2,133 research outputs found
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
Diffusion Adaptation over Networks under Imperfect Information Exchange and Non-stationary Data
Adaptive networks rely on in-network and collaborative processing among
distributed agents to deliver enhanced performance in estimation and inference
tasks. Information is exchanged among the nodes, usually over noisy links. The
combination weights that are used by the nodes to fuse information from their
neighbors play a critical role in influencing the adaptation and tracking
abilities of the network. This paper first investigates the mean-square
performance of general adaptive diffusion algorithms in the presence of various
sources of imperfect information exchanges, quantization errors, and model
non-stationarities. Among other results, the analysis reveals that link noise
over the regression data modifies the dynamics of the network evolution in a
distinct way, and leads to biased estimates in steady-state. The analysis also
reveals how the network mean-square performance is dependent on the combination
weights. We use these observations to show how the combination weights can be
optimized and adapted. Simulation results illustrate the theoretical findings
and match well with theory.Comment: 36 pages, 7 figures, to appear in IEEE Transactions on Signal
Processing, June 201
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Uncertainty modelling in power system state estimation
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.As a special case of the static state estimation problem, the load-flow problem is studied
in this thesis. It is demonstrated that the non-linear load-flow formulation may be solved
by real-coded genetic algorithms. Due to its global optimisation ability, the proposed
method can be useful for off-line studies where multiple solutions are suspected.
This thesis presents two methods for estimating the uncertainty interval in power system
state estimation due to uncertainty in the measurements. The proposed formulations are
based on a parametric approach which takes in account the meter inaccuracies. A nonlinear
and a linear formulation are proposed to estimate the tightest possible upper and
lower bounds on the states. The uncertainty analysis, in power system state estimation, is
also extended to other physical quantities such as the network parameters. The
uncertainty is then assumed to be present in both measurements and network parameters.
To find the tightest possible upper and lower bounds of any state variable, the problem is
solved by a Sequential Quadratic Programming (SQP) technique.
A new robust estimator based on the concept of uncertainty in the measurements is
developed here. This estimator is known as Maximum Constraints Satisfaction (MCS).
Robustness and performance of the proposed estimator is analysed via simulation of
simple regression examples, D.C. and A.C. power system models.Embassy of Kuwai
Space-time processing for wireless mobile communications
Intersymbol interference (ISI) and co-channel interference (CCI) are two major
obstacles to high speed data transmission in wireless cellular communications
systems. Unlike thermal noise, their effects cannot be removed by
increasing the signal power and are time-varying due to the relative motion
between the transmitters and receivers. Space-time processing offers a signal
processing framework to optimally integrate the spatial and temporal properties
of the signal for maximal signal reception and at the same time, mitigate
the ISI and CCI impairments. In this thesis, we focus on the development of
this emerging technology to combat the undesirable effects of ISI and CCL
We first develop a convenient mathematical model to parameterize the
space-time multipath channel based on signal path power, directions and
times of arrival. Starting from the continuous time-domain, we derive compact
expressions of the vector space-time channel model that lead to the
notion of block space-time manifold, Under certain identifiability conditions,
the noiseless vector-channel outputs will lie on a subspace constructed from
a set. of basis belonging to the block space-time manifold. This is an important
observation as many high resolution array processing algorithms Can be
applied directly to estimate the multi path channel parameters.
Next we focus on the development of semi-blind channel identification
and equalization algorithms for fast time-varying multi path channels. Specifically.
we develop space-time processing algorithms for wireless TDMA networks that use short burst data formats with extremely short training data.
sequences. Due to the latter, the estimated channel parameters are extremely
unreliable for equalization with conventional adaptive methods. We approach
the channel acquisition, tracking and equalization problems jointly, and exploit
the richness of the inherent structural relationship between the channel
parameters and the data sequence by repeated use of available data through a forward- backward optimization procedure. This enables the fuller exploitation
of the available data. Our simulation studies show that significant performance
gains are achieved over conventional methods.
In the final part of this thesis, we address the problem identifying and
equalizing multi path communication channels in the presence of strong CCl.
