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Error Registration for Temporal and Spatial Alignment on HFSWR
Author: RenXueFei
Tutor: QuanTaiFan
School: Harbin Institute of Technology
Course: Information and Communication Engineering
Keywords: HF ground wave Extended Kalman Filter Particle Filter The method of least squares Lagrangian method
CLC: TP202
Type: Master's thesis
Year: 2011
Downloads: 28
Quote: 0
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Abstract
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Space-time alignment problem has become an important research topic in the field of data fusion , to explore the multi-sensor data fusion of multi-source spatial and temporal alignment , and to improve the overall performance of the system in the multi- sensor data fusion for multi-sensor source information fusion research is of great significance . The background of this study is the over-the-horizon detection information . Ground wave radar to fully take advantage of high frequency electromagnetic waves with vertical polarization can coastal plane diffraction from the characteristics of the curvature of the Earth , not the line-of-sight , over-the-horizon detection of sea air . It is also the surface propagation of electromagnetic waves , such multi- sensor networking space the coordinate alignment problems complicate . This paper fully consider the propagation of electromagnetic waves along the Earth's surface characteristics , the spatial geometry of the basic relationship , space alignment algorithm in the context of the over-the-horizon target detection , and analysis of the characteristics of the coordinate conversion error . The error in the multi-sensor information fusion system can be simply divided into systematic error and random error . The system error in this article consider a system work time period is fixed. Random error is generally believed that the normal distribution with mean zero Gaussian distribution . In reference to a number of documents on the basis of modeling of both random and systematic errors . Kalman filter as the basic theory , the system of the target 's location , speed and system error as a state vector for the nonlinear system , the application of the extended Kalman filter , iterated extended Kalman filter and particle filter estimates of the target state , while to obtain a real-time estimates of the system errors . Time alignment of many sources of error , this article focused on networking sensors sampling cycle out of sync due time does not match and alignment errors . In this paper, simulation analysis of the least squares method and Lagrange interpolation , both effective Lagrangian algorithm more widely .
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