.png){kind=icon}
.png){kind=icon}
Return
A Convex Relaxation Algorithm for Source Localization Considering Sensor Motion in Wireless Sensor Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2021.3062668.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this letter, by using the effect of sensor motion on time delay and Doppler shift measurements, a semi-definite programming (SDP) solution is developed via convex relaxation to determine the unknown source location. A novel constrained weighted least squares (CWLS) problem is formulated according to the non-recursive measurement models without any approximation. After that, semi-definite relaxation (SDR) is performed to transform the CWLS problem into a convex programming problem, which can guarantee the global optimal solution. Simulation results show that the proposed method can provide a significant reduction in mean-square error (MSE) compared with the existing methods.
Keywords:
Location awareness
Time measurement
Noise measurement
Delay effects
Doppler shift
Motion measurement
Q measurement
Source localization
semi-definite programming (SDP)
doppler frequency shift
sensor motion
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W