A Low-Complexity FMCW Surveillance Radar Algorithm Using Two Random Beat Signals

Kim, Bong-Seok; Jin, Young-Seok; Kim, Sang‐Dong; Lee, Jonghun

doi:10.3390/s19030608

Cited by 19 publications

(19 citation statements)

References 12 publications

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“…We used a 24 GHz FMCW radar system with two TX and eight RX antennas, as was used in [14,21]. Figure 8 shows a block diagram of the radio frequency (RF) front-end module i.e., the front end module (FEM) used in the experiment.…”

Section: Methodsmentioning

confidence: 99%

“…To suppress the negative effects due to undesired echoes, the experiment was performed in an anechoic chamber located at the DGIST in Korea. This chamber is designed for use with 8 to 110 GHz frequencies, and its size is 5 m (W) × 10 m (L) × 4 m (H) [14]. Corner reflectors with 14 cm side length were used as targets to preserve the radar cross-section.…”

Section: Methodsmentioning

confidence: 99%

“…2048 point FFTs were performed for range estimation and 512 point FFTs were performed for Doppler estimation. The experimental parameters are shown in Table 1 [14]. The center frequency was set to 24 GHz, the bandwidth was set to 1 GHz, and the sampling frequency was set to 5 MHz.…”

Section: Methodsmentioning

confidence: 99%

“…However, this algorithm might miss the moving target with certain velocity because this algorithm fixedly employs two beat signals. In order to overcome this disadvantage, an FMCW radar algorithm has been proposed by randomly employing two beat signals in [14]. This algorithm effectively avoids missing a target with a certain velocity by randomly selecting two beat signals every frame.…”

mentioning

confidence: 99%

“…By doing so, the proposed algorithm further reduces the overall complexity compared to low-complexity algorithms that use an ROI. The proposed algorithm overcomes the shortcomings of [14], which cannot detect the velocity of the target because it uses all beat components corresponding to ROI. Simulation results show that the complexity according to the number of targets in order to check our derived criterion.…”

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confidence: 99%

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

Kim

Jin

et al. 2019

Sensors

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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.clutter, there is no Doppler effect. Hence, two beat signals are the same except for noise term and thus the difference of two beat signals contains only noise term. On the other hand, in the case of moving target, there is Doppler effect due to the moving target and thus the difference of them contains the range information of moving target. However, this algorithm might miss the moving target with certain velocity because this algorithm fixedly employs two beat signals. In order to overcome this disadvantage, an FMCW radar algorithm has been proposed by randomly employing two beat signals in [14]. This algorithm effectively avoids missing a target with a certain velocity by randomly selecting two beat signals every frame. In addition, this algorithm performs an angle detection algorithm only if there is a moving target. Therefore, this algorithm reduces the overall complexity. However, this algorithm has still a disadvantage in that it does not detect the velocity of the target. This is because the difference between the two beat signals is used to reduce the complexity of the moving target detection process and in this process, information necessary for the velocity detection of the target is lost.Meanwhile, in [15][16][17][18], low-complexity detection algorithms for FMCW radar have been proposed which intend to reduce the number of FFTs compared to a full-dimension FFT-based FMCW radar algorithm. These algorithms determine a region of interest (ROI), thus reducing the number of inputs in the FFTs for Doppler estimation. However, there is still unnecessary computational complexity in these algorithms, although the complexity is reduced. The number of range bins used as the input in FFTs for Doppler estimation depends on the number of targets. In this algorithm, all chirp signals are used in an FFT to determine the range bins in which peaks exist. However, there is a disadvantage in that the number of range bins calculated in the first FFT for range estimation is too large compared to the number of range bins used as inputs in the FFT fo...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Kim

Jin

et al. 2019

Sensors

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Extrapolation-RELAX Estimator Based on Spectrum Partitioning for DOA Estimation of FMCW Radar

Kim

Jin

et al. 2019

IEEE Access

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This paper proposes an extrapolation-RELAX estimator based on spectrum partitioning (SP) for the direction of arrival (DOA) estimation of frequency-modulated continuous-wave (FMCW) radar. The FMCW radar employs fast Fourier transform (FFT)-based digital beamforming (DBF) for the DOA estimation owing to its low complexity and easy implementation. However, the DBF algorithm has a disadvantage of low angle resolution. To improve the angle resolution, super-resolution algorithms such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) are proposed. However, these algorithms require the high signal-to-noise ratio (SNR) to meet the required performance. To overcome this drawback of super-resolution algorithms, the SP-based extrapolation method has been proposed. However, this algorithm still has the problem that the resolution performance degrades owing to the insufficient number of actual antenna arrays. To solve this problem, we propose the SP-based extrapolation-RELAX algorithm for DOA estimation of FMCW radar. Through extrapolation, the proposed structure solves the problem of insufficient number of arrays, resulting in high reliability of SP results. When the extrapolation algorithm is used to generate the input signal of the RELAX algorithm, the RELAX method improves the performance of the DOA estimation. To confirm the effectiveness of the proposed estimation, we compare the Monte Carlo simulation and root-mean-square error results of the proposed and conventional algorithms. To verify the performance of the proposed algorithm in practical conditions, experiments were performed using the FMCW radar module within a chamber and in an indoor environment.

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Radar Based Target Tracking and Classification for Efficient Robot Speed Control in Fenceless Environments

Ubezio

Schöffmann

Wohlhart

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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A Low-Complexity FMCW Surveillance Radar Algorithm Using Two Random Beat Signals

Cited by 19 publications

References 12 publications

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

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

Extrapolation-RELAX Estimator Based on Spectrum Partitioning for DOA Estimation of FMCW Radar

Radar Based Target Tracking and Classification for Efficient Robot Speed Control in Fenceless Environments

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