{"title":"用于宽带频率不变光束图案合成的有效卷积神经网络","authors":"Xiao Yan Ju;Yong Qiang Hei;Wen Tao Li","doi":"10.1109/LAWP.2024.3454749","DOIUrl":null,"url":null,"abstract":"In this letter, a convolutional neural network (CNN) based framework is proposed to achieve efficient wideband frequency invariant (FI) beampattern synthesis. The CNN is employed to acquire a set of optimized finite-impulse-response (FIR) filter coefficients corresponding to the desired wideband FI pattern. In the proposed CNN framework, a set of initialized filter coefficients obtained by the upper bound of the desired pattern is taken as the unlabeled input sample, with two input channels representing the real and the imaginary parts of the filter coefficients, respectively. By carefully designing loss function, low sidelobe level (SLL) and frequency invariant beam pattern are well achieved. Then, by minimizing the loss function during the training process, the filter coefficients corresponding to the desired pattern are acquired. Numerical examples involving wideband FI pencil-beam pattern, shaped beam pattern, and scannable beam pattern are provided to validate the flexibility and effectiveness of the proposed method.","PeriodicalId":51059,"journal":{"name":"IEEE Antennas and Wireless Propagation Letters","volume":"23 12","pages":"4538-4542"},"PeriodicalIF":3.7000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effective Convolutional Neural Network for Wideband Frequency Invariant Beam Pattern Synthesis\",\"authors\":\"Xiao Yan Ju;Yong Qiang Hei;Wen Tao Li\",\"doi\":\"10.1109/LAWP.2024.3454749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this letter, a convolutional neural network (CNN) based framework is proposed to achieve efficient wideband frequency invariant (FI) beampattern synthesis. The CNN is employed to acquire a set of optimized finite-impulse-response (FIR) filter coefficients corresponding to the desired wideband FI pattern. In the proposed CNN framework, a set of initialized filter coefficients obtained by the upper bound of the desired pattern is taken as the unlabeled input sample, with two input channels representing the real and the imaginary parts of the filter coefficients, respectively. By carefully designing loss function, low sidelobe level (SLL) and frequency invariant beam pattern are well achieved. Then, by minimizing the loss function during the training process, the filter coefficients corresponding to the desired pattern are acquired. Numerical examples involving wideband FI pencil-beam pattern, shaped beam pattern, and scannable beam pattern are provided to validate the flexibility and effectiveness of the proposed method.\",\"PeriodicalId\":51059,\"journal\":{\"name\":\"IEEE Antennas and Wireless Propagation Letters\",\"volume\":\"23 12\",\"pages\":\"4538-4542\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Antennas and Wireless Propagation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10666005/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Antennas and Wireless Propagation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10666005/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Effective Convolutional Neural Network for Wideband Frequency Invariant Beam Pattern Synthesis
In this letter, a convolutional neural network (CNN) based framework is proposed to achieve efficient wideband frequency invariant (FI) beampattern synthesis. The CNN is employed to acquire a set of optimized finite-impulse-response (FIR) filter coefficients corresponding to the desired wideband FI pattern. In the proposed CNN framework, a set of initialized filter coefficients obtained by the upper bound of the desired pattern is taken as the unlabeled input sample, with two input channels representing the real and the imaginary parts of the filter coefficients, respectively. By carefully designing loss function, low sidelobe level (SLL) and frequency invariant beam pattern are well achieved. Then, by minimizing the loss function during the training process, the filter coefficients corresponding to the desired pattern are acquired. Numerical examples involving wideband FI pencil-beam pattern, shaped beam pattern, and scannable beam pattern are provided to validate the flexibility and effectiveness of the proposed method.
期刊介绍:
IEEE Antennas and Wireless Propagation Letters (AWP Letters) is devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation. These are areas of competence for the IEEE Antennas and Propagation Society (AP-S). AWPL aims to be one of the "fastest" journals among IEEE publications. This means that for papers that are eventually accepted, it is intended that an author may expect his or her paper to appear in IEEE Xplore, on average, around two months after submission.