{"title":"利用堆叠自动编码器和时频域特征在低信噪比条件下检测雷达信号","authors":"Yuan Huang, Tao Liu, Ke Li","doi":"10.1117/12.3032046","DOIUrl":null,"url":null,"abstract":"To improve radar signal detection accuracy of traditional methods under low SNR, a detection method based on stacked auto-encoder (SAE) and time-frequency domain features is proposed. The time-domain features, frequency-domain features and joint time-frequency domain features of signal are extracted by SAE to obtain the representative features of radar signal. The extracted features are input into support vector data description (SVDD) for open-set judgment to distinguish radar signal from background signal. Simulation results show that the accuracy and robustness of object detection are improved and the performance of object detection algorithms in complex environments is improved by integrating time-domain features and frequency-domain features information from the target background into detection decisions. It has practical significance for improving the detection accuracy of radar signal detection under low SNR.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radar signal detection under low SNR using stacked auto-encoder and time-frequency domain features\",\"authors\":\"Yuan Huang, Tao Liu, Ke Li\",\"doi\":\"10.1117/12.3032046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To improve radar signal detection accuracy of traditional methods under low SNR, a detection method based on stacked auto-encoder (SAE) and time-frequency domain features is proposed. The time-domain features, frequency-domain features and joint time-frequency domain features of signal are extracted by SAE to obtain the representative features of radar signal. The extracted features are input into support vector data description (SVDD) for open-set judgment to distinguish radar signal from background signal. Simulation results show that the accuracy and robustness of object detection are improved and the performance of object detection algorithms in complex environments is improved by integrating time-domain features and frequency-domain features information from the target background into detection decisions. It has practical significance for improving the detection accuracy of radar signal detection under low SNR.\",\"PeriodicalId\":342847,\"journal\":{\"name\":\"International Conference on Algorithms, Microchips and Network Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Algorithms, Microchips and Network Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.3032046\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Algorithms, Microchips and Network Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.3032046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Radar signal detection under low SNR using stacked auto-encoder and time-frequency domain features
To improve radar signal detection accuracy of traditional methods under low SNR, a detection method based on stacked auto-encoder (SAE) and time-frequency domain features is proposed. The time-domain features, frequency-domain features and joint time-frequency domain features of signal are extracted by SAE to obtain the representative features of radar signal. The extracted features are input into support vector data description (SVDD) for open-set judgment to distinguish radar signal from background signal. Simulation results show that the accuracy and robustness of object detection are improved and the performance of object detection algorithms in complex environments is improved by integrating time-domain features and frequency-domain features information from the target background into detection decisions. It has practical significance for improving the detection accuracy of radar signal detection under low SNR.