[1] Fairchild D P, Narayanan R M. Classification of human motions using empirical mode decomposition of human micro-Doppler signatures[J]. IET Radar, Sonar & Navigation, 8, 425-434(2014).

[2] Zhang Y, Zhu Y P, Cheng Y Q et al. Human target radar echo signal analysis based on micro-Doppler characteristic[J]. Signal Processing, 25, 1616-1623(2009).

[3] Huang Y W, Wan C L, Feng H. Multi-feature fusion human behavior recognition algorithm based on convolutional neural network and long short term memory neural network[J]. Laser & Optoelectronics Progress, 56, 071505(2019).

[4] Ikeda T, Ishiguro H, Miyashita T et al. Pedestrian identification by associating wearable and environmental sensors based on phase dependent correlation of human walking[J]. Journal of Ambient Intelligence and Humanized Computing, 5, 645-654(2014).

[5] Xu H Y, Kong J, Jiang M et al. Action recognition based on histogram of spatio-temporal oriented principal components[J]. Laser & Optoelectronics Progress, 55, 061009(2018).

[6] van Dorp P, Groen F C A. Feature-based human motion parameter estimation with radar[J]. IET Radar, Sonar & Navigation, 2, 135-145(2008).

[7] Kim Y, Ling H. Human activity classification based on micro-Doppler signatures using a support vector machine[J]. IEEE Transactions on Geoscience and Remote Sensing, 47, 1328-1337(2009).

[8] Ricci R, Balleri A. Recognition of humans based on radar micro-Doppler shape spectrum features[J]. IET Radar, Sonar & Navigation, 9, 1216-1223(2015).

[9] Li J, Phung S L. Tivive F H C, et al. Automatic classification of human motions using Doppler radar. [C]∥The 2012 International Joint Conference on Neural Networks (IJCNN), June 10-15, 2012, Brisbane, QLD, Australia. New York: IEEE, 12906230(2012).

[10] Javier R J, Kim Y. Application of linear predictive coding for human activity classification based on micro-Doppler signatures[J]. IEEE Geoscience and Remote Sensing Letters, 11, 1831-1834(2014).

[11] Tahmoush D. Review of micro-Doppler signatures[J]. IET Radar, Sonar & Navigation, 9, 1140-1146(2015).

[12] Kim Y, Moon T. Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 13, 8-12(2016).

[13] Cao P B, Xia W J, Ye M et al. Radar-ID: human identification based on radar micro-Doppler signatures using deep convolutional neural networks[J]. IET Radar, Sonar & Navigation, 12, 729-734(2018).

[14] Chen Z X, Li G, Fioranelli F et al. Personnel recognition and gait classification based on multistatic micro-Doppler signatures using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 15, 669-673(2018).

[15] Seyfioglu M S, Ozbayoglu A M, Gurbuz S Z. Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities[J]. IEEE Transactions on Aerospace and Electronic Systems, 54, 1709-1723(2018).

[16] Kim Y, Li Y. Human activity classification with transmission and reflection coefficients of on-body antennas through deep convolutional neural networks[J]. IEEE Transactions on Antennas and Propagation, 65, 2764-2768(2017).

[17] Klarenbeek G. Harmanny R I A, Cifola L. Multi-target human gait classification using LSTM recurrent neural networks applied to micro-Doppler. [C]∥2017 European Radar Conference (EURAD), October 11-13, 2017, Nuremberg, Germany. New York: IEEE, 167-170(2017).

[18] Lu Y X, Kumar A, Zhai S F et al. Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 1131-1140(2017).

[19] Long M S, Cao Z J, Wang J M et al. Learning multiple tasks with multilinear relationship networks. [C]∥Advances in Neural Information Processing Systems 30 (NIPS 2017), December 4-9, 2017, Long Beach, CA, USA. Canada: NIPS, 1594-1603(2017).

[20] Hashimoto K, Xiong C M, Tsuruoka Y et al. -07-24)[2019-06-09]. https:∥arxiv., org/abs/1611, 01587(2017).

[21] Kendall A, Gal Y, Cipolla R. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-22, 2018, Salt Lake City, Utah. New York: IEEE, 7482-7491(2018).

[22] Liu Y P, Wang C, Xia H Y. Application progress of time-frequency analysis for lidar[J]. Laser & Optoelectronics Progress, 55, 120005(2018).

[23] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2019).

[24] Zhou G B, Wu J X, Zhang C L et al. Minimal gated unit for recurrent neural networks[J]. International Journal of Automation and Computing, 13, 226-234(2016).

[25] Wen Y D, Zhang K P, Li Z F et al. A discriminative feature learning approach for deep face recognition[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9911, 499-515(2016).