least square estimation of system identification
标签： identification estimation square system
上传时间： 2014-01-08
上传用户：Shaikh
Please read your package and describe it at least 40 bytes in English. System will automatically delete the directory of debug and release, so please do not put files on these two directory.
标签： automatically describe English package
上传时间： 2017-08-29
上传用户：ccclll
Mean shift clustering. K means clustering.
标签： clustering shift means Mean
上传时间： 2014-01-08
上传用户：钓鳌牧马
f you have not registered, Please [regist first].You should upload at least five sourcecodes/documents. (upload 5 files, you can download 200 files). Webmaster will activate your member account after checking your files. If you do not want to upload source code, you can join the [VIP member] to
标签： sourcecodes registered documen Please
上传时间： 2017-09-13
上传用户：ljmwh2000
f you have not registered, Please [regist first].You should upload at least five sourcecodes/documents. (upload 5 files, you can download 200 files). Webmaster will activate your member account after checking your files. If you do not want to upload source code, you can join the [VIP member] to
标签： sourcecodes registered documen Please
上传时间： 2014-01-16
上传用户：fandeshun
Please read your package and describe it at least 40 bytes in English.
标签： describe English package Please
上传时间： 2013-12-06
上传用户：waizhang
Please read your package and describe it at least 40 bytes in English. System will automatically delete the directory of debug and release, so please do not put files on these two directory.
标签： automatically describe English package
上传时间： 2017-09-20
上传用户：alan-ee
a function called fit least square in mathematics with an excel sheet
标签： mathematics function called square
上传时间： 2014-08-12
上传用户：yiwen213
最小二乘法曲面拟合,包括C程序及说明文件。对于搞三维重建的有一定帮助-Least squares surface fitting, including the C procedures and documentation. For engaging in three-dimensional reconstruction to some extent help the
标签： 通信网
上传时间： 2015-11-28
上传用户：schhqq
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
标签： 传感器网络
上传时间： 2016-11-27
上传用户：xxmluo
