Motion compensation for TDM MIMO radar by sparse reconstruction

Hu, Xueyao; Lu, Man; Li, Yang; Wang, Yanhua

doi:10.1049/el.2017.3524

Cited by 16 publications

(8 citation statements)

References 6 publications

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“…The driver's movement causes irregular fluctuations in the radar signal [15], which reduces the accuracy of vital signal monitoring through the FMCW radar [16]. Therefore, it is necessary to compensate for the respiration signal reflecting the movement of the driver in the movement situation to improve the accuracy of signal extraction [17,18].…”

Section: Introductionmentioning

confidence: 99%

Movement Compensated Driver’s Respiratory Rate Extraction

Yoo

Shin

2022

Applied Sciences

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In non-contact vital sign monitoring using radar, radar signal distorted by the surrounding unspecified factors is unsuitable for monitoring vital signs. In order to monitor vital signs accurately, it is essential to compensate for distortion of radar signals caused by surrounding environmental factors. In this paper, we propose a driver vital signal compensation method in driving situations, including the driver’s movements using a frequency-modulated continuous-wave (FMCW) radar. Driver’s movement is quantified from the radar signal and used to set a distortion signal compensation index to compensate for the signal distortion induced in the driving situation that the driver’s movement occurs. The experimental results show that the respiration rate estimated from the radar signal compensated through the proposed method is similar to the actual respiration rate than from the signal before calibration. These results confirm the possibility of using the proposed method in a non-statistic situation and effectiveness in estimating respiration rate reflecting human movement in monitoring vital signs using FMCW radar.

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Section: Introductionmentioning

confidence: 99%

Movement Compensated Driver’s Respiratory Rate Extraction

Yoo

Shin

2022

Applied Sciences

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“…Guetlein [19] and Rambach [20] proposed modulation schemes to eliminate the phase shift by compensating the target, but their strategies only considered the scenario for a single target. In [21], sparse reconstruction is applied to deal with the phase shifts, but it takes a lot of steps and calculations. A phase error correction method based on DFT is proposed in [22].…”

Section: Introductionmentioning

confidence: 99%

“…A phase error correction method based on DFT is proposed in [22]. While all of the above methods [18,19,20,21,22] are focused on eliminating the phase shift. In this paper, a migration caused by frequency is found existing in the system for the high-speed target detection (such as in automotive radar, the high speed refers to 72 km/h to 300 km/h).…”

Section: Introductionmentioning

confidence: 99%

An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

Miao

Zhao

et al. 2019

Sensors

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In this paper, a Multiple Input Multiple Output (MIMO) radar system based on a sparse-array is proposed. In order to reduce the side-lobe level, a genetic algorithm (GA) is used to optimize the array arrangement. To reduce the complexity of the system, time-division multiplexing (TDM) technology is adopted. Since the signals are received in different periods, a frequency migration will emerge if the target is in motion, which will lead to the lower direction-of-arrival (DOA) performance of the system. To solve this problem, a stretching transformation method in the fast-frequency slow-time domain is proposed, in order to eliminate frequency migration. Only minor adjustments need to be implemented for the signal processing, and the root-mean-square error (RMSE) of the DOA estimation will be reduced by about 90%, compared with the one of an uncalibrated system. For example, a uniform linear array (ULA) MIMO system with 2 transmitters and 20 receivers can be replaced by the proposed system with 2 transmitters and 12 receivers, achieving the same DOA performance. The calibration formulations are given, and the simulation results of the automotive radar system are also provided, which validate the theory.

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“…However, this method is only applicable to scenes where the target moves at a slower speed. Some random switching methods were proposed in works by the authors of [5][6][7] to realize motion compensation. However, these methods need to combine the target scattering characteristics and optimization algorithms to solve the optimal switching scheme, consuming time and computing resources, making application to scenarios with high real-time requirements difficult.…”

Section: Introductionmentioning

confidence: 99%

Motion Compensation for SAA FMCW Radar Based on Specific Switching Scheme

Lin

Guo

et al. 2019

Applied Sciences

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Switch antenna array (SAA) frequency modulated continuous wave (FMCW) radar has been studied by many researchers due to its low cost and straightforward architecture. However, the motion-induced phase leads to incorrect direction-of-arrival (DoA) estimation, which is one of the main obstacles when it is applied to a moving scenario. To address this problem, we propose a motion compensation method based on a specific switching scheme in this paper. The effectiveness of this method is verified by simulation and measurement results. This approach only requires basic signal processing techniques and avoids complex optimization algorithms.

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Motion compensation for TDM MIMO radar by sparse reconstruction

Cited by 16 publications

References 6 publications

Movement Compensated Driver’s Respiratory Rate Extraction

Movement Compensated Driver’s Respiratory Rate Extraction

An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

Motion Compensation for SAA FMCW Radar Based on Specific Switching Scheme

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