An Event-Based Approach for the Conservative Compression of Covariance Matrices

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.Comment: 12 pages, 9 figures, submitted to: IEEE Transactions on Automatic Contro

Event-based State Estimation in Multisensor Systems

Nowadays sensors are implemented in countless of actual scenarios ranging from securityto entertainment applications. They generate a huge amount of transmissionswithin the network they belong to, resulting in a costly communication e↵ort. Inorder to optimize the transmission process, an event-based system – instead of theconventional periodic approach – should be used. In this sense, several challengesappear when multiple sensors are involved at the same time, where new issues abouttheir event-criteria arise, i.e., how could sensors compare their observations to makea transmission decision. To this e↵ect, communication between sensor nodes is tobe studied seeking to utilize information in a profitable way. In this work, di↵erentmultisensor network structures are to be compared, i.e., star, chain, and hierarchicaltopologies. Finally, quality will be deeply discussed in terms of estimation’s qualitydegradation due to the proposed joint trigger criteria as compared to independentevent triggers.<br /

KPZ-type fluctuation bounds for interacting diffusions in equilibrium

We study the fluctuations in equilibrium of a class of Brownian motions interacting through a potential. For a certain choice of exponential potential, the distribution of the system coincides with differences of free energies of the stationary semi-discrete or O'Connell-Yor polymer. We show that for Gaussian potentials, the fluctuations are of order $N^{\frac{1}{4}}$ when the time and system size coincide, whereas for a class of more general convex potentials $V$ the fluctuations are of order at most $N^{\frac{1}{3}}$. In the O'Connell-Yor case, we recover the known upper bounds for the fluctuation exponents using a dynamical approach, without reference to the polymer partition function interpretation.Comment: 43 page

Event-based sensor fusion in human-machine teaming

Realizing intelligent production systems where machines and human workers can team up seamlessly demands a yet unreached level of situational awareness. The machines&#39; leverage to reach such awareness is to amalgamate a wide variety of sensor modalities through multisensor data fusion. A particularly promising direction to establishing human-like collaborations can be seen in the use of neuro-inspired sensing and computing technologies due to their resemblance with human cognitive processing. This note discusses the concept of integrating neuromorphic sensing modalities into classical sensor fusion frameworks by exploiting event-based fusion and filtering methods that combine time-periodic process models with event-triggered sensor data. Event-based sensor fusion hence adopts the operating principles of event-based sensors and even exhibits the ability to extract information from absent data. Thereby, it can be an enabler to harness the full information potential of the intrinsic spiking nature of event-driven sensors

State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties

State estimation techniques for centralized, distributed, and decentralized systems are studied. An easy-to-implement state estimation concept is introduced that generalizes and combines basic principles of Kalman filter theory and ellipsoidal calculus. By means of this method, stochastic and set-membership uncertainties can be taken into consideration simultaneously. Different solutions for implementing these estimation algorithms in distributed networked systems are presented

State Estimation with Sets of Densities considering Stochastic and Systematic Errors

In practical applications, state estimation requires the consideration of stochastic and systematic errors. If both error types are present, an exact probabilistic description of the state estimate is not possible, so that common Bayesian estimators have to be questioned. This paper introduces a theoretical concept, which allows for incorporating unknown but bounded errors into a Bayesian inference scheme by utilizing sets of densities. In order to derive a tractable estimator, the Kalman filter is applied to ellipsoidal sets of means, which are used to bound additive systematic errors. Also, an extension to nonlinear system and observation models with ellipsoidal error bounds is presented. The derived estimator is motivated by means of two example applications

Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation

Especially in the field of sensor networks and multi-robot systems, fully decentralized estimation techniques are of particular interest. As the required elimination of the complex dependencies between estimates generally yields inconsistent results, several approaches, e.g., covariance intersection, maintain consistency by providing conservative estimates. Unfortunately, these estimates are often too conservative and therefore, much less informative than a corresponding centralized approach. In this paper, we provide a concept that conservatively decorrelates the estimates while bounding the unknown correlations as closely as possible. For this purpose, known independent quantities, such as measurement noise, are explicitly identified and exploited. Based on tight covariance bounds, the new approach allows for an intuitive and systematic derivation of appropriate tailor-made filter equations and does not require heuristics. Its performance is demonstrated in a comparative study within a typical SLAM scenario