1,720,975 research outputs found
Distributed parameter and state estimation for wireless sensor networks
Full text linkThe research in distributed algorithms is linked with the developments of statistical inference
in wireless sensor networks (WSNs) applications. Typically, distributed approaches process
the collected signals from networked sensor nodes. That is to say, the sensors receive local
observations and transmit information between each other. Each sensor is capable of combining
the collected information with its own observations to improve performance. In this thesis, we
propose novel distributed methods for the inference applications using wireless sensor networks.
In particular, the efficient algorithms which are not computationally intensive are investigated.
Moreover, we present a number of novel algorithms for processing asynchronous network events
and robust state estimation.
In the first part of the thesis, a distributed adaptive algorithm based on the component-wise
EM method for decentralized sensor networks is investigated. The distributed component-wise
Expectation-Maximization (EM) algorithm has been designed for application in a Gaussian
density estimation. The proposed algorithm operates a component-wise EM procedure for local
parameter estimation and exploit an incremental strategy for network updating, which can provide
an improved convergence rate. Numerical simulation results have illustrated the advantages of
the proposed distributed component-wise EM algorithm for both well-separated and overlapped
mixture densities. The distributed component-wise EM algorithm can outperform other EM-based
distributed algorithms in estimating overlapping Gaussian mixtures.
In the second part of the thesis, a diffusion based EM gradient algorithm for density estimation
in asynchronous wireless sensor networks has been proposed. Specifically, based on the
asynchronous adapt-then-combine diffusion strategy, a distributed EM gradient algorithm that
can deal with asynchronous network events has been considered. The Bernoulli model has been
exploited to approximate the asynchronous behaviour of the network. Compared with existing
distributed EM based estimation methods using a consensus strategy, the proposed algorithm
can provide more accurate estimates in the presence of asynchronous networks uncertainties,
such as random link failures, random data arrival times, and turning on or off sensor nodes
for energy conservation. Simulation experiments have been demonstrated that the proposed
algorithm significantly outperforms the consensus based strategies in terms of Mean-Square-
Deviation (MSD) performance in an asynchronous network setting.
Finally, the challenge of distributed state estimation in power systems which requires low
complexity and high stability in the presence of bad data for a large scale network is addressed.
A gossip based quasi-Newton algorithm has been proposed for solving the power system state
estimation problem. In particular, we have applied the quasi-Newton method for distributed
state estimation under the gossip protocol. The proposed algorithm exploits the Broyden-
Fletcher-Goldfarb-Shanno (BFGS) formula to approximate the Hessian matrix, thus avoiding the
computation of inverse Hessian matrices for each control area. The simulation results for IEEE
14 bus system and a large scale 4200 bus system have shown that the distributed quasi-Newton
scheme outperforms existing algorithms in terms of Mean-Square-Error (MSE) performance with
bad data
Target localization using RSS measurements in wireless sensor networks
Full text linkThe subject of this thesis is the development of localization algorithms for target localization in
wireless sensor networks using received signal strength (RSS) measurements or Quantized RSS
(QRSS) measurements.
In chapter 3 of the thesis, target localization using RSS measurements is investigated. Many
existing works on RSS localization assumes that the shadowing components are uncorrelated.
However, here, shadowing is assumed to be spatially correlated. It can be shown that
localization accuracy can be improved with the consideration of correlation between pairs of RSS
measurements. By linearizing the corresponding Maximum Likelihood (ML) objective function,
a weighted least squares (WLS) algorithm is formulated to obtain the target location. An iterative
technique based on Newtons method is utilized to give a solution. Numerical simulations show
that the proposed algorithms achieves better performance than existing algorithms with reasonable
complexity.
In chapter 4, target localization with an unknown path loss model parameter is investigated. Most
published work estimates location and these parameters jointly using iterative methods with a good
initialization of path loss exponent (PLE). To avoid finding an initialization, a global optimization
algorithm, particle swarm optimization (PSO) is employed to optimize the ML objective function.
