TDOA based data association and multi-targets passive localization algorithm

2014 ◽  
Author(s):  
Hongwei Li ◽  
Chun Li
Keyword(s):  
Data Association ◽  
Localization Algorithm ◽  
Passive Localization

scholarly journals Performance Analysis of AOA-Based Localization Using the LS Approach: Explicit Expression of Mean-Squared Error

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Byung-Kwon Son ◽  
Do-Jin An ◽  
Joon-Ho Lee
Keyword(s):  
Explicit Expression ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Squared Error ◽  
Distance Weighted ◽  
The Mean ◽  
Passive Localization ◽  
Location Estimate

In this paper, a passive localization of the emitter using noisy angle-of-arrival (AOA) measurements, called Brown DWLS (Distance Weighted Least Squares) algorithm, is considered. The accuracy of AOA-based localization is quantified by the mean-squared error. Various estimates of the AOA-localization algorithm have been derived (Doğançay and Hmam, 2008). Explicit expression of the location estimate of the previous study is used to get an analytic expression of the mean-squared error (MSE) of one of the various estimates. To validate the derived expression, we compare the MSE from the Monte Carlo simulation with the analytically derived MSE.

Autonomous Passive Localization Algorithm for Road Sensor Networks

2011 ◽  
Vol 60 (11) ◽  
pp. 1622-1637 ◽  
Author(s):  
Jaehoon Jeong ◽  
Shuo Guo ◽  
Tian He ◽  
D. H. C. Du
Keyword(s):  
Sensor Networks ◽  
Localization Algorithm ◽  
Passive Localization

Optimum Sensor Array for Passive Localization from Time Differences of Arrival

2014 ◽  
Vol 721 ◽  
pp. 411-415
Author(s):  
Cheng Zhou ◽  
Gao Ming Huang ◽  
Jun Gao
Keyword(s):  
Lower Bound ◽  
Unbiased Estimator ◽  
Sensor Array ◽  
Sensor Placement ◽  
Localization Algorithm ◽  
Localization Performance ◽  
Cramer Rao Lower Bound ◽  
Optimum Sensor ◽  
Optimum Sensor Placement ◽  
Passive Localization

The problem how to improve the accuracy of passive localization from time differences of arrival received considerable interest. The localization performance of any unbiased estimator can be explicitly characterized by certain measures, for example, by the Cramer-Rao lower bound (CRLB) on the estimator variance. The lower the CRLB, the better localization performance. It is well known that the relative sensor-target geometry can significantly affect the performance of any particular localization algorithm. It demonstrates, when target is surrounded by the sensors, uniform angular array is the optimum sensor placement, in which the CRLB is minimized.

Sign in / Sign up

Export Citation Format

Share Document