Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Low-power Distance Bounding

Abstract: A distance bounding system guarantees an upper bound on the physical distance between a verifier and a prover. However, in contrast to a conventional wireless communication system, distance bounding systems introduce tight requirements on the processing delay at the prover and require high distance measurement precision making their practical realization challenging. Prior proposals of distance bounding systems focused primarily on building provers with minimal processing delays but did not consider the power limitations of provers and verifiers. However, in a wide range of applications (e.g., physical access control), provers are expected to be fully or semi-passive introducing additional constraints on the design and implementation of distance bounding systems. In this work, we propose a new physical layer scheme for distance bounding and leverage this scheme to implement a distance bounding system with a low-power prover. Our physical layer combines frequency modulated continuous wave (FMCW) and backscatter communication. The use of backscatter communication enables low power consumption at the prover which is critical for a number of distance bounding applications. By using the FMCW-based physical layer, we further decouple the physical distance estimation from the processing delay at the prover, thereby enabling the realization of the majority of distance bounding protocols developed in prior art. We evaluate our system under various attack scenarios and show that it offers strong security guarantees against distance, mafia and terrorist frauds. Additionally, we validate the communication and distance measurement characteristics of our system through simulations and experiments and show that it is well suited for short-range physical access control and payment applications.

Finite-time state bounding of homogeneous nonlinear positive systems with disturbance

Abstract: This paper deals with the finite-time state bounding problem of homogeneous nonlinear positive systems with disturbance. Based on a technique used in positive systems, explicit conditions are established such that all solutions of homogeneous nonlinear positive systems with degree 0

Limited Complexity and Parallel Implementation of Polytope Updating

Abstract: The methods introduced in this paper aim at helping the practical applications of polytope based parameter and state bounding, and for this purpose parallel computing and limited complexity procedures are introduced to update convex polytopes. As opposed to the case of covariance based recursive statistical estimation where parallel computation can only result moderate improvement for lower dimensions of parametervectors, polytope updating can be parallelized more efficiently. The increase in the speed of the procedure can be made proportional to the number of applied processors. This result means that using sufficent number of processors polytope based parameter bounding can be made even faster than statistical estimation. Another development of this paper is the introduction of some schemes to limit the complexity of the updated polytopes. Combining limited complexity calculations with parallel processing helps the way towards on-line applications of polytope-based parameter and state bounding in adaptive control and signal processing.

Analyzing Team Actions with Cascading HMM.

Abstract: While team action recognition has a relatively extended literature, less attention has been given to the detailed realtime analysis of the internal structure of the team actions. This includes recognizing the current state of the action, predicting the next state, recognizing deviations from the standard action model, and handling ambiguous cases. The underlying probabilistic reasoning model has a major impact on the type of data it can extract, its accuracy, and the computational cost of the reasoning process. In this paper we are using Cascading Hidden Markov Models (CHMM) to analyze Bounding Overwatch, an important team action in military tactics. The team action is represented in the CHMM as a plan tree. Starting from real-world recorded data, we identify the subteams through clustering and extract team oriented discrete features. In an experimental study, we investigate whether the better scalability and the more structured information provided by the CHMM comes with an unacceptable cost in accuracy. We find the a properly parametrized CHMM estimating the current goal chain of the Bounding Overwatch plan tree comes very close to a flat HMM estimating only the overall Bounding Overwatch state (a subset of the goal chain) at a respective overall state accuracy of 95% vs 98%, making the CHMM a good candidate for deployed systems.