Adaptive digital beamforming for interference suppression in automotive FMCW radars

Rameez, Muhammad; Dahl, Mattias; Pettersson, Mats I.

doi:10.1109/radar.2018.8378566

Cited by 34 publications

(10 citation statements)

References 5 publications

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“…In addition, this method does not work properly if either the power of the interference signal is strong compared with the desired signal or the frequency slope difference between the desired and interferer is small. There is a way to reduce the mutual interference of automotive radar through adaptive beamforming [ 7 ]. The problem of the method proposed in [ 7 ] is that the computational complexity is significant because the adaptive weight should be obtained using the steepest-descent method.…”

Section: Introductionmentioning

confidence: 99%

“…There is a way to reduce the mutual interference of automotive radar through adaptive beamforming [ 7 ]. The problem of the method proposed in [ 7 ] is that the computational complexity is significant because the adaptive weight should be obtained using the steepest-descent method. Furthermore, it needs a certain convergence time so it cannot cope well with the rapidly time-varying interferences.…”

Section: Introductionmentioning

confidence: 99%

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Automotive Frequency Modulated Continuous Wave Radar Interference Reduction Using Per-Vehicle Chirp Sequences

Son

Sung

Heo

2018

Sensors

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Recently, many automobiles adopt radar sensors to support advanced driver assistance system (ADAS) functions. As the number of vehicles with radar systems increases the probability of radar signal interference and the accompanying ghost target problems become serious. In this paper, we propose a novel algorithm where we deploy per-vehicle chirp sequence in a frequency modulated continuous wave (FMCW) radar to mitigate the vehicle-to-vehicle radar interference. We devise a chirp sequence set so that the slope of each vehicle’s chirp sequence does not overlap within the set. By assigning one of the chirp sequences to each vehicle, we mitigate the interference from the radar signals transmitted by the neighboring vehicles. We confirm the performance of the proposed method stochastically by computer simulation. The simulation results show that the detection and false alarm performance is improved significantly by the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automotive Frequency Modulated Continuous Wave Radar Interference Reduction Using Per-Vehicle Chirp Sequences

Son

Sung

Heo

2018

Sensors

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“…The ADBF method is applied in the RAM map to improve the angular estimation accuracy and to separate the multiple vital signs. Digital beamforming is achieved digitally by using phase shift, scaling and summation, which in turn can be defined as spatial filtering of the received signal [27]. In this paper, ADBF combined with MIMO extension technology are used at the antenna receiving end to suppress strong interference and directional interference, and effectively extract the vital signs of multiple human.…”

Section: Lcmv-adbf-based Multi-human Target Separationmentioning

confidence: 99%

Multi-target vital signs detection using frequency-modulated continuous wave radar

Wang

Shui

Yang

et al. 2021

EURASIP J. Adv. Signal Process.

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Respiration and heartbeats rates are important physiological assessment indicators that provide valid prior-knowledge for the diagnosis of numerous diseases. However, most of the current research focuses on the vital signs measurement of single target, and multi-target vital signs detection has not received much attention. In this paper, we use frequency-modulated continuous wave (FMCW) radar to measure the vital signs signals of multi-target. First, we apply the three-dimensional fast Fourier transform (3D-FFT) method to separate multiple targets and get their distance and azimuth information. Subsequently, the linear constrained minimum variance-based adaptive beamforming (LCMV-ADBF) technique is proposed to form a spatially distributed beams on the targets of interest directions. Finally, a compressive sensing based on orthogonal matching pursuit (CS-OMP) method and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) method are present to extract the respiratory and heartbeat signals. We perform tests in a real experimental environment and compare the proposed method with reference devices. The results show that the degrees of agreement for respiratory and heartbeat are 89% and 87%, respectively, for two human targets. The level of agreement for respiratory and heartbeat is 87% and 85%, respectively, for three human targets, proving the effectiveness of the proposed method.

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“…Various signal processing techniques have been proposed to address the problem of mutual interference [6][7][8][9][10][11][12][13][14] in the frequency-modulated continuous wave (FMCW) radar or the chirp sequence (CS) radar [15]. Besides the traditional signal processing approaches, recent research results have shown that deep learning approaches can be used to solve this problem [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A DNN Autoencoder for Automotive Radar Interference Mitigation

Chen

Taghia

Fei

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, a novel interference mitigation approach using an autoencoder in combination with a traditional interference detection filter is introduced. It is shown that by employing the gated convolution, the encoder has the ability to learn the signal pattern from the remaining interference-free signal. The decoder can recover the interference-contaminated signal segments from the bottleneck representation as computed by the encoder.Experimental results show that the proposed method can provide a remarkable improvement in signal-to-interference-plus-noise ratio (SINR) and preserves its robustness on real radar measurements in severely disturbed scenarios that are more complex than the training dataset.

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Adaptive digital beamforming for interference suppression in automotive FMCW radars

Cited by 34 publications

References 5 publications

Automotive Frequency Modulated Continuous Wave Radar Interference Reduction Using Per-Vehicle Chirp Sequences

Automotive Frequency Modulated Continuous Wave Radar Interference Reduction Using Per-Vehicle Chirp Sequences

Multi-target vital signs detection using frequency-modulated continuous wave radar

A DNN Autoencoder for Automotive Radar Interference Mitigation

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