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Bounding overwatch
About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.
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TL;DR: KA1 and KA2 are analyzed to compute the rejection probability of a valid tag due to channel errors and it is shown that increasing the number of rounds (iterations) with predefined challenges causes the rejection probabilities to increase.
3 citations
05 Apr 2022
TL;DR: Two deterministic approaches for GNSS uncertainty bounding are introduced and compared and one takes advantage of geometrical constraints and convex optimization, leading to a poly topic solution set and zonotopic confidence region.
Abstract: Uncertainty modeling and bounding are of vital importance for high-integrity GNSS applications. Classical approaches are mostly developed in a stochastic manner with probabilistic assumptions. However, the exact error distribution is often unknown, and remaining systematics may persist, so that a purely stochastic modeling of all error sources will not be adequate, and alternative uncertainty bounding and propagation should be studied. This paper introduces two deterministic approaches for GNSS uncertainty bounding and compares them with the conventional least-squares method theoretically and experimentally with simulated and real measurements. Both methods use deterministic intervals to denote observation un-certainty, subsequently following a linear uncertainty propagation instead of quadratic one. The interval extension of least-squares transfers the uncertainty into the position domain in the form of zonotope and further bound the stochasticity by the extended point confidence domain. As a comparison, the other method takes advantage of geometrical constraints and convex optimization, leading to a poly topic solution set and zonotopic confidence region. We show their theoretical similarities and high-light different interpretations in practice. Nevertheless, both are sufficient to account for both random and systematic components of uncertainty.
3 citations
TL;DR: In this article, an upper bound on the numerical error introduced by the presented spectral diffusion algorithm, in both constant and time-varying conditions, depending on the number of modes and on the time discretization, is provided.
3 citations
Posted Content•
TL;DR: TricueNet as discussed by the authors represents each object as a 2D Tricube kernel and extracts bounding boxes using appearance-based post-processing, which can save the computational complexity and the number of hyperparameters by eliminating the anchor box.
Abstract: We present a new approach for oriented object detection, an anchor-free one-stage detector. This approach, named TricubeNet, represents each object as a 2D Tricube kernel and extracts bounding boxes using appearance-based post-processing. Unlike existing anchor-based oriented object detectors, we can save the computational complexity and the number of hyperparameters by eliminating the anchor box in the network design. In addition, by adopting a heatmap-based detection process instead of the box offset regression, we simply and effectively solve the angle discontinuity problem, which is one of the important problems for oriented object detection. To further boost the performance, we propose some effective techniques for the loss balancing, extracting the rotation-invariant feature, and heatmap refinement. To demonstrate the effectiveness of our TricueNet, we experiment on various tasks for the weakly-occluded oriented object detection. The extensive experimental results show that our TricueNet is highly effective and competitive for oriented object detection. The code is available at this https URL.
3 citations
TL;DR: The model reduction algorithm provides fast, reliable estimates of the parameters required for the reduced-order models and the use of the error measure quantity defined in this work is shown to be of value in evaluating issues related to model suitability.
3 citations