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Distributed sensing systems provide an inherent spatial diversity by viewing a potential target from different aspect angles. Even though the centralized fully adapted processing scheme provides optimum performance, it is impractical for reasons of computational complexity and the sample support required for weight training. This paper presents two decentralized reduced rank STAP algorithms to overcome...
This paper considers the problem of space-time adaptive processing (STAP) in non-homogeneous environments, where the disturbance covariance matrices of the training and test signals are assumed random and different with each other. A Bayesian detection statistic is proposed by incorporating the randomness of the disturbance covariance matrices, utilizing a priori knowledge, and exploring the inherent...
Advanced quantization schemes are proposed for closed-loop transmit precoding over correlated multiple-input multiple-output (MIMO) channels in this work. Unlike the conventional schemes that directly quantize the MIMO channel covariance matrix and feed back each quantized matrix element, the proposed schemes quantize the channel covariance matrix by exploiting the common rank-one codebook shared...
In this paper, we propose a robust STBC transmission scheme to combat the timing synchronization errors over frequency-selective multiple-access channels. First, the equivalent channel model in the presence of timing synchronization errors is derived and we find that the synchronization errors result in an equivalent channel model with larger number of correlated channel taps. Based on this correlated...
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