12,026 research outputs found

    SIMDET - Version 4 A Parametric Monte Carlo for a TESLA Detector

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    A new release of the parametric detector Monte Carlo program \verb+SIMDET+ (version 4.01) is now available. We describe the principles of operation and the usage of this program to simulate the response of a detector for the TESLA linear collider. The detector components are implemented according to the TESLA Technical Design Report. All detector component responses are treated in a realistic way using a parametrisation of results from the {\em ab initio} Monte Carlo program \verb+BRAHMS+. Pattern recognition is emulated using a complete cross reference between generated particles and detector response. Also, for charged particles, the covariance matrix and dE/dxdE/dx information are made available. An idealised energy flow algorithm defines the output of the program, consisting of particles generically classified as electrons, photons, muons, charged and neutral hadrons as well as unresolved clusters. The program parameters adjustable by the user are described in detail. User hooks inside the program and the output data structure are documented.Comment: 30 pages, 7 figure

    A New Approach To Estimate The Collision Probability For Automotive Applications

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    We revisit the computation of probability of collision in the context of automotive collision avoidance (the estimation of a potential collision is also referred to as conflict detection in other contexts). After reviewing existing approaches to the definition and computation of a collision probability we argue that the question "What is the probability of collision within the next three seconds?" can be answered on the basis of a collision probability rate. Using results on level crossings for vector stochastic processes we derive a general expression for the upper bound of the distribution of the collision probability rate. This expression is valid for arbitrary prediction models including process noise. We demonstrate in several examples that distributions obtained by large-scale Monte-Carlo simulations obey this bound and in many cases approximately saturate the bound. We derive an approximation for the distribution of the collision probability rate that can be computed on an embedded platform. In order to efficiently sample this probability rate distribution for determination of its characteristic shape an adaptive method to obtain the sampling points is proposed. An upper bound of the probability of collision is then obtained by one-dimensional numerical integration over the time period of interest. A straightforward application of this method applies to the collision of an extended object with a second point-like object. Using an abstraction of the second object by salient points of its boundary we propose an application of this method to two extended objects with arbitrary orientation. Finally, the distribution of the collision probability rate is identified as the distribution of the time-to-collision.Comment: Revised and restructured version, discussion of extended vehicles expanded, section on TTC expanded, references added, other minor changes, 17 pages, 18 figure

    Riemann-Langevin Particle Filtering in Track-Before-Detect

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    Track-before-detect (TBD) is a powerful approach that consists in providing the tracker with sensor measurements directly without pre-detection. Due to the measurement model non-linearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to exploit all available knowledge of the posterior (that is, both prior and measurement information). This letter synthesizes recent advances in LMC-based filtering to describe the Riemann-Langevin particle filter and introduces its novel application to TBD. The benefits of our approach are illustrated in a challenging low-noise scenario.Comment: Minor grammatical update
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