6,366 research outputs found
Extension of Impulse Detectors to Spatial Dimension and their Utilization as Switch in the LMS L-SD Filter
In this paper, one kind of adaptive LMS filters based on order statistics is used for two-dimensional filtration of noisy greyscale images degraded by mixed noise. The signal-dependent adaptive LMS L-filter (L-SD) consists of two normalized constrained adaptive LMS L-filters, because they have better convergence properties than simple LMS algorithm. Moreover, first filter suppresses the noise in homogeneous regions and second filter preserves the high components of filtered image. Some versions of spatial order statistic detectors were developed from the impulse detectors and were employed as switch between output these filters
A New LLMS Algorithm for Antenna Array Beamforming
A new adaptive algorithm, called LLMS, which employs an array image factor, AI, sandwiched in between two Least Mean Square (LMS) sections, is proposed for different applications of array beamforming. The convergence of LLMS algorithm is analyzed, in terms of mean square error, in the presence of Additive White Gaussian Noise (AWGN) for two different modes of operation; namely with either an external reference or self-referencing. Unlike earlier LMS based schemes, which make use of step size adaptation to enhance their performance, the proposed algorithm derives its overall error signal by feeding back the error signal from the second LMS stage to combine with that of the first LMS section.This results in LLMS being less sensitive to variations in input signal-to-noise ratio as well as the step sizes used. Computer simulation results show that the proposed LLMS algorithm is superior in convergence performance over the conventional LMS algorithm as well some of the more recent LMS based algorithms, such as constrained-stability LMS (CSLMS), and Modified Robust Variable Step Size LMS (MRVSS) algorithms. Also, the operation of LLMS remains stable even when its reference signal is corrupted by AWGN. It is also shown that LLMS performs well in the presence of Rayleigh fading
Statistical mechanics approach to the phase unwrapping problem
The use of Mean-Field theory to unwrap principal phase patterns has been
recently proposed. In this paper we generalize the Mean-Field approach to
process phase patterns with arbitrary degree of undersampling. The phase
unwrapping problem is formulated as that of finding the ground state of a
locally constrained, finite size, spin-L Ising model under a non-uniform
magnetic field. The optimization problem is solved by the Mean-Field Annealing
technique. Synthetic experiments show the effectiveness of the proposed
algorithm
Distributed Recursive Least-Squares: Stability and Performance Analysis
The recursive least-squares (RLS) algorithm has well-documented merits for
reducing complexity and storage requirements, when it comes to online
estimation of stationary signals as well as for tracking slowly-varying
nonstationary processes. In this paper, a distributed recursive least-squares
(D-RLS) algorithm is developed for cooperative estimation using ad hoc wireless
sensor networks. Distributed iterations are obtained by minimizing a separable
reformulation of the exponentially-weighted least-squares cost, using the
alternating-minimization algorithm. Sensors carry out reduced-complexity tasks
locally, and exchange messages with one-hop neighbors to consent on the
network-wide estimates adaptively. A steady-state mean-square error (MSE)
performance analysis of D-RLS is conducted, by studying a stochastically-driven
`averaged' system that approximates the D-RLS dynamics asymptotically in time.
For sensor observations that are linearly related to the time-invariant
parameter vector sought, the simplifying independence setting assumptions
facilitate deriving accurate closed-form expressions for the MSE steady-state
values. The problems of mean- and MSE-sense stability of D-RLS are also
investigated, and easily-checkable sufficient conditions are derived under
which a steady-state is attained. Without resorting to diminishing step-sizes
which compromise the tracking ability of D-RLS, stability ensures that per
sensor estimates hover inside a ball of finite radius centered at the true
parameter vector, with high-probability, even when inter-sensor communication
links are noisy. Interestingly, computer simulations demonstrate that the
theoretical findings are accurate also in the pragmatic settings whereby
sensors acquire temporally-correlated data.Comment: 30 pages, 4 figures, submitted to IEEE Transactions on Signal
Processin
- …