12,026 research outputs found
SIMDET - Version 4 A Parametric Monte Carlo for a TESLA Detector
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 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
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
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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