10,080 research outputs found
Orbit determination of space objects based on sparse optical data
While building up a catalog of Earth orbiting objects, if the available
optical observations are sparse, not deliberate follow ups of specific objects,
no orbit determination is possible without previous correlation of observations
obtained at different times. This correlation step is the most computationally
intensive, and becomes more and more difficult as the number of objects to be
discovered increases. In this paper we tested two different algorithms (and the
related prototype software) recently developed to solve the correlation problem
for objects in geostationary orbit (GEO), including the accurate orbit
determination by full least squares solutions with all six orbital elements.
Because of the presence in the GEO region of a significant subpopulation of
high area to mass objects, strongly affected by non-gravitational
perturbations, it was actually necessary to solve also for dynamical parameters
describing these effects, that is to fit between 6 and 8 free parameters for
each orbit. The validation was based upon a set of real data, acquired from the
ESA Space Debris Telescope (ESASDT) at the Teide observatory (Canary Islands).
We proved that it is possible to assemble a set of sparse observations into a
set of objects with orbits, starting from a sparse time distribution of
observations, which would be compatible with a survey capable of covering the
region of interest in the sky just once per night. This could result in a
significant reduction of the requirements for a future telescope network, with
respect to what would have been required with the previously known algorithm
for correlation and orbit determination.Comment: 20 pages, 8 figure
Multi-Bernoulli Sensor-Control via Minimization of Expected Estimation Errors
This paper presents a sensor-control method for choosing the best next state
of the sensor(s), that provide(s) accurate estimation results in a multi-target
tracking application. The proposed solution is formulated for a multi-Bernoulli
filter and works via minimization of a new estimation error-based cost
function. Simulation results demonstrate that the proposed method can
outperform the state-of-the-art methods in terms of computation time and
robustness to clutter while delivering similar accuracy
Sensor Control for Multi-Object Tracking Using Labeled Multi-Bernoulli Filter
The recently developed labeled multi-Bernoulli (LMB) filter uses better
approximations in its update step, compared to the unlabeled multi-Bernoulli
filters, and more importantly, it provides us with not only the estimates for
the number of targets and their states, but also with labels for existing
tracks. This paper presents a novel sensor-control method to be used for
optimal multi-target tracking within the LMB filter. The proposed method uses a
task-driven cost function in which both the state estimation errors and
cardinality estimation errors are taken into consideration. Simulation results
demonstrate that the proposed method can successfully guide a mobile sensor in
a challenging multi-target tracking scenario
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
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