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.
標簽： 傳感器網絡
上傳時間： 2016-11-27
上傳用戶：xxmluo
System identification with adaptive filter using full and partial-update Least-Mean-Squares
標簽： Least-Mean-Squares identification partial-update adaptive
上傳時間： 2014-01-02
上傳用戶：bibirnovis
System identification with adaptive filter using full and partial-update Normalised-Least-Mean-Squares
標簽： Normalised-Least-Mean-Squar identification partial-update adaptive
上傳時間： 2017-09-13
上傳用戶：leixinzhuo
System identification with adaptive filter using full and partial-update Transform-Domain Least-Mean-Squares
標簽： Transform-Domain identification partial-update Least-Mean
上傳時間： 2014-01-12
上傳用戶：ztj182002
recursive:數據結構(黃國瑜 葉乃菁 編著)中的遞歸例子
上傳時間： 2013-11-26
上傳用戶：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.
標簽： interesting open-source understood projects
上傳時間： 2015-06-30
上傳用戶：zyt
控制行業(yè)中重要的least square parameter solution，里面使用了一個例子，可以將輸入改變然后使用
標簽： parameter solution square least
上傳時間： 2014-02-13
上傳用戶：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.
標簽： interesting open-source understood projects
上傳時間： 2015-07-26
上傳用戶：mpquest
Least Square - ARMA 算法的MATLAB代碼, 是頻譜分析(通常是在高級DSP這門課中會用到的)的常用算法
上傳時間： 2013-12-21
上傳用戶：zhangjinzj
卡爾曼濾波C程序 卡爾曼濾波器是一個“optimal recursive data processing algorithm（最優(yōu)化自回歸數據處理算法）”。 對于解決很大部分的問題，他是最優(yōu)，效率最高甚至是最有用的。他的廣泛應用已經超過30年，包括機器人導航，控制， 傳感器數據融合甚至在軍事方面的雷達系統(tǒng)以及導彈追蹤等等。近年來更被應用于計算機圖像處理， 例如頭臉識別，圖像分割，圖像邊緣檢測等等。
標簽： processing algorithm recursive optimal
上傳時間： 2013-12-19
上傳用戶：pinksun9
