Analysis of location accuracy of low orbit dual-satellites passive location system based on differential method

2018 ◽  
pp. 589-593
Keyword(s):  
Differential Method ◽  
Passive Location ◽  
Location Accuracy ◽  
Location System

Sensor registration technique for passive location system

2009 ◽  
Author(s):  
Xiong Wei ◽  
Pan Xu-dong ◽  
Peng Ying-ning
Keyword(s):  
Passive Location ◽  
Location System ◽  
Sensor Registration

The Application of Kalman Filtering on Vessel Positioning System

2012 ◽  
Vol 263-266 ◽  
pp. 501-505
Author(s):  
Xiong Dong Ding ◽  
Guang Qian Chu
Keyword(s):  
Measurement Error ◽  
Kalman Filtering ◽  
Measurement Errors ◽  
Positioning System ◽  
Location Accuracy ◽  
Wireless Location ◽  
Location System ◽  
The Subject ◽  
Channel Environment

In the ocean, due to the non-ideal channel environment, it is difficult to locate the position of the vessel in the sea quickly and exactly. In order to improve the location accuracy of accident vessel in the ocean, one method is to analyze the main cause of the different kinds of the measurement errors to find their solutions to decrease the errors. The subject that Kalman filtering technology is applied in wireless location system is researched in this paper, which can reduce the measurement error and greatly improve the location accuracy.

A blind signal detection algorithm for passive location system based on troposcatter

2018 ◽  
Vol 10 (10) ◽  
pp. 1128-1133
Author(s):  
Zan Liu ◽  
Xihong Chen ◽  
Qiang Liu ◽  
Zedong Xie
Keyword(s):  
Signal Detection ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Passive Location ◽  
Intrinsic Mode Functions ◽  
Location System ◽  
Blind Signal ◽  
Mode Decomposition ◽  
Signal Simulation

AbstractTo improve detection performance of passive location system based on troposcatter, we propose a blind signal detection algorithm. According to our algorithm, complementary ensemble empirical mode decomposition decomposes the received signal into several intrinsic mode functions (IMFs). To reconstruct the signal and background noises, difference between the entropy of adjacent IMFs is utilized as a standard. Different IMFs are utilized to estimate threshold of energy detection algorithm and energy level of received signal. Simulation examples indicate that the proposed algorithm can blindly and effectively detect the signal.

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