22,915 research outputs found
Tracking Angles of Departure and Arrival in a Mobile Millimeter Wave Channel
Millimeter wave provides a very promising approach for meeting the
ever-growing traffic demand in next generation wireless networks. To utilize
this band, it is crucial to obtain the channel state information in order to
perform beamforming and combining to compensate for severe path loss. In
contrast to lower frequencies, a typical millimeter wave channel consists of a
few dominant paths. Thus it is generally sufficient to estimate the path gains,
angles of departure (AoDs), and angles of arrival (AoAs) of those paths.
Proposed in this paper is a dual timescale model to characterize abrupt channel
changes (e.g., blockage) and slow variations of AoDs and AoAs. This work
focuses on tracking the slow variations and detecting abrupt changes. A Kalman
filter based tracking algorithm and an abrupt change detection method are
proposed. The tracking algorithm is compared with the adaptive algorithm due to
Alkhateeb, Ayach, Leus and Heath (2014) in the case with single radio frequency
chain. Simulation results show that to achieve the same tracking performance,
the proposed algorithm requires much lower signal-to-noise-ratio (SNR) and much
fewer pilots than the other algorithm. Moreover, the change detection method
can always detect abrupt changes with moderate number of pilots and SNR.Comment: 6 pages, 7 figures, submitted to ICC 201
Active Classification for POMDPs: a Kalman-like State Estimator
The problem of state tracking with active observation control is considered
for a system modeled by a discrete-time, finite-state Markov chain observed
through conditionally Gaussian measurement vectors. The measurement model
statistics are shaped by the underlying state and an exogenous control input,
which influence the observations' quality. Exploiting an innovations approach,
an approximate minimum mean-squared error (MMSE) filter is derived to estimate
the Markov chain system state. To optimize the control strategy, the associated
mean-squared error is used as an optimization criterion in a partially
observable Markov decision process formulation. A stochastic dynamic
programming algorithm is proposed to solve for the optimal solution. To enhance
the quality of system state estimates, approximate MMSE smoothing estimators
are also derived. Finally, the performance of the proposed framework is
illustrated on the problem of physical activity detection in wireless body
sensing networks. The power of the proposed framework lies within its ability
to accommodate a broad spectrum of active classification applications including
sensor management for object classification and tracking, estimation of sparse
signals and radar scheduling.Comment: 38 pages, 6 figure
Kalman-filter control schemes for fringe tracking. Development and application to VLTI/GRAVITY
The implementation of fringe tracking for optical interferometers is
inevitable when optimal exploitation of the instrumental capacities is desired.
Fringe tracking allows continuous fringe observation, considerably increasing
the sensitivity of the interferometric system. In addition to the correction of
atmospheric path-length differences, a decent control algorithm should correct
for disturbances introduced by instrumental vibrations, and deal with other
errors propagating in the optical trains. We attempt to construct control
schemes based on Kalman filters. Kalman filtering is an optimal data processing
algorithm for tracking and correcting a system on which observations are
performed. As a direct application, control schemes are designed for GRAVITY, a
future four-telescope near-infrared beam combiner for the Very Large Telescope
Interferometer (VLTI). We base our study on recent work in adaptive-optics
control. The technique is to describe perturbations of fringe phases in terms
of an a priori model. The model allows us to optimize the tracking of fringes,
in that it is adapted to the prevailing perturbations. Since the model is of a
parametric nature, a parameter identification needs to be included. Different
possibilities exist to generalize to the four-telescope fringe tracking that is
useful for GRAVITY. On the basis of a two-telescope Kalman-filtering control
algorithm, a set of two properly working control algorithms for four-telescope
fringe tracking is constructed. The control schemes are designed to take into
account flux problems and low-signal baselines. First simulations of the
fringe-tracking process indicate that the defined schemes meet the requirements
for GRAVITY and allow us to distinguish in performance. In a future paper, we
will compare the performances of classical fringe tracking to our Kalman-filter
control.Comment: 17 pages, 8 figures, accepted for publication in A&
Detection of abrupt changes in dynamic systems
Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed
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