国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Weighted Sparse Representation based Classification (WSRC) has achieved good results in vehicle recognition in acoustic sensor networks. However, the collaborative representation of all classes in the dictionary actually plays an important role in the Sparse Representation based Classification (SRC).Collaborative Representation based Classification (CRC) is proposed to improve the efficiency of the algorithm. The CRC framework also improves the residual calculation method to improve the recognition accuracy.It is found in WSRC that data locality plays a very good role in improving the recognition rate. Therefore, weighted coding is introduced into CRC, and a vehicle recognition method based on Weighted Collaborative Representation based Classification (WCRC) in acoustic sensor networks is proposed, which achieves obvious speed (compared with WSRC, SRC) and good accuracy (compared with WSRC, CRC, SRC) improvement.At the same time, in view of the shortcomings of Euclidean distance in judging sample similarity, the Manhattan distance is introduced into weighted coding, and a vehicle recognition method based on Manhattan distance Weighted Collaborative Representation based Classification(Manhattan-WCRC) is proposed. Manhattan-WCRC achieves the best recognition rate with almost the same speed as WCRC.