Searching Velocity Fourier Transform: A Novel Focus-Before-Detection Algorithm in FMCW Radar

The conventional frequency modulated continuous wave (FMCW) radar accuracy range detection algorithm is based on the frequency estimation and additional phase evaluation which contains Fourier transform and frequency refining analysis in each chirp, so it has the disadvantages of being computationally expensive, and not being suitable for real-time motion measurement. In addition, if there are other objects near the target, the spectra of the clutter and the target will be adjacent and affect each other, making it more challenging to estimate the frequency of the target. In this paper, the analytical expression of the Fourier transform of the beat signal is presented and it can be seen that spectrum leakage makes the phase of Fourier transform no longer consistent with the real phase of signal. The change regularities of real and imaginary parts of Fourier transform are studied, and the corrected phase of ellipse approximation is given in the industrial, scientific, and medical (ISM) band. Accurate displacement can be obtained by accurate phase. The algorithm can filter the direct current (DC) offset which is mainly caused by stationary objects. The performance of the algorithm is evaluated by a radar system whose center frequency is 24.075 GHz and the bandwidth is 0.15 GHz; the measurement accuracy of displacement is 0.087 mm and the accuracy of distance is 0.043 m.

Download Full-text

High-Speed Target Detection Algorithm Based on Sparse Fourier Transform

IEEE Access ◽

10.1109/access.2018.2853180 ◽

2018 ◽

Vol 6 ◽

pp. 37828-37836 ◽

Cited By ~ 12

Author(s):

Cunsuo Pang ◽

Shengheng Liu ◽

Yan Han

Keyword(s):

Fourier Transform ◽

Target Detection ◽

High Speed ◽

Detection Algorithm

Download Full-text

Low-Complexity MUSIC-Based Direction-of-Arrival Detection Algorithm for Frequency-Modulated Continuous-Wave Vital Radar

Sensors ◽

10.3390/s20154295 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4295 ◽

Cited By ~ 1

Author(s):

Bong-seok Kim ◽

Youngseok Jin ◽

Jonghun Lee ◽

Sangdong Kim

Keyword(s):

Fourier Transform ◽

Fast Fourier Transform ◽

Continuous Wave ◽

Direction Of Arrival ◽

Doa Estimation ◽

Low Complexity ◽

Detection Algorithm ◽

Field Of View ◽

Music Algorithm ◽

Trade Offs

This paper proposes a low complexity multiple-signal-classifier (MUSIC)-based direction-of-arrival (DOA) detection algorithm for frequency-modulated continuous-wave (FMCW) vital radars. In order to reduce redundant complexity, the proposed algorithm employs characteristics of distance between adjacent arrays having trade-offs between field of view (FOV) and resolution performance. First, the proposed algorithm performs coarse DOA estimation using fast Fourier transform. On the basis of the coarse DOA estimation, the number of channels as input of the MUSIC algorithm are selected. If the estimated DOA is smaller than 30°, it implies that there is an FOV margin. Therefore, the proposed algorithm employs only half of the channels, that is, it is the same as doubling the spacing between arrays. By doing so, the proposed algorithm achieves more than 40% complexity reduction compared to the conventional MUSIC algorithm while achieving similar performance. By experiments, it is shown that the proposed algorithm despite the low complexity is enable to distinguish the adjacent DOA in a practical environment.

Download Full-text

Detection Algorithm of Maneuvering Target Based on Matched Fourier Transform

2018 IEEE 18th International Conference on Communication Technology (ICCT) ◽

10.1109/icct.2018.8600102 ◽

2018 ◽

Author(s):

Baofeng Guo ◽

Huixian Sun ◽

Wenhua Hu ◽

Ning Han ◽

Huiyan Zeng ◽

...

Keyword(s):

Fourier Transform ◽

Detection Algorithm ◽

Maneuvering Target

Download Full-text

Multi-target detection algorithm for FMCW radar

2012 IEEE Radar Conference ◽

10.1109/radar.2012.6212161 ◽

2012 ◽

Cited By ~ 5

Author(s):

Eugin Hyun ◽

Woojin Oh ◽

Jong-Hun Lee

Keyword(s):

Target Detection ◽

Detection Algorithm ◽

Fmcw Radar

Download Full-text

Automotive FMCW Radar-Enhanced Range Estimation via a Local Resampling Fourier Transform

International Journal of Advanced Robotic Systems ◽

10.5772/62179 ◽

2016 ◽

Vol 13 (1) ◽

pp. 13 ◽

Cited By ~ 1

Author(s):

Cailing Wang ◽

Huajun Liu ◽

Guang Han ◽

Xiaoyuan Jing

Keyword(s):

Fourier Transform ◽

Range Estimation ◽

Fmcw Radar

Download Full-text

Features of Multi-target Detection Algorithm for Automotive FMCW Radar

Lecture Notes in Computer Science - Internet of Things, Smart Spaces, and Next Generation Networks and Systems ◽

10.1007/978-3-030-30859-9_30 ◽

2019 ◽

pp. 355-364 ◽

Cited By ~ 5

Author(s):

Vladimir D. Kuptsov ◽

Sergei I. Ivanov ◽

Alexander A. Fedotov ◽

Vladimir L. Badenko

Keyword(s):

Target Detection ◽

Detection Algorithm ◽

Fmcw Radar

Download Full-text

Track Health Monitoring Using Wavelets

ASME 2010 Rail Transportation Division Fall Technical Conference ◽

10.1115/rtdf2010-42004 ◽

2010 ◽

Cited By ~ 1

Author(s):

Brad M. Hopkins ◽

Saied Taheri

Keyword(s):

Signal Processing ◽

Fourier Transform ◽

Defect Detection ◽

Health Monitoring ◽

Finite Energy ◽

Detection Algorithm ◽

Transient Signal ◽

Vertical Acceleration ◽

Typical Day ◽

The Fourier Transform

This paper presents a defect detection algorithm for rail health monitoring that could potentially be used with limited bogie. Current wheel and track monitoring requires expensive track instrumentation and/or time consuming operation of railway monitoring vehicles. The proposed health monitoring algorithm can potentially be used with a portable data acquisition system that can be relocated from train to train to monitor and diagnose the conditions of the track as a train is driven during typical day-to-day operation. The algorithm processes the data using wavelets and is able to locate defects and provide information that may help to distinguish between various types of rail defects. In recent years, wavelets have been used extensively in signal processing because of their ability to analyze a signal simultaneously in the time and frequency domains. The Fourier transform has been used traditionally in signal processing to locate dominant frequencies in a signal, but it is unable to provide time localization of those frequencies. Unlike the Fourier transform, the wavelet transform uses a set of basis functions with finite energy, which is advantageous for detecting the irregular events that may show up in a transient signal. The wavelets used in the proposed signal processing routine were chosen for optimal signal decomposition through consideration of the signals that are likely to be generated from common rail and wheel defects, including rail cracks, squats, corrugation, and, wheel out-of-rounds. A sample accelerometer signal was generated from information found in existing literature and was then processed using the proposed defect detection algorithm. Results show the potential of this algorithm to locate and diagnose defects from limited bogie vertical acceleration data. This study is intended to present a proof-of-concept for the proposed defect detection algorithm, providing a basis for which a more comprehensive defect detection and diagnosis algorithm can be developed.

Download Full-text