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Practical method to predict an upper bound for minimum variance track-to-track fusion

Zarei Jalalabadi, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1049/iet-spr.2017.0121
  3. Abstract:
  4. This study deals with the problem of track-to-track fusion in a sensor network when the correlation terms between the estimates of the agents are unknown. The proposed method offers an upper bound for the optimal minimum variance fusion rule through construction of the correlation terms according to an optimisation scheme. In general, the upper bound filter provides an estimate that is more conservative than the optimal estimate generated by the minimum variance fusion rule, while at the same time is less conservative than one obtained by the widely used covariance intersection method. From the geometrical viewpoint, the upper bound filter results in the inscribed largest volume ellipsoid within the intersection region defined by the ellipsoids corresponding to the fused estimates while the covariance intersection leads to the external minimum volume ellipsoid over the intersection region. The authors demonstrate the superiority of the proposed method through analysing estimation error and consistency of the fusion filter over Monte-Carlo simulations for a multi-dimensional system. © The Institution of Engineering and Technology 2017
  5. Keywords:
  6. Intelligent systems ; Sensor networks ; Covariance intersection ; Estimation errors ; Minimum variance ; Minimum volume ellipsoids ; Multidimensional systems ; Optimal estimates ; Practical method ; Track-to-track fusion ; Monte carlo methods
  7. Source: IET Signal Processing ; Volume 11, Issue 8 , 2017 , Pages 961-968 ; 17519675 (ISSN)
  8. URL: https://ieeexplore.ieee.org/document/8056556