Mitigating Quantization Effects on Distributed Sensor Fusion: A Least Squares Approach - 2018

PROJECT TITLE :

Mitigating Quantization Effects on Distributed Sensor Fusion: A Least Squares Approach - 2018

ABSTRACT:

In this Project, we tend to contemplate the matter of sensor fusion over networks with asymmetric links, where the common goal is linear parameter estimation. For the scenario of bandwidth-constrained networks, existing literature shows that nonvanishing errors always occur, which rely on the quantization scheme. To tackle this difficult issue, we introduce the notion of virtual measurements and propose a distributed resolution LS-DSFS, that may be a combination of a quantized consensus algorithm and the smallest amount squares approach. We give detailed analysis of the LS-DSFS on its performance in terms of unbiasedness and mean square property. Analytical results show that the LS-DSFS is effective in smearing out the quantization errors, and achieving the minimum mean square error (MSE) among the present centralized and distributed algorithms. Moreover, we tend to characterize its rate of convergence in the mean square sense and that of the mean sequence. More importantly, we find that the LS-DSFS outperforms the centralized approaches among a moderate range of iterations in terms of MSE, and can invariably consume less energy and achieve more balanced energy expenditure as the quantity of nodes within the network grows. Simulation results are presented to validate theoretical findings and highlight the enhancements over existing algorithms.

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