4,196 research outputs found
Multi-Model Kalman Filtering for Adaptive Nonuniformity: Correction in Infrared Sensors
This paper presents an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamical-model parameters, which underly the statistics of the scene and the nonuniformity as well as the temporal drift in the nonuniformity. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are obtained according to the a posteriori -likelihood principle, applied to the family of models by considering the output residual errors associated with each filter. These weights are updated iteratively between blocks of data, providing the estimator the means to follow the dynamics of the scenes and the sensor. The performance of the proposed estimator and its ability to compensate for fixed-pattern noise are tested using both real and simulated data. The real data is obtained using two cameras operating in the mid- and long-wave infrared regime
Kalman Filter Estimation for Focal Plane Wavefront Correction
Space-based coronagraphs for future earth-like planet detection will require
focal plane wavefront control techniques to achieve the necessary contrast
levels. These correction algorithms are iterative and the control methods
require an estimate of the electric field at the science camera, which requires
nearly all of the images taken for the correction. We demonstrate a Kalman
filter estimator that uses prior knowledge to create the estimate of the
electric field, dramatically reducing the number of exposures required to
estimate the image plane electric field. In addition to a significant reduction
in exposures, we discuss the relative merit of this algorithm to other
estimation schemes, particularly in regard to estimate error and covariance. As
part of the reduction in exposures we also discuss a novel approach to
generating the diversity required for estimating the field in the image plane.
This uses the stroke minimization control algorithm to choose the probe shapes
on the deformable mirrors, adding a degree of optimality to the problem and
once again reducing the total number of exposures required for correction.
Choosing probe shapes has been largely unexplored up to this point and is
critical to producing a well posed set of measurements for the estimate.
Ultimately the filter will lead to an adaptive algorithm which can estimate
physical parameters in the laboratory and optimize estimation.Comment: 14 pages, 9 figures, SPIE Astronomical Telescopes and Instrumentation
2012 conference proceedings. Journal version at arXiv:1301.382
Dynamic Iterative Pursuit
For compressive sensing of dynamic sparse signals, we develop an iterative
pursuit algorithm. A dynamic sparse signal process is characterized by varying
sparsity patterns over time/space. For such signals, the developed algorithm is
able to incorporate sequential predictions, thereby providing better
compressive sensing recovery performance, but not at the cost of high
complexity. Through experimental evaluations, we observe that the new algorithm
exhibits a graceful degradation at deteriorating signal conditions while
capable of yielding substantial performance gains as conditions improve.Comment: 6 pages, 7 figures. Accepted for publication in IEEE Transactions on
Signal Processin
Cross-coupled doa trackers
A new robust, low complexity algorithm for multiuser tracking is proposed, modifying the two-stage parallel architecture of the estimate-maximize (EM) algorithm. The algorithm copes with spatially colored noise, large differences in source powers, multipath, and crossing trajectories. Following a discussion on stability, the simulations demonstrate an asymptotic and tracking behavior that neither the EM nor a nonparallelized tracker can emulate.Peer ReviewedPostprint (published version
Kernel-based Inference of Functions over Graphs
The study of networks has witnessed an explosive growth over the past decades
with several ground-breaking methods introduced. A particularly interesting --
and prevalent in several fields of study -- problem is that of inferring a
function defined over the nodes of a network. This work presents a versatile
kernel-based framework for tackling this inference problem that naturally
subsumes and generalizes the reconstruction approaches put forth recently by
the signal processing on graphs community. Both the static and the dynamic
settings are considered along with effective modeling approaches for addressing
real-world problems. The herein analytical discussion is complemented by a set
of numerical examples, which showcase the effectiveness of the presented
techniques, as well as their merits related to state-of-the-art methods.Comment: To be published as a chapter in `Adaptive Learning Methods for
Nonlinear System Modeling', Elsevier Publishing, Eds. D. Comminiello and J.C.
Principe (2018). This chapter surveys recent work on kernel-based inference
of functions over graphs including arXiv:1612.03615 and arXiv:1605.07174 and
arXiv:1711.0930
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