By combining PSO with a consensus algorithm, the centralized estimation problem is extended to
a distributed version so that can be implemented in distributed WSN. Although suboptimal, the
distributed approach is very suitable for implementation in real sensor networks, as it is scalable,
robust against changing of network topology and requires only local communication. Numerical
simulations show that the accuracy of centralized PSO can attain the Cramer Rao Lower Bound
(CRLB). Also, as expected, there is some degradation in performance of the distributed PSO with
respect to the centralized PSO.
In chapter 5, a distributed gradient algorithm for RSS based target localization using only
quantized data is proposed. The ML of the Quantized RSS is derived and PSO is used to provide an
initial estimate for the gradient algorithm. A practical quantization threshold designer is presented
for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate
at each node is also quantized. The RSS measurements and the local estimate at each sensor
node are quantized in different ways. By using a quantization elimination scheme, a quantized
distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the
local estimate is gradually eliminated with each iteration. Simulations show that the performance
of the centralized algorithm can reach the CRLB. The proposed distributed algorithm using a
small number of bits can achieve the performance of the distributed gradient algorithm using
unquantized data
Exploiting sparsity for persistent scatterer detection to aid X-band airborne SAR tomography
Full text linkThis thesis evaluates the potential for using line of sight returns and return signals
from underneath a forest canopy using X-band, airborne synthetic aperture radar
(SAR) tomography. Approximately 30% of the Earth’s land surface is covered
by vegetation, therefore global digital elevation models (DEMs) contain a signal
from the forest canopy and not the ground. By uncovering new techniques to find
the ground signals, using data collected from airborne platforms as verification,
such procedures could be applied to currently operational and future X-band,
spaceborne systems with the aim of resolving much of the vegetation bias on an
international scale.
Data from three sources is presented; data collected from Selex ES’s SAR
systems, the GOTCHA dataset and simulated data. Before carrying out
tomography it is shown that SAR interferometry (InSAR) can successfully be
applied to X-band, helicopter data. A scatterer defined as a candidate persistent
scatterer (CPS) is introduced, where the pixels are stable and coherent over a
matter of days. An algorithm for selecting CPSs is developed by exploiting
sparsity and a novel choice of hard thresholding operator. Using simulated
forestry and SAR information the effects of changing input parameters on the
outcome of the tomographic profile is analysed.
What is found in this study is that model simulations demonstrate that ground
points can be detected if the platform motion is relatively stable and that
temporal decorrelation over the forest volume is kept to a minimal. An
understory can confuse the tomographic profile since less line of sight
observations can be made. By combining line of sight observations alongside
new tomography techniques on high resolution SAR data this thesis shows it is
possible to detect ground scatterers, even at X-band
IRCI-free MIMO SAR
Full text linkThis thesis presents new configurations that (i) utilise the available bandwidth to the maximum efficiency in multiple subband multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) and (ii) employ all of the phase centres in orthogonal waveform encoding MIMO SAR. These configurations enable us to image a wider swath with a higher cross-range resolution compared to the conventional orthogonal waveform encoding MIMO SAR. Two different multiple subband MIMO SAR configurations are proposed. The first one makes use of multiple contiguous narrow receiving beams with different phase centres which permits the use of a pulse repetition frequency (PRF) lower than the total Doppler bandwidth. Echoes corresponding to different transmitted subband waveforms are processed jointly without separating them at the receiver using a bank of bandpass filers (BPFs) to utilise the bandwidth to the maximum efficiency (i.e. there is no need to add guard bands between the adjacent subband spectra). Digital beamforming (DBF) on receive in elevation is proposed to mitigate the effect of interbeams overlapping on the azimuth ambiguity characteristics. The second proposed multiple subband MIMO SAR configuration has an advantage over the first in that the beamwidths of all transmitters and receivers are the same. The beams simultaneously illuminate the same imaging area which overcome the receiving interbeams overlapping problem without employing DBF
