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This paper addresses the shrinkage estimation problem of high-dimensional covariance matrices with low sample size data. A class of structured target matrices that include banding, thresholding, diagonal and block diagonal matrices is proposed, and an optimal oracle shrinkage coefficient is derived. To approximate the oracle estimator, an iterative method is presented and proved to be convergent....
For extended-object/group-target tracking (EOT/GTT), the random-matrix approach is appealing. This approach assumes that the measurements are linear in the state and in the noise with its covariance being a random matrix to represent the object extension or the target group. In practice, however, the measurements are nonlinear in the state and noise. This paper proposes a random-matrix approach for...
Double sequential probability ratio test (2-SPRT), as an extended version of SPRT to cope with the no-upper-bound problem, is extended to the multiple-model hypothesis testing (MMHT) approach, called 2-MMSPRT, for detecting unknown events that may have multiple prior distributions. Not only does it address the mis-specified problem of the SPRT based MMHT method (MMSPRT), but it also can be expected...
Most of the existing distributed estimation fusion algorithms rely on the existence of the inverses of the corresponding error covariance matrices, e.g., distributed estimation fusion algorithms based on the information form of the Kalman filter and the optimal weighted least-square (WLS) estimator. Theoretically speaking, the error covariance matrices are only at least positive semi-definite and...
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