{"title":"用于最小二乘估计的线性收缩阵列","authors":"M.-J. Chen, K. Yao","doi":"10.1109/ARRAYS.1988.18047","DOIUrl":null,"url":null,"abstract":"The use of square-root-free linear systolic array structure to perform the QR decomposition needed in the solution of least-squares (LS) problems is proposed. A form of the Kalman filter algorithm is applied to perform the recursive LS estimation. Compared with the conventional triangular systolic array structure for LS estimation, the linear array has the advantage of requiring less area and being simpler for VLSI implementation.<<ETX>>","PeriodicalId":339807,"journal":{"name":"[1988] Proceedings. International Conference on Systolic Arrays","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Linear systolic array for least-squares estimation\",\"authors\":\"M.-J. Chen, K. Yao\",\"doi\":\"10.1109/ARRAYS.1988.18047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of square-root-free linear systolic array structure to perform the QR decomposition needed in the solution of least-squares (LS) problems is proposed. A form of the Kalman filter algorithm is applied to perform the recursive LS estimation. Compared with the conventional triangular systolic array structure for LS estimation, the linear array has the advantage of requiring less area and being simpler for VLSI implementation.<<ETX>>\",\"PeriodicalId\":339807,\"journal\":{\"name\":\"[1988] Proceedings. International Conference on Systolic Arrays\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1988-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1988] Proceedings. International Conference on Systolic Arrays\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ARRAYS.1988.18047\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1988] Proceedings. International Conference on Systolic Arrays","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ARRAYS.1988.18047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Linear systolic array for least-squares estimation
The use of square-root-free linear systolic array structure to perform the QR decomposition needed in the solution of least-squares (LS) problems is proposed. A form of the Kalman filter algorithm is applied to perform the recursive LS estimation. Compared with the conventional triangular systolic array structure for LS estimation, the linear array has the advantage of requiring less area and being simpler for VLSI implementation.<>
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