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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
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.
最小二乘法曲面擬合,包括C程序及說明文件。對于搞三維重建的有一定幫助-Least squares surface fitting, including the C procedures and documentation. For engaging in three-dimensional reconstruction to some extent help the
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.
