimate the position of other vehicles. Compared with conventional low complexity high resolution, in experimental results, the proposed method had better performance.

scholarly journals Analysis of sampling clock phase noise in homodyne FMCW radar systems

2016 ◽  
Author(s):  
Kashif Siddiq ◽  
Robert J. Watson ◽  
Steve R. Pennock ◽  
Philip Avery ◽  
Richard Poulton ◽  
...  
Keyword(s):  
Phase Noise ◽  
Fmcw Radar ◽  
Radar Systems

Analysis of Spurs Impact in PLL-Based FMCW Radar Systems

2020 ◽  
Author(s):  
Luigi Grimaldi ◽  
Dmytro Cherniak ◽  
Werner Grollitsch ◽  
Roberto Nonis
Keyword(s):  
Fmcw Radar ◽  
Radar Systems

scholarly journals YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2897 ◽  
Author(s):  
Woosuk Kim ◽  
Hyunwoong Cho ◽  
Jongseok Kim ◽  
Byungkwan Kim ◽  
Seongwook Lee
Keyword(s):  
Spatial Clustering ◽  
Radar Data ◽  
Support Vector ◽  
Automotive Radar ◽  
Radar Sensor ◽  
Fmcw Radar ◽  
Radar Systems ◽  
Conventional Methods ◽  
Two Stages ◽  
Deep Learning Model

This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body.

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