Passive Localization Algorithm for Remote Multitarget Localization Information

2020 ◽  
Vol 15 (8) ◽  
pp. 1183-1187
Author(s):  
Gang Niu ◽  
Jie Gao ◽  
Taihang Du
Keyword(s):  
Localization Algorithm ◽  
Passive Localization ◽  
Localization Information

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.

scholarly journals Autonomous Passive Localization Algorithm for Shared Bikes

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 119917-119930
Author(s):  
Xu Yang ◽  
Yuqing Yin ◽  
Zhaoyu Sun ◽  
Shouwan Gao ◽  
Qiang Niu
Keyword(s):  
Localization Algorithm ◽  
Passive Localization

scholarly journals Passive Localization of 3D Near-Field Cyclostationary Sources Using Parallel Factor Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Chen ◽  
Guohong Liu ◽  
Xiaoying Sun
Keyword(s):  
Near Field ◽  
Three Dimensional ◽  
Least Squares Regression ◽  
Localization Algorithm ◽  
Analysis Model ◽  
Third Order ◽  
Multidimensional Search ◽  
Satisfactory Performance ◽  
Parallel Factor ◽  
Passive Localization

By exploiting favorable characteristics of a uniform cross-array, a passive localization algorithm of narrowband cyclostationary sources in the spherical coordinates (azimuth, elevation, and range) is proposed. Firstly, we construct a parallel factor (PARAFAC) analysis model by computing the third-order cyclic moment matrices of the properly chosen sensor outputs. Then, we analyze the uniqueness of the constructed model and obtain three-dimensional (3D) near-field parameters via trilinear alternating least squares regression (TALS). The investigated algorithm is well suitable for the localization of the near-field cyclostationary sources. In addition, it avoids the multidimensional search and pairing parameters. Results of computer simulations are carried out to confirm the satisfactory performance of the proposed method.

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