Robust and optimal control
A distributed IMM fusion algorithm for multi-platform tracking
Signal Processing
Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
On robust signal reconstruction in noisy filter banks
Signal Processing - Content-based image and video retrieval
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
H∞ optimality of the LMS algorithm
IEEE Transactions on Signal Processing
Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback
Automatica (Journal of IFAC)
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This paper deals with the problem of tracking a single maneuvering target from multiple platforms in the cluttered environment. A new solution based on H"~ filtering is presented to relax the requirement of a prior knowledge of the noise statistics in the conventional Kalman filter. The contribution of this paper is twofold. First, the distributed H"~ filtering fusion formulae for single model are developed. Second, in order to carry out distributed fusion within the multiple model framework, novel equivalent platform and global models are constructed using the best fitting Gaussian approximation approach so that the developed distributed fusion formulae can be applied directly in the fusion center. The effectiveness of the proposed algorithm is demonstrated through Monte Carlo simulations involving tracking of a highly maneuvering target in the three-dimensional (3D) experiment. The algorithm performs better in a simulated uncertain noise statistics scenario than the Kalman filtering counterpart.