on receive in elevation. This reduces the implementation complexity. The proposed orthogonal waveform encoding MIMO SAR configuration employs multiple contiguous azimuth beams. It uses all of the phase centres including the spatially overlapping ones to reduce the minimum operating PRF that should be satisfied to avoid aliasing in the azimuth dimension. The received signals in all proposed configurations are processed as the solution to system identification
problems using the principle of displaced phase centres (DPC). This, in turn, facilitates the use of linear frequency modulated (LFM) waveforms for transmission and, hence, gains all the inherent benefits of these waveforms. The impulse response in the range dimension is identified using a proposed frequency domain system identification (FDSI) estimation algorithm instead of a matched filter. The length of the transmitted waveform is not a function of the channel impulse response length in the range dimension which makes the proposed algorithm suitable for a stripmap SAR application. The estimated range profile obtained using the FDSI-based algorithm has ideally a zero sidelobes level which is the property interrange cell interference (IRCI) free
Frequency-based radar waveform design for target classification performance optimisation using Fisher analysis
Full text linkThis thesis presents non-adaptive radar waveform and receiver designs to improve radar target
identification performance. The designs are based on the theory of Fisher discriminants analysis and Fisher separability functions. Introducing Fisher discriminants analysis in waveform
design for target maximisation is the first contribution of this thesis. By using the concepts of
Fisher analysis both for 2-class or multiclass scenarios, a separability rational function can be
derived for practical extended targets classification. The separability functions are formulated
to maximise the distance between the means of data classes while minimising their variance.
Fisher separability is used as an objective function for the optimisation problem to find the
optimal waveform that maximises it under constant energy constraints. The classifiers are derived and inspired by Fisher minimum distance classifiers. The second contribution of the thesis is deriving low-energy low-covariance (LELC) closed-form solutions for the optimisation
problem under additive white Gaussian noise (AWGN) conditions. These solutions perform
well especially when the signal-to-noise ratio is low. Further, a closed-form solution for the
optimisation problem is derived for the 2-class scenario. The solution achieves classification
performance comparable to solutions obtained using general optimisation solvers. The proposed waveform and receiver design methods are tested using synthetic and real target data
and is shown to achieve better performance than the wideband chirp and other non-adaptive
waveform design methods reported in the literature
Compressed sensing with approximate message passing: measurement matrix and algorithm design
Full text linkCompressed sensing (CS) is an emerging technique that exploits the properties of a sparse or
compressible signal to efficiently and faithfully capture it with a sampling rate far below the
Nyquist rate. The primary goal of compressed sensing is to achieve the best signal recovery
with the least number of samples. To this end, two research directions have been receiving
increasing attention: customizing the measurement matrix to the signal of interest and optimizing
the reconstruction algorithm. In this thesis, contributions in both directions are made
in the Bayesian setting for compressed sensing. The work presented in this thesis focuses on
the approximate message passing (AMP) schemes, a new class of recovery algorithm that takes
advantage of the statistical properties of the CS problem.
First of all, a complete sample distortion (SD) framework is presented to fundamentally quantify
the reconstruction performance for a certain pair of measurement matrix and recovery
scheme. In the SD setting, the non-optimality region of the homogeneous Gaussian matrix
is identified and the novel zeroing matrix is proposed with an improved performance. With the
SD framework, the optimal sample allocation strategy for the block diagonal measurement matrix
are derived for the wavelet representation of natural images. Extensive simulations validate
the optimality of the proposed measurement matrix design.
Motivated by the zeroing matrix, we extend the seeded matrix design in the CS literature to
the novel modulated matrix structure. The major advantage of the modulated matrix over the
seeded matrix lies in the simplicity of its state evolution dynamics. Together with the AMP
based algorithm, the modulated matrix possesses a 1-D performance prediction system, with
which we can optimize the matrix configuration. We then focus on a special modulated matrix
form, designated as the two block matrix, which can also be seen as a generalization of the
zeroing matrix. The effectiveness of the two block matrix is demonstrated through both sparse
and compressible signals. The underlining reason for the improved performance is presented
through the analysis of the state evolution dynamics.
