An Event-Based Approach for the Conservative Compression of Covariance Matrices
arxiv(2024)
摘要
This work introduces a flexible and versatile method for the data-efficient
yet conservative transmission of covariance matrices, where a matrix element is
only transmitted if a so-called triggering condition is satisfied for the
element. Here, triggering conditions can be parametrized on a per-element
basis, applied simultaneously to yield combined triggering conditions or
applied only to certain subsets of elements. This allows, e.g., to specify
transmission accuracies for individual elements or to constrain the bandwidth
available for the transmission of subsets of elements. Additionally, a
methodology for learning triggering condition parameters from an
application-specific dataset is presented. The performance of the proposed
approach is quantitatively assessed in terms of data reduction and
conservativeness using estimate data derived from real-world vehicle
trajectories from the InD-dataset, demonstrating substantial data reduction
ratios with minimal over-conservativeness. The feasibility of learning
triggering condition parameters is demonstrated.
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