An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Sin...An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.展开更多
引言这个纪念集将连续两期出现在《美国数学会通讯(Notices of the AMS)》中,赞扬75年来最有影响力的数学家之一Isadore Singer(辛格)的生平和工作.Singer生于1924年5月3日,于2021年2月11日去世。各种易于获取的资源深入讲述了他丰富的...引言这个纪念集将连续两期出现在《美国数学会通讯(Notices of the AMS)》中,赞扬75年来最有影响力的数学家之一Isadore Singer(辛格)的生平和工作.Singer生于1924年5月3日,于2021年2月11日去世。各种易于获取的资源深入讲述了他丰富的人生故事.展开更多
As a subfield of Multimedia Information Retrieval(MIR), Singer IDentification(SID) is still in the research phase. On one hand, SID cannot easily achieve high accuracy because the singing voice is difficult to model a...As a subfield of Multimedia Information Retrieval(MIR), Singer IDentification(SID) is still in the research phase. On one hand, SID cannot easily achieve high accuracy because the singing voice is difficult to model and always disturbed by the background instrumental music. On the other hand, the performance of conventional machine learning methods is limited by the scale of the training dataset. This study proposes a new deep learning approach based on Long Short-Term Memory(LSTM) and Mel-Frequency Cepstral Coefficient(MFCC) features to identify the singer of a song in large datasets. The results of this study indicate that LSTM can be used to build a representation of the relationships between different MFCC frames. The experimental results show that the proposed method achieves better accuracy for Chinese SID in the MIR-1 K dataset than the traditional approaches.展开更多
基金supported by the National Natural Science Foundation of China(6153102061471383)
文摘An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.
基金supported by the National Natural Science Foundation of China(Nos.61402210 and 60973137)the Program for New Century Excellent Talents in University(No.NCET-12-0250)+4 种基金the Major Project of HighResolution Earth Observation System(No.30-Y20A34-9010-15/17)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA03030100)the Gansu Sci.&Tech.Program(Nos.1104GKCA049,1204GKCA061,and 1304GKCA018)the Fundamental Research Funds for the Central Universities(No.lzujbky-2016-140)the support of NVIDIA Corporation with the donation of the Jetson TX1 used for this research
文摘As a subfield of Multimedia Information Retrieval(MIR), Singer IDentification(SID) is still in the research phase. On one hand, SID cannot easily achieve high accuracy because the singing voice is difficult to model and always disturbed by the background instrumental music. On the other hand, the performance of conventional machine learning methods is limited by the scale of the training dataset. This study proposes a new deep learning approach based on Long Short-Term Memory(LSTM) and Mel-Frequency Cepstral Coefficient(MFCC) features to identify the singer of a song in large datasets. The results of this study indicate that LSTM can be used to build a representation of the relationships between different MFCC frames. The experimental results show that the proposed method achieves better accuracy for Chinese SID in the MIR-1 K dataset than the traditional approaches.
