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Combining the Least\u2010Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN\u2010GP), the CN\u2010LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN\u2010LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one\u2010dimensional CNN method and the Long Short\u2010Term Memory (LSTM) network method.<\/jats:p>","DOI":"10.1155\/2021\/6664530","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T18:05:38Z","timestamp":1618337138000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Time\u2010Frequency Analysis and Target Recognition of HRRP Based on CN\u2010LSGAN, STFT, and CNN"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6135-9808","authenticated-orcid":false,"given":"Jianghua","family":"Nie","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7815-9431","authenticated-orcid":false,"given":"Yongsheng","family":"Xiao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2148-2902","authenticated-orcid":false,"given":"Lizhen","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Lv","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2011.2141664"},{"key":"e_1_2_8_2_2","doi-asserted-by":"crossref","unstructured":"Lund\u00e9nJ.andKoivunenV. 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