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
System identification with adaptive filter using full and partial-update Least-Mean-Squares
标签： Least-Mean-Squares identification partial-update adaptive
上传时间： 2014-01-02
上传用户：bibirnovis
System identification with adaptive filter using full and partial-update Normalised-Least-Mean-Squares
标签： Normalised-Least-Mean-Squar identification partial-update adaptive
上传时间： 2017-09-13
上传用户：leixinzhuo
System identification with adaptive filter using full and partial-update Transform-Domain Least-Mean-Squares
标签： Transform-Domain identification partial-update Least-Mean
上传时间： 2014-01-12
上传用户：ztj182002
recursive:数据结构(黄国瑜 叶乃菁 编著)中的递归例子
上传时间： 2013-11-26
上传用户：上善若水
The Linux kernel is one of the most interesting yet least understood open-source projects. It is also a basis for developing new kernel code. That is why Sams is excited to bring you the latest Linux kernel development information from a Novell insider in the second edition of Linux Kernel Development. This authoritative, practical guide will help you better understand the Linux kernel through updated coverage of all the major subsystems, new features associated with Linux 2.6 kernel and insider information on not-yet-released developments. You ll be able to take an in-depth look at Linux kernel from both a theoretical and an applied perspective as you cover a wide range of topics, including algorithms, system call interface, paging strategies and kernel synchronization. Get the top information right from the source in Linux Kernel Development.
标签： interesting open-source understood projects
上传时间： 2015-06-30
上传用户：zyt
控制行业中重要的least square parameter solution，里面使用了一个例子，可以将输入改变然后使用
标签： parameter solution square least
上传时间： 2014-02-13
上传用户：tb_6877751
The Linux kernel is one of the most interesting yet least understood open-source projects. It is also a basis for developing new kernel code. That is why Sams is excited to bring you the latest Linux kernel development information from a Novell insider in the second edition of Linux Kernel Development. This authoritative, practical guide will help you better understand the Linux kernel through updated coverage of all the major subsystems, new features associated with Linux 2.6 kernel and insider information on not-yet-released developments. You ll be able to take an in-depth look at Linux kernel from both a theoretical and an applied perspective as you cover a wide range of topics, including algorithms, system call interface, paging strategies and kernel synchronization. Get the top information right from the source in Linux Kernel Development.
标签： interesting open-source understood projects
上传时间： 2015-07-26
上传用户：mpquest
Least Square - ARMA 算法的MATLAB代码, 是频谱分析(通常是在高级DSP这门课中会用到的)的常用算法
上传时间： 2013-12-21
上传用户：zhangjinzj
卡尔曼滤波C程序 卡尔曼滤波器是一个“optimal recursive data processing algorithm（最优化自回归数据处理算法）”。 对于解决很大部分的问题，他是最优，效率最高甚至是最有用的。他的广泛应用已经超过30年，包括机器人导航，控制， 传感器数据融合甚至在军事方面的雷达系统以及导弹追踪等等。近年来更被应用于计算机图像处理， 例如头脸识别，图像分割，图像边缘检测等等。
标签： processing algorithm recursive optimal
上传时间： 2013-12-19
上传用户：pinksun9
