期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 66, 期 10, 页码 4939-4944出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2020.3046567
关键词
Transportation; Estimation; Density measurement; Weight measurement; Gaussian processes; Covariance matrices; Symmetric matrices; Convex optimization; generalized covariance extension problem; optimal transport; spectral analysis
资金
- Department of Information Engineering of the University of Padova SID Project A Multidimensional and Multivariate Moment Problem Theory for Target Parameter Estimation in Automotive Radars'' [ZORZ_SID19_01]
This article examines the optimal transport problem between multivariate Gaussian stationary stochastic processes, introducing the concept of transportation effort as the variance of the filtered discrepancy process. The main contribution is demonstrating that the solution leads to a weighted Hellinger distance between multivariate power spectral densities, and proposing a spectral estimation method based on this distance in the case of indirect measurements.
We consider the optimal transport problem between multivariate Gaussian stationary stochastic processes. The transportation effort is the variance of the filtered discrepancy process. The main contribution of this article is to show that the corresponding solution leads to a weighted Hellinger distance between multivariate power spectral densities. Then, we propose a spectral estimation approach in the case of indirect measurements, which is based on this distance.
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