Yun Tao Xu
ABSTRACT
A multi-component separation method using Constant False Alarm Rate (CFAR) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methods and Ridge Path Regrouping (RPRG) is proposed aiming at the problem that the method based on Intrinsic Chirp Component Decomposition (ICCD) and RPRG needs to know the number of Instantaneous Frequency (IF) components. CFAR is used to detect points that may be ridges in T-F Representation (TFR), and then DBSCAN method is used to divide these points into multiple classes. The Maximum Likelihood Estimation (MLE) is used to estimate the number of IF components in the signal. The ridge paths of all IF components in all TFRs can be estimated based on this estimate using the RPRG method. The problem of frequency component separation in one-dimensional signal was transformed by this method into a path detection problem in two-dimensional TFR. Simulation and experimental data show that the ridge path estimation method based on CFAR and DBSCAN can achieve similar results with the original method when the number of components is unknown and the SNR is 5dB. The simulation MATLAB code of this paper is in: https://gitee.com/xuyuntao/md-curve-extract.git
- V. C. Chen, F. Li, S.-S. Ho, and H. Wechsler, “Micro-Doppler effect in radar: phenomenon, model, and simulation study,” IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 1, pp. 2–21, 2006, doi: 10.1109/TAES.2006.1603402Google Scholar
Cross Ref
- A. Hanif, M. Muaz, A. Hasan, and M. Adeel, “Micro-Doppler Based Target Recognition With Radars: A Review,” IEEE Sensors Journal, vol. 22, no. 4, pp. 2948–2961, Feb. 2022, doi: 10.1109/JSEN.2022.3141213Google ScholarCross Ref
- N. A. Khan and S. Ali, “A robust and efficient instantaneous frequency estimator of multi-component signals with intersecting time-frequency signatures,” Signal Processing, vol. 177, p. 107728, Dec. 2020, doi: 10.1016/j.sigpro.2020.107728Google ScholarCross Ref
- S. Li, X. Si, C. Zhang, and J. Liang, “Micro-doppler Feature Extraction of UAVs Based on Synchrosqueezed Transform and Ridge Path Regrouping,” in Communications, Signal Processing, and Systems, Q. Liang, W. Wang, X. Liu, Z. Na, and B. Zhang, Eds., in Lecture Notes in Electrical Engineering. Singapore: Springer Nature, 2023, pp. 20–27. doi: 10.1007/978-981-99-1260-5_3Google ScholarCross Ref
- Y. Hu, X. Tu, F. Li, Y. Zhu, and J. Lu, “Adaptive instantaneous frequency ridge extraction based on target tracking for frequency-modulated signals,” ISA Transactions, vol. 128, pp. 665–674, Sep. 2022, doi: 10.1016/j.isatra.2021.10.011Google ScholarCross Ref
- Y. Li, W. Xia, and S. Dong, “Time-based multi-component irregular FM micro-Doppler signals decomposition via STVMD,” IET Radar, Sonar & Navigation, vol. 14, no. 10, pp. 1502–1511, 2020, doi: 10.1049/iet-rsn.2020.0091Google ScholarCross Ref
- N. A. Khan and I. Djurović, “ADTFD-RANSAC For multi-component IF estimation,” Signal Processing, vol. 195, p. 108494, Jun. 2022, doi: 10.1016/j.sigpro.2022.108494Google ScholarDigital Library
- N. A. Khan and S. Ali, “Multi-component instantaneous frequency estimation in mono-sensor and multi-sensor recordings with application to source localization,” Multidim Syst Sign Process, vol. 32, no. 3, pp. 959–973, Jul. 2021, doi: 10.1007/s11045-021-00769-wGoogle ScholarDigital Library
- N. E. Huang , “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A, vol. 454, no. 1971, pp. 903–995, Mar. 1998, doi: 10.1098/rspa.1998.0193Google ScholarCross Ref
- K. Dragomiretskiy and D. Zosso, “Variational Mode Decomposition,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531–544, Feb. 2014, doi: 10.1109/TSP.2013.2288675Google ScholarDigital Library
- S. Chen, X. Dong, Z. Peng, W. Zhang, and G. Meng, “Nonlinear Chirp Mode Decomposition: A Variational Method,” IEEE Transactions on Signal Processing, vol. 65, no. 22, pp. 6024–6037, Nov. 2017, doi: 10.1109/TSP.2017.2731300Google ScholarDigital Library
- J. Gilles, “Empirical Wavelet Transform,” IEEE Transactions on Signal Processing, vol. 61, no. 16, pp. 3999–4010, Aug. 2013, doi: 10.1109/TSP.2013.2265222Google ScholarDigital Library
- S. Chen, X. Dong, G. Xing, Z. Peng, W. Zhang, and G. Meng, “Separation of Overlapped Non-Stationary Signals by Ridge Path Regrouping and Intrinsic Chirp Component Decomposition,” IEEE Sensors J., vol. 17, no. 18, pp. 5994–6005, Sep. 2017, doi: 10.1109/JSEN.2017.2737467Google ScholarCross Ref
- Y. ZHAO, Y. NIE, L. WU, Y. ZHANG, and S. HE, “Multi-component signal separation using variational nonlinear chirp mode decomposition based on ridge tracking,” Journal of Zhejiang University (Engineering Science), vol. 54, no. 10, pp. 1874–1882, 2020, doi: 10.3785/j.issn.1008-973X.2020.10.002.Google ScholarCross Ref
- Y. CHENG, J. SHAO, and Y. ZHAO, “Parameters Estimation for Multi-Component Polynomial Phase Signal by Combining RPRG and ICCD with RANSAC,” Electronic Sci. & Tech., vol. 33, no. 2, pp. 66–70, 2020, doi: 10.16180/j.cnki.issn1007-7820.2020.02.012Google ScholarCross Ref
- H. Rohling, “Radar CFAR Thresholding in Clutter and Multiple Target Situations,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-19, no. 4, pp. 608–621, Jul. 1983, doi: 10.1109/TAES.1983.309350Google ScholarCross Ref
- M. Ester, H.-P. Kriegel, J. Sander, X. Xu, and others, “A density-based algorithm for discovering clusters in large spatial databases with noise.,” in kdd, 1996, pp. 226–231Google Scholar
- V. C. Chen, The micro-Doppler effect in radar, Second. in Artech House radar series. Norwood, MA: Artech House, 2013Google Scholar
Index Terms
- Multi-component Signal Separation Using CFAR and DBSCAN Based on Ridge Path Regrouping
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