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(wǎng)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
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
LRU(最近最少使用算法) and MRU(最近最常使用算法)所謂的LRU(Least recently used)算法的基本概念是:當內存的剩余的可用空間不夠時,緩沖區(qū)盡可能的先保留使用者最常使用的數(shù)據(jù),換句話說就是優(yōu)先清除”較不常使用的數(shù)據(jù)”,并釋放其空間
上傳時間： 2014-01-03
上傳用戶：彭玖華
This program simulates plant identification least mean square (NLMS) alogrithm reference: 《LMS算法的頻域快速實現(xiàn)》
標簽： identification alogrithm simulates reference
上傳時間： 2013-12-17
上傳用戶：kristycreasy
