Abstract
There are many difficulties in detecting weak targets from actually measured radar echo. In this paper, we present a feasible approach for weak moving target detection based on the mode decomposition algorithms. First, the EMD algorithm combined with the all-phase FFT was utilized to extract the features of weak moving targets for detection and classification. Considering the defects of EMD, the variational mode decomposition (VMD) algorithm substituted for EMD, which is a generation of the self-adaptive Wiener filter, to realize classification and detection of weak moving targets. The proposed method has good robustness to noise. Simultaneously, we gave the detection results of measured radar signals for experimental verification.
Sponsored by Natural Science Foundation of Shanghai (19ZR1454000).
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References
Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proce. A 454(1971), 903–995 (1998)
Oh, B.S., Guo, X., Wan, F.Y., et al.: Micro-Doppler mini-UAV classification using empirical-mode decomposition features. IEEE Geosci. Remote Sens. Lett. 15(2), 227–231 (2018)
Weishaupt, F., Walterscheid, I., Biallawons, O., et al.: Vital Sign Localization and Measurement Using an LFMCW MIMO Radar. The19th International Radar Symposium (2018)
Nguyen, V., Weitnauer, A.M.A.: Denoised maximum likelihood estimation of chest wall displacement from the IR-UWB spectrum. IEEE Access 6, 15248–15258 (2018)
Zhao, G.L., Liang, Q.L., Durrani, T.S.: An EMD based Sense-Through-Foliage Target Detection UWB Radar Sensor Networks. IEEE. Access 6, 29254–29261 (2018)
Chen, Z., Xie, F., Zhao, C., He, C.: Radio frequency interference mitigation for high-frequency surface wave radar. IEEE Geosci. Remote Sens. Lett. 15(7), 986–990 (2018)
Xu, Q., Feng, Y., Jing, Z.: Underground compactness inversion algorithm based on hilbert marginal spectrum. 17th International Conference on Ground Penetrating Radar (GPR), pp. 1–4 (2018)
Zhang, X., Feng, X., Nilot, E., et al.: Noise suppression of GPR data using Variational Mode Decompostion. 17th International Conference on Ground Penetrating Radar (GPR) (2018)
Cexus, J.-C., Toumi, A.: Radar target recognition using time-frequency analysis and polar transformation.4th ATSIP (2018)
Yang, S.L., et al.: A gyroscope signal denoising method based on empirical mode decomposition and signal reconstruction. Sensors 19(23), 50–64 (2019)
Cui, B., Chen, X.: Improved hybrid filter for fiber optic gyroscope signal denoising based on EMD and forward linear prediction. Sens Actuators A Phys. 230, 150–155 (2015)
Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2014)
Bagheri, A., Ozbulut, O.E., Harris, D.K.: Structural system identification based on variational mode decomposition. J. Sound Vib. 417, 182–197 (2018)
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Xia, H., Zhou, C., Yin, J., Gao, L., Liu, Y. (2022). A Feasible Approach for Weak Moving Target Detection Using Radar Echo. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_63
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DOI: https://doi.org/10.1007/978-981-15-8155-7_63
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