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Transmitter precoding strategies in broadcast systems generally assume perfect knowledge of channel state information (CSI) at the transmitter. In this paper, we study linear precoding with arbitrary error in the CSI from an estimation theoretic point of view. We derive a Bayesian Cramer-Rao type bound on the sum mean squared error (SMSE) achievable at the receiver for any linear precoding scheme...
Transmitter precoding strategies achieve a large portion of the capacity promised in broadcast MIMO systems. However, these schemes generally require perfect channel information at the transmitter. In this paper, the impact of Gaussian additive noise in the channel information is investigated for a downlink system where the transmitter uses a zero forcing precoding strategy. It is shown that noise...
This paper analyzes the impact of uncertain channel information for a Tomlinson-Harashima precoder designed with the zero forcing criterion. The channel uncertainty is modeled as the channel matrix corrupted by additive Gaussian noise. General expressions are derived for the densities of the feedback and feedforward precoding matrices. Under an assumption of small variance in the channel uncertainty,...
A new algorithm for particle filter initialization for multi-sensor bearing only tracking is developed to enhance tracker performance and stability. Multiple bearing observations are used by a least squares technique to form multiple initial position estimates; these estimates are in turn used to compute the statistics of the initial state distribution. Simulated data is used to demonstrate the performance...
We introduce a new algorithm, multiple imputation particle filter, to solve the problem of data fusion with missing data in nonlinear state space models. The new algorithm is then applied to the problem of fusing observations by multiple asynchronous radars. Simulated data is used demonstrate the effectiveness and performance of the fusing algorithm
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