An Advanced Wideband Interference Suppression Technique using Envelope Detection and Sorting for Automotive FMCW Radar

Shimura, Takuto; Umehira, Masahiro; Watanabe, Y.; Wang, Xiaoyan; Takeda, Shigeki

doi:10.1109/radarconf2248738.2022.9764275

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…In order to solve the problem in the conventional zero suppression method, interference mitigation method based on envelope detection and sorting [12] has been proposed. We briefly explain the process of this method as follows.…”

Section: Envelope Detection and Sorting Based Interference Mitigation...mentioning

confidence: 99%

“…In [11], the interference is mitigated by reconstructing the disrupted samples in the time-domain baseband signal by Kalman filtering. To appropriately control the interference detection threshold from the received beat signals, an advanced wideband interference mitigation technique using envelope detection and sorting has been proposed for automotive FMCW radar [12]. In [13], the authors proposed an interference mitigation method using an adaptive canceller to minimize the correlation between the waveforms of the victim and interfering radars.…”

Section: Introductionmentioning

confidence: 99%

“…where 𝑁 is the number of samples of the beat signal, 𝑘 is a parameter to adjust the threshold. In the original beat signal, samples satisfying 𝑅 𝑡ℎ < |𝑟(𝑖)| are considered as interference, and their values will be set to 0 [12]. However, this method has a problem that it becomes difficult to detect the interference when the level of the interference signal is small.…”

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confidence: 99%

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Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

KOIZUMI,

WANG,

UMEHIRA

et al. 2024

IEICE Trans. Fundamentals

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In recent years, high-resolution 77GHz band automotive radar, which is indispensable for autonomous driving, has been extensively investigated. In the future, as vehicle-mounted CS (chirp sequence) radars become more and more popular, intensive inter-radar wideband interference will become a serious problem, which results in undesired miss detection of targets. To address this problem, learning-based wideband interference mitigation method has been proposed, and its feasibility has been validated by simulations. In this paper, firstly we evaluated the tradeoff between interference mitigation performance and model training time of the learning-based interference mitigation method in a simulation environment. Secondly, we conducted extensive inter-radar interference experiments by using multiple 77GHz MIMO (Multiple-Input and Multiple-output) CS radars and collected real-world interference data. Finally, we compared the performance of learning-based interference mitigation method with existing algorithm-based methods by real experimental data in terms of SINR (signal to interference plus noise ratio) and MAPE (mean absolute percentage error).

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Section: Envelope Detection and Sorting Based Interference Mitigation...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

KOIZUMI,

WANG,

UMEHIRA

et al. 2024

IEICE Trans. Fundamentals

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“…Radar system anti-jamming capability is the key to the design, and the strategy includes transmit and receive anti-jamming measures. Receiver anti-jamming in spatial, frequency, and transform domains can be computationally intensive [4] [5] and may not fully exploit waveform design flexibility [6]. Complex frequency domain modulation is thus seen as a way to boost radar performance and jamming resistance.…”

Section: Introductionmentioning

confidence: 99%

Design of low-correlation sidelobe FSK-PSK waveforms with controllable frequency points

Lv,

Liu,

Shi

et al. 2024

J. Phys.: Conf. Ser.

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Frequency shift keying-phase shift keying (FSK-PSK) waveforms are recognized for their superior resolution and signal detectability. However, the optimization of FSK-PSK parameters, particularly frequency hopping control, remains challenging and can lead to increased sidelobe levels. This paper develops an optimization model to substantially enhance the controllability of hopping frequency in FSK-PSK waveforms for radar systems. By harnessing the Karush-Kuhn-Tucker (KKT) conditions, the model strategically minimizes the autocorrelation integrated sidelobe level (ISL), thereby sharpening radar target discrimination and bolstering anti-jamming capabilities. An iterative algorithm specifically addresses the model’s optimal subproblems, optimizing frequency agility and stability. Simulation results show that our FSK-PSK waveform outperforms the waveform optimized by the Multi-Objective Quantum Genetic Algorithm (MoQGA) with a 4.93 dB sidelobe suppression gain, enhancing detection accuracy and anti-jamming performance in disturbed environments.

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“…The authors of [ 23 , 24 , 25 , 26 , 27 ] used different deep learning models and preprocessing algorithms to classify and recognize radar signals, ships, and intracellular molecules. In [ 28 , 29 , 30 ], generative adversarial network and adaptive noise suppression are used to sort and identify modulation features for radar signals, and good results are obtained.…”

Section: Introductionmentioning

confidence: 99%

Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model

Huang

Yan

Hao

et al. 2022

Sensors

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It is difficult for traditional signal-recognition methods to effectively classify and identify multiple emitter signals in a low SNR environment. This paper proposes a multi-emitter signal-feature-sorting and recognition method based on low-order cyclic statistics CWD time-frequency images and the YOLOv5 deep network model, which can quickly dissociate, label, and sort the multi-emitter signal features in the time-frequency domain under a low SNR environment. First, the denoised signal is extracted based on the low-order cyclic statistics of the typical modulation types of radiation source signals. Second, the time-frequency graph of multisource signals was obtained through CWD time-frequency analysis. The cyclic frequency was controlled to balance the noise suppression effect and operation time to achieve noise suppression of multisource signals at a low SNR. Finally, the YOLOv5s deep network model is used as a classifier to sort and identify the received signals from multiple radiation sources. The method proposed in this paper has high real-time performance. It can identify the radiation source signals of different modulation types with high accuracy under the condition of a low SNR.

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An Advanced Wideband Interference Suppression Technique using Envelope Detection and Sorting for Automotive FMCW Radar

Cited by 8 publications

References 11 publications

Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

Design of low-correlation sidelobe FSK-PSK waveforms with controllable frequency points

Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model

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