2,299 research outputs found
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Spatial Compressive Sensing for MIMO Radar
We study compressive sensing in the spatial domain to achieve target
localization, specifically direction of arrival (DOA), using multiple-input
multiple-output (MIMO) radar. A sparse localization framework is proposed for a
MIMO array in which transmit and receive elements are placed at random. This
allows for a dramatic reduction in the number of elements needed, while still
attaining performance comparable to that of a filled (Nyquist) array. By
leveraging properties of structured random matrices, we develop a bound on the
coherence of the resulting measurement matrix, and obtain conditions under
which the measurement matrix satisfies the so-called isotropy property. The
coherence and isotropy concepts are used to establish uniform and non-uniform
recovery guarantees within the proposed spatial compressive sensing framework.
In particular, we show that non-uniform recovery is guaranteed if the product
of the number of transmit and receive elements, MN (which is also the number of
degrees of freedom), scales with K(log(G))^2, where K is the number of targets
and G is proportional to the array aperture and determines the angle
resolution. In contrast with a filled virtual MIMO array where the product MN
scales linearly with G, the logarithmic dependence on G in the proposed
framework supports the high-resolution provided by the virtual array aperture
while using a small number of MIMO radar elements. In the numerical results we
show that, in the proposed framework, compressive sensing recovery algorithms
are capable of better performance than classical methods, such as beamforming
and MUSIC.Comment: To appear in IEEE Transactions on Signal Processin
Radar networks: A review of features and challenges
Networks of multiple radars are typically used for improving the coverage and
tracking accuracy. Recently, such networks have facilitated deployment of
commercial radars for civilian applications such as healthcare, gesture
recognition, home security, and autonomous automobiles. They exploit advanced
signal processing techniques together with efficient data fusion methods in
order to yield high performance of event detection and tracking. This paper
reviews outstanding features of radar networks, their challenges, and their
state-of-the-art solutions from the perspective of signal processing. Each
discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved
in the last decade in the context of target tracking, with regard to either a single target or multiple
targets in the presence of false or missing data. The first part of our review is on remarkable
achievements that have been made for the single-target PF from several aspects including importance
proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal
systems. The second part of our review is on analyzing the intractable challenges raised within
the general multitarget (multi-sensor) tracking due to random target birth and termination, false
alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream
multitarget PF approaches consist of two main classes, one based on M2T association approaches and
the other not such as the finite set statistics-based PF. In either case, significant challenges remain due
to unknown tracking scenarios and integrated tracking management
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