The final contribution of the thesis explores improving the reconstruction algorithm. By taking
the signal prior into account, the Bayesian optimal AMP (BAMP) algorithm is demonstrated
to dramatically improve the reconstruction quality. The key insight for its success is that it
utilizes the minimum mean square error (MMSE) estimator for the CS denoising. However, the
prerequisite of the prior information makes it often impractical. A novel SURE-AMP algorithm
is proposed to address the dilemma. The critical feature of SURE-AMP is that the Stein’s
unbiased risk estimate (SURE) based parametric least square estimator is used to replace the
MMSE estimator. Given the optimization of the SURE estimator only involves the noisy data,
it eliminates the need for the signal prior, thus can accommodate more general sparse models
Software for "Approximate Adaptive Beam-Pattern Design"
No full textMatlab code supporting the publication: Herbert, Hopgood and Mulgrew, "Computationally simple MMSE (A-optimal)adaptive beam-pattern design for MIMO activesensing systems via a linear-Gaussian approximation", accepted in IEEE Transactions on Signal Processing.Herbert, Steven; Hopgood, James R.; Mulgrew, Bernie. (2018). Approximate Adaptive Beam-Pattern Design, [software]. University of Edinburgh. School of Engineering. Institute of Digital Communications. https://doi.org/10.7488/ds/2403
Robust spectrum sensing techniques for cognitive radio networks
Full text linkCognitive radio is a promising technology that improves the spectral utilisation by allowing
unlicensed secondary users to access underutilised frequency bands in an opportunistic manner.
This task can be carried out through spectrum sensing: the secondary user monitors the
presence of primary users over the radio spectrum periodically to avoid harmful interference to
the licensed service.
Traditional energy based sensing methods assume the value of noise power as prior knowledge.
They suffer from the noise uncertainty problem as even a mild noise level mismatch will lead
to significant performance loss. Hence, developing an efficient robust detection method is
important. In this thesis, a novel sensing technique using the F-test is proposed. By assuming
a multiple antenna assisted receiver, this detector uses the F-statistic as the test statistic which
offers absolute robustness against the noise variance uncertainty. In addition, since the channel
state information (CSI) is required to be known, the impact of CSI uncertainty is also discussed.
Results show the F-test based sensing method performs better than the energy detector and has
a constant false alarm probability, independent of the accuracy of the CSI estimate.
Another main topic of this thesis is to address the sensing problem for non-Gaussian noise.
Most of the current sensing techniques consider Gaussian noise as implied by the central limit
theorem (CLT) and it offers mathematical tractability. However, it sometimes fails to model the
noise in practical wireless communication systems, which often shows a non-Gaussian heavy-tailed
behaviour.
In this thesis, several sensing algorithms are proposed for non-Gaussian noise. Firstly, a non-parametric
eigenvalue based detector is developed by exploiting the eigenstructure of the sample
covariance matrix. This detector is blind as no information about the noise, signal and
channel is required. In addition, the conventional energy detector and the aforementioned F-test
based detector are generalised to non-Gaussian noise, which require the noise power and
CSI to be known, respectively. A major concern of these detection methods is to control the
false alarm probability. Although the test statistics are easy to evaluate, the corresponding null
distributions are difficult to obtain as they depend on the noise type which may be unknown and
non-Gaussian. In this thesis, we apply the powerful bootstrap technique to overcome this difficulty.
The key idea is to reuse the data through resampling instead of repeating the experiment
a large number of times. By using the nonparametric bootstrap approach to estimate the null
distribution of the test statistic, the assumptions on the data model are minimised and no large
sample assumption is invoked. In addition, for the F-statistic based method, we also propose
a degrees-of-freedom modification approach for null distribution approximation. This method
assumes a known noise kurtosis and yields closed form solutions. Simulation results show that
in non-Gaussian noise, all the three detectors maintain the desired false alarm probability by
using the proposed algorithms. The F-statistic based detector performs the best, e.g., to obtain
a 90% detection probability in Laplacian noise, it provides a 2.5 dB and 4 dB signal-to-noise
ratio (SNR) gain compared with the eigenvalue based detector and the energy based detector,
respectively
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
