11,120 research outputs found
A Novel Family of Adaptive Filtering Algorithms Based on The Logarithmic Cost
We introduce a novel family of adaptive filtering algorithms based on a
relative logarithmic cost. The new family intrinsically combines the higher and
lower order measures of the error into a single continuous update based on the
error amount. We introduce important members of this family of algorithms such
as the least mean logarithmic square (LMLS) and least logarithmic absolute
difference (LLAD) algorithms that improve the convergence performance of the
conventional algorithms. However, our approach and analysis are generic such
that they cover other well-known cost functions as described in the paper. The
LMLS algorithm achieves comparable convergence performance with the least mean
fourth (LMF) algorithm and extends the stability bound on the step size. The
LLAD and least mean square (LMS) algorithms demonstrate similar convergence
performance in impulse-free noise environments while the LLAD algorithm is
robust against impulsive interferences and outperforms the sign algorithm (SA).
We analyze the transient, steady state and tracking performance of the
introduced algorithms and demonstrate the match of the theoretical analyzes and
simulation results. We show the extended stability bound of the LMLS algorithm
and analyze the robustness of the LLAD algorithm against impulsive
interferences. Finally, we demonstrate the performance of our algorithms in
different scenarios through numerical examples.Comment: Submitted to IEEE Transactions on Signal Processin
Two Channel Estimation Methods for MIMO-OFDM System
Adaptive Filter is a part of the modern communication system. The applications of the adaptive filters are channel equalization, noise cancellation, system identification and adaptive beam forming. So the proper implementation of adaptive filter is a great deal. The intersymbol interference (ISI) caused by the multipath in band limited frequency selective time dispersion channel distort the transmitted signal. In this paper, we have concentrated on modifying the algorithm for the adaptive filter. The proposed VSS-LLMS and Modified Variable Step Size Leaky LMS (MVSS-LLMS) which improves the channel estimation in the noisy environment. Also we compared the results of our proposed algorithms with the LMS, RLS and VLLMS and observed that it improves in computational complexity and Bit Error Rate (BER) performance. Keywords: Adaptive Channel Estimation, Adaptive filter, LMS, RLS, VLLMS and MVSS-LLM
Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition
Dynamic mode decomposition (DMD) provides a practical means of extracting
insightful dynamical information from fluids datasets. Like any data processing
technique, DMD's usefulness is limited by its ability to extract real and
accurate dynamical features from noise-corrupted data. Here we show
analytically that DMD is biased to sensor noise, and quantify how this bias
depends on the size and noise level of the data. We present three modifications
to DMD that can be used to remove this bias: (i) a direct correction of the
identified bias using known noise properties, (ii) combining the results of
performing DMD forwards and backwards in time, and (iii) a total
least-squares-inspired algorithm. We discuss the relative merits of each
algorithm, and demonstrate the performance of these modifications on a range of
synthetic, numerical, and experimental datasets. We further compare our
modified DMD algorithms with other variants proposed in recent literature
Distributed detection, localization, and estimation in time-critical wireless sensor networks
In this thesis the problem of distributed detection, localization, and estimation
(DDLE) of a stationary target in a fusion center (FC) based wireless sensor network
(WSN) is considered. The communication process is subject to time-critical
operation, restricted power and bandwidth (BW) resources operating over a shared
communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm
is proposed to solve the DDLE problem consisting of two dependent stages:
distributed detection and distributed estimation. The WSN performs distributed
detection first and based on the global detection decision the distributed estimation
stage is performed. The communication between the SNs and the FC occurs over a
shared channel via a slotted Aloha MAC protocol to conserve BW.
In distributed detection, hard decision fusion is adopted, using the counting
rule (CR), and sensor censoring in order to save power and BW. The effect of
Rayleigh fading on distributed detection is also considered and accounted for by
using distributed diversity combining techniques where the diversity combining is
among the sensor nodes (SNs) in lieu of having the processing done at the FC.
Two distributed techniques are proposed: the distributed maximum ratio combining
(dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show
superior detection performance when compared to conventional diversity combining
procedures that take place at the FC.
In distributed estimation, the segmented distributed localization and estimation
(SDLE) framework is proposed. The SDLE enables efficient power and BW
processing. The SOLE hinges on the idea of introducing intermediate parameters
that are estimated locally by the SNs and transmitted to the FC instead of the
actual measurements. This concept decouples the main problem into a simpler set
of local estimation problems solved at the SNs and a global estimation problem
solved at the FC. Two algorithms are proposed for solving the local problem: a
nonlinear least squares (NLS) algorithm using the variable projection (VP) method
and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve
the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust
hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through
local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS),
NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm
combination delivers the best localization and estimation performance. In fact , the
target can be localized with less than one meter error.
The SNs send their local estimates to the FC over a shared channel using the
slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel.
However, Aloha is known for its relatively high medium access or contention delay
given the medium access probability is poorly chosen. This fact significantly
hinders the time-critical operation of the system. Hence, multi-packet reception
(MPR) is used with slotted Aloha protocol, in which several channels are used for
contention. The contention delay is analyzed for slotted Aloha with and without
MPR. More specifically, the mean and variance have been analytically computed
and the contention delay distribution is approximated. Having theoretical expressions
for the contention delay statistics enables optimizing both the medium access
probability and the number of MPR channels in order to strike a trade-off between
delay performance and complexity
Decorrelation of Neutral Vector Variables: Theory and Applications
In this paper, we propose novel strategies for neutral vector variable
decorrelation. Two fundamental invertible transformations, namely serial
nonlinear transformation and parallel nonlinear transformation, are proposed to
carry out the decorrelation. For a neutral vector variable, which is not
multivariate Gaussian distributed, the conventional principal component
analysis (PCA) cannot yield mutually independent scalar variables. With the two
proposed transformations, a highly negatively correlated neutral vector can be
transformed to a set of mutually independent scalar variables with the same
degrees of freedom. We also evaluate the decorrelation performances for the
vectors generated from a single Dirichlet distribution and a mixture of
Dirichlet distributions. The mutual independence is verified with the distance
correlation measurement. The advantages of the proposed decorrelation
strategies are intensively studied and demonstrated with synthesized data and
practical application evaluations
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