References
- Chen VC, Li F, Ho SS, et al. Micro-Doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans Aerosp Electron Syst. 2006;42(1):2–21.
- Lei P, Wang J, Guo P, et al. Automatic classification of radar targets with micro-motions using entropy segmentation and time-frequency features. AEU – Int. J. Electron. Commun. 2011;65(10):806–813.
- Li J, Zeng Z, Sun J, et al. Through-wall detection of human being’s movement by UWB radar. IEEE Geosci. Remote Sens. Lett. 2012;9(6):1079–1083.
- Mahafza BR. Radar systems analysis and design using MATLAB. Boca Raton, FL: Chapman and Hall/CRC Press; 2005.
- Chen VC, Tahmoush D, Miceli WJ. Radar micro-Doppler signature processing and applications. London: Institution of Engineering and Technology; 2014.
- Singh V, Bhattacharyya S, Jain PK. Micro-Doppler classification of human movements using spectrogram spatial features and support vector machine. Int. J. RF Microw. Comput. Eng. 2020;30(8):e22264.
- Kong F, Zhang Y, Palmer R, et al. Radar micro-Doppler signature of wind turbines. Radar Sonar Navig. Ser. 2014;34:345–381.
- Rahman S, Robertson DA. Radar micro-Doppler signatures of drones and birds at K-band and W-band. Sci Rep. 2018;8(1):1–11.
- Du L, Li L, Wang B, et al. Micro-Doppler feature extraction based on time-frequency spectrogram for ground moving targets classification with low-resolution radar. IEEE Sens J. 2016;16(10):3756–3763.
- Fioranelli F, Ritchie M, Griffiths H. Aspect angle dependence and multistatic data fusion for micro-Doppler classification of armed/unarmed personnel. IET Radar Sonar Navig. 2015;9(9):1231–1239.
- Yang L, Chen G, Li G. Classification of personnel targets with baggage using dual-band radar. Remote Sens (Basel). 2017;9(6):594.
- Kim Y, Ling H. Human activity classification based on micro-Doppler signatures using a support vector machine. IEEE Trans. Geosci. Remote Sens. 2009;47(5):1328–1337.
- Fairchild DP, Narayanan RM, Beckel ER, et al. (2014). Through-the-wall micro-Doppler signatures. Chapter 5 in Radar micro-Doppler signature-processing and applications.
- Vishwakarma S, Ram SS. Detection of multiple movers based on single channel source separation of their micro-dopplers. IEEE Trans Aerosp Electron Syst. 2018;54(1):159–169.
- Bartoletti S, Conti A, Dai W, et al. Threshold profiling for wideband ranging. IEEE Signal Process Lett. 2018;25(6):873–877.
- Reddy AM, Raj B. Soft mask methods for single-channel speaker separation. IEEE Trans. Audio Speech Lang. Process. 2007;15(6):1766–1776.
- Deng L, Yu D. Deep learning: methods and applications. Found. Trends® Signal Process. 2013;7(3–4):197–387.
- Kim Y, Moon T. Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 2016;13(1):8–12.
- Lathuilière S, Mesejo P, Alameda-Pineda X, et al. A comprehensive analysis of deep regression. IEEE Trans Pattern Anal Mach Intell. 2019;42(9):2065–2081.
- Wang D, Chen J. Supervised speech separation based on deep learning: An overview. IEEE/ACM Trans Audio Speech Lang Process. 2018;26(10):1702–1726.
- Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167; 2015.
- Donelli M. A rescue radar system for the detection of victims trapped under rubble based on the independent component analysis algorithm. Prog. Electromagn. Res. M. 2011;19:173–181.
- Singh V, Bhattacharyya S, Jain PK. Human micro-Doppler intensity transformation for gait velocity estimation. 2020 URSI Regional Conference on Radio Science (URSI-RCRS). IEEE; 2020, February:1–4.