Distributed fusion for satellite passive localization using convex combination

In this paper, distributed data fusion for satellite passive localization is examined. The traditional single or multiple satellite passive localization systems usually use a fixed localization mode which commonly has poor fault tolerance capability. We investigate a distributed passive localization...

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Vydáno v:2010 IEEE International Conference on Information Theory and Information Security s. 994 - 997
Hlavní autoři: Zhengbin Yang, Yan Qiu, Xiaoxing Li, Yong Kang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2010
Témata:
Accuracy
Adaptive filters
convex combination
data fusion
Earth
Estimation
Kalman filters
nonlinear estimation
satellite passive localization
Satellites
Sensors
SPKF
ISBN:1424469422, 9781424469420
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Shrnutí:In this paper, distributed data fusion for satellite passive localization is examined. The traditional single or multiple satellite passive localization systems usually use a fixed localization mode which commonly has poor fault tolerance capability. We investigate a distributed passive localization scheme using angle only measurements from multiple satellites. However, recursive fusion algorithms are subject to different tradeoffs regarding their speed of convergence and convergence accuracy, and the satellite passive localization applications usually suffer large initial error. The convex combination is recently used to combine different adaptive filters to obtain better filtering performance than the original filters. To improve the fusion performance, data fusion algorithm using convex combination is developed based on sigma point Kalman filter (SPKF). Computer simulation experiments are performed to show the effectiveness of the proposed localization scheme and algorithm.
ISBN:1424469422
9781424469420
DOI:10.1109/ICITIS.2010.5689655