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Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
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Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3069; https://doi.org/10.3390/s19143069
Received: 22 May 2019 / Revised: 23 June 2019 / Accepted: 10 July 2019 / Published: 11 July 2019
(This article belongs to the Section Sensor Networks)
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Abstract

Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy. View Full-Text
Keywords: target tracking; distributed estimation; consensus strategy; information filter; sensor network target tracking; distributed estimation; consensus strategy; information filter; sensor network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, Z.; Wang, Y.; Zheng, W. Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise. Sensors 2019, 19, 3069.

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