By considering CCI as stochasic processes, we find that temporal diversity
can be gained by observing the channel outputs from a tapped delay line. Together with the assertion that the finite alphabet property of the information
sequences can offer additional information about the channel parameters and
the noise-plus-covariance matrix, we develop a spatial temporal algorithm,
iterative reweighting alternating minimization, to estimate the channel parameters
and information sequence in a weighted least squares framework.
The proposed algorithm is robust as it does not require knowledge of the
number of CCI nor their structural information. Simulation studies demonstrate
its efficacy over many reported methods
When Decision Meets Estimation: Theory and Applications
In many practical problems, both decision and estimation are involved. This dissertation intends to study the relationship between decision and estimation in these problems, so that more accurate inference methods can be developed. Hybrid estimation is an important formulation that deals with state estimation and model structure identification simultaneously. Multiple-model (MM) methods are the most widelyused tool for hybrid estimation. A novel approach to predict the Internet end-to-end delay using MM methods is proposed. Based on preliminary analysis of the collected end-to-end delay data, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Experimental results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness. Although hybrid estimation can identify model structure, it mainly focuses on the estimation part. When decision and estimation are of (nearly) equal importance, a joint solution is preferred. By noticing the resemblance, a new Bayes risk is generalized from those of decision and estimation, respectively. Based on this generalized Bayes risk, a novel, integrated solution to decision and estimation is introduced. Our study tries to give a more systematic view on the joint decision and estimation (JDE) problem, which we believe the work in various fields, such as target tracking, communications, time series modeling, will benefit greatly from. We apply this integrated Bayes solution to joint target tracking and classification, a very important topic in target inference, with simplified measurement models. The results of this new approach are compared with two conventional strategies. At last, a surveillance testbed is being built for such purposes as algorithm development and performance evaluation. We try to use the testbed to bridge the gap between theory and practice. In the dissertation, an overview as well as the architecture of the testbed is given and one case study is presented. The testbed is capable to serve the tasks with decision and/or estimation aspects, and is helpful for the development of the JDE algorithms
The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions
Bibliography: leaves. 63-66.Neural networks have been applied to a number of problems over the past few years. One of the emerging applications of neural networks is adaptive communication channel equalisation. This area of research has become prominent due to the reformulation of the equalisation problem as a classification problem. Viewing equalisation as a classification problem allows researchers to apply the knowledge gained from other fields to equalisation. A wide variety of neural network structures have been suggested to equalise communication channels. Each structure may in turn have a number of different possible algorithms to train the equaliser. A neural network is essentially a non-linear classifier; in general a neural network is able to classify data by employing a non-linear function. The primary subject of this dissertation is the comparative performance of neural networks employing non-localised basis (non-linear) functions (Multi-layer Perceptron) versus those employing localised basis functions (Radial Basis Function Network)
When Decision Meets Estimation: Theory and Applications
In many practical problems, both decision and estimation are involved. This dissertation intends to study the relationship between decision and estimation in these problems, so that more accurate inference methods can be developed. Hybrid estimation is an important formulation that deals with state estimation and model structure identification simultaneously. Multiple-model (MM) methods are the most widelyused tool for hybrid estimation. A novel approach to predict the Internet end-to-end delay using MM methods is proposed. Based on preliminary analysis of the collected end-to-end delay data, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Experimental results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness. Although hybrid estimation can identify model structure, it mainly focuses on the estimation part. When decision and estimation are of (nearly) equal importance, a joint solution is preferred. By noticing the resemblance, a new Bayes risk is generalized from those of decision and estimation, respectively. Based on this generalized Bayes risk, a novel, integrated solution to decision and estimation is introduced. Our study tries to give a more systematic view on the joint decision and estimation (JDE) problem, which we believe the work in various fields, such as target tracking, communications, time series modeling, will benefit greatly from. We apply this integrated Bayes solution to joint target tracking and classification, a very important topic in target inference, with simplified measurement models. The results of this new approach are compared with two conventional strategies. At last, a surveillance testbed is being built for such purposes as algorithm development and performance evaluation. We try to use the testbed to bridge the gap between theory and practice. In the dissertation, an overview as well as the architecture of the testbed is given and one case study is presented. The testbed is capable to serve the tasks with decision and/or estimation aspects, and is helpful for the development of the JDE algorithms
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