Iterative modified threshold method based on EMD for interference suppression in FMCW radars

The mutual interference between frequency-modulated continuous-wave (FMCW) automotive radars increases the noise floor, even submerges the information of targets, and creates ghost targets. To reduce the impact of the mutual interference between automotive radars and improve the detection performance, this study proposes an iterative modified threshold method based on empirical mode decomposition (IMT-EMD) for interference suppression in FMCW automotive radars. The IMT-EMD uses the consecutive mean square error algorithm to determine the interference-dominated components after decomposing, which are adjusted according to the modified threshold. The proposed method can mitigate the interference effectively while keeping the signals continuous and reducing the loss of useful signals as much as possible. To further improve the detection performance, the authors used the proposed algorithm iteratively. The improvement of the signal-to-noise ratio of targets will converge to an optimal value when the number of iterations reaches a certain value after using the IMT-EMD. The results of simulation and field experiments showed that the proposed algorithm has a satisfactory effect on interference suppression and improves detection performance.

[1]  Jiwon Choi,et al.  Mutual Interference Suppression Using Clipping and Weighted-Envelope Normalization for Automotive FMCW Radar Systems , 2016, IEICE Trans. Commun..

[2]  E. J. Kelly.  An Adaptive Detection Algorithm , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Tao Jin,et al.  A Novel Adaptive EEMD Method for Switchgear Partial Discharge Signal Denoising , 2019, IEEE Access.

[4]  V. A. Protopopescu,et al.  Empirical Mode Decomposition Technique With Conditional Mutual Information for Denoising Operational Sensor Data , 2011, IEEE Sensors Journal.

[5]  Min Gao,et al.  Adaptive modulation interval filtering algorithm based on empirical mode decomposition , 2019 .

[6]  Yifei Tong,et al.  A UV-visible absorption spectrum denoising method based on EEMD and an improved universal threshold filter , 2018, RSC advances.

[7]  Ramy Hussein,et al.  Energy conservation-based thresholding for effective wavelet denoising of partial discharge signals , 2016 .

[8]  Tariq S. Durrani,et al.  An EMD Based Sense-Through-Foliage Target Detection UWB Radar Sensor Networks , 2018, IEEE Access.

[9]  Yang Song,et al.  Ensemble EMD-based signal denoising using modified interval thresholding , 2017, IET Signal Process..

[10]  Mei Li,et al.  An Improved EMD Method for Time–Frequency Feature Extraction of Telemetry Vibration Signal Based on Multi-Scale Median Filtering , 2015, Circuits Syst. Signal Process..

[11]  Aydin Akan,et al.  Detrended fluctuation thresholding for empirical mode decomposition based denoising , 2014, Digit. Signal Process..

[12]  Fei Xie,et al.  Radio Frequency Interference Mitigation for High-Frequency Surface Wave Radar , 2018, IEEE Geoscience and Remote Sensing Letters.

[13]  I. Mostafanezhad,et al.  Cancellation of Unwanted Doppler Radar Sensor Motion Using Empirical Mode Decomposition , 2013, IEEE Sensors Journal.

[14]  G.M. Brooker,et al.  Mutual Interference of Millimeter-Wave Radar Systems , 2007, IEEE Transactions on Electromagnetic Compatibility.

[15]  Abdel-Ouahab Boudraa,et al.  EMD-Based Signal Filtering , 2007, IEEE Transactions on Instrumentation and Measurement.

[16]  Siyang Cao,et al.  Automotive Radar Interference Mitigation Using Adaptive Noise Canceller , 2019, IEEE Transactions on Vehicular Technology.

[17]  Mengdao Xing,et al.  Narrow-Band Interference Suppression for SAR Based on Complex Empirical Mode Decomposition , 2009, IEEE Geoscience and Remote Sensing Letters.

[18]  Yuanyuan Liu,et al.  EMD interval thresholding denoising based on similarity measure to select relevant modes , 2015, Signal Process..

[19]  Xinhua Guo,et al.  EMD Threshold Denoising Algorithm Based on Variance Estimation , 2018, Circuits Syst. Signal Process..

[20]  Wen-Liang Hwang,et al.  EMD Revisited: A New Understanding of the Envelope and Resolving the Mode-Mixing Problem in AM-FM Signals , 2012, IEEE Transactions on Signal Processing.

[21]  John J. Soraghan,et al.  Empirical mode decomposition-based monopulse processor for enhanced radar tracking in the presence of high-power interference , 2011 .

[22]  Sherif A. Elgamel.  Using empirical mode decomposition and fractional Fourier transform-segment filtering algorithm to suppress high-power interference in non-linear chirp radars , 2016 .