Antenna array geometries and algorithms for direction of arrival estimation

Direction of arrival (DOA) estimation with the antenna array was a forever topic of scientist. In this dissertation, a detailed comparison of the direction of arrival (DOA) estimation algorithms, including three classic algorithms as MUSIC, Root-MUSIC and ESPRIT, was performed and an analysis of various array geometries’ (configurations) properties in DOA estimation was demonstrated. Cramer-Rao Bound (CRB) was used for theoretic analysis and Root Mean Square Error (RMSE), which determined the best performance for a given geometry, regardless the specific estimation algorithm used, was implemented in simulation comparison. In the first part, MUSIC, Root-MUSIC and ESPRIT were illustrated, where theoretic underlying of the algorithms were expressed by revisited, paseudo code algorithms, and compared in the aspects of accuracy and computational efficiency. Consequently, ESPRIT was found more efficient than the other two algorithms in computation. However, the accuracy of MUSIC was better than ESPRIT. In the second part, four particular array geometries, including Uniform Circular Array (UCA), L Shaped Array (LSA), Double L Shaped Array (DLSA) and Double Uniform Circular Array (DUCA), were analyzed in the area of directivity, accuracy and resolving ability. A simulation comparison of DOA estimation with these four array geometries by MUSIC algorithm in two dimensions was made then, since MUSIC had the best accuracy in these three algorithms. According to the analysis and comparison, it was found that L Shaped Array (LSA) and Double L Shaped Array (DLSA) were more accurate than others, considering both azimuth and elevation estimation. Also, in the case of two dimensional DOA estimation, the Double L Shaped Array (DLSA) was shown a theoretically relative isotropy to other array geometries. From the simulation, the detection ability of Double L Shaped Array (DLSA) was proved the best in the array geometries discussed in this dissertation. These findings had significant implications for the further study of the array geometry in DOA estimation

Beamforming and Direction of Arrival Estimation Based on Vector Sensor Arrays

Array signal processing is a technique linked closely to radar and sonar systems. In communication, the antenna array in these systems is applied to cancel the interference, suppress the background noise and track the target sources based on signals'parameters. Most of existing work ignores the polarisation status of the impinging signals and is mainly focused on their direction parameters. To have a better performance in array processing, polarized signals can be considered in array signal processing and their property can be exploited by employing various electromagnetic vector sensor arrays. In this thesis, firstly, a full quaternion-valued model for polarized array processing is proposed based on the Capon beamformer. This new beamformer uses crossed-dipole array and considers the desired signal as quaternion-valued. Two scenarios are dealt with, where the beamformer works at a normal environment without data model errors or with model errors under the worst-case constraint. After that, an algorithm to solve the joint DOA and polarisation estimation problem is proposed. The algorithm applies the rank reduction method to use two 2-D searches instead of a 4-D search to estimate the joint parameters. Moreover, an analysis is given to introduce the difference using crossed-dipole sensor array and tripole sensor array, which indicates that linear crossed-dipole sensor array has an ambiguity problem in the estimation work and the linear tripole sensor array avoid this problem effectively. At last, we study the problem of DOA estimation for a mixture of single signal transmission (SST) signals and duel signal transmission (DST) signals. Two solutions are proposed: the first is a two-step method to estimate the parameters of SST and DST signals separately; the second one is a unified one-step method to estimate SST and DST signals together, without treating them separately in the estimation process

Antenna array geometries and algorithms for direction of arrival estimation

Direction of arrival (DOA) estimation with the antenna array was a forever topic of scientist. In this dissertation, a detailed comparison of the direction of arrival (DOA) estimation algorithms, including three classic algorithms as MUSIC, Root-MUSIC and ESPRIT, was performed and an analysis of various array geometries’ (configurations) properties in DOA estimation was demonstrated. Cramer-Rao Bound (CRB) was used for theoretic analysis and Root Mean Square Error (RMSE), which determined the best performance for a given geometry, regardless the specific estimation algorithm used, was implemented in simulation comparison. In the first part, MUSIC, Root-MUSIC and ESPRIT were illustrated, where theoretic underlying of the algorithms were expressed by revisited, paseudo code algorithms, and compared in the aspects of accuracy and computational efficiency. Consequently, ESPRIT was found more efficient than the other two algorithms in computation. However, the accuracy of MUSIC was better than ESPRIT. In the second part, four particular array geometries, including Uniform Circular Array (UCA), L Shaped Array (LSA), Double L Shaped Array (DLSA) and Double Uniform Circular Array (DUCA), were analyzed in the area of directivity, accuracy and resolving ability. A simulation comparison of DOA estimation with these four array geometries by MUSIC algorithm in two dimensions was made then, since MUSIC had the best accuracy in these three algorithms. According to the analysis and comparison, it was found that L Shaped Array (LSA) and Double L Shaped Array (DLSA) were more accurate than others, considering both azimuth and elevation estimation. Also, in the case of two dimensional DOA estimation, the Double L Shaped Array (DLSA) was shown a theoretically relative isotropy to other array geometries. From the simulation, the detection ability of Double L Shaped Array (DLSA) was proved the best in the array geometries discussed in this dissertation. These findings had significant implications for the further study of the array geometry in DOA estimation

Signal eigen-analysis and L1 inversion of seismic data

This thesis covers seismic signal analysis and inversion. It can be divided into two parts. The first part includes principal component analysis (PCA) and singular spectrum analysis (SSA). The objectives of these two eigen-analyses are extracting weak signals and designing optimal spatial sampling interval. The other part is on least squares inverse problems with a L1 norm constraint. The study covers seismic reflectivity inversion in which L1 regularization provides us a sparse solution of reflectivity series, and seismic reverse time migration in which L1 regularization generates high-resolution images. PCA is a well-known eigenvector-based multivariate analysis technique which decomposes a data set into principal components, in order to maximize the information content in the recorded data with fewer dimensions. PCA can be described from two viewpoints, one of which is derived by maximizing the variance of the principal components, and the other draws a connection between the representation of data variance and the representation of data themself by using Singular Value Decomposition (SVD). Each approach has a unique motivation, and thus comparison of these two approaches provides further understanding of the PCA theory. While dominant components contain primary energy of the original seismic data, remaining may be used to reconstruct weak signals, which reflect the geometrical properties of fractures, pores and fluid properties in the reservoirs. When PCA is conducted on time-domain data, Singular Spectrum Analysis (SSA) technology is applied to frequency-domain data, to analyse signal characters related to spatial sampling. For a given frequency, this technique transforms the spatial acquisition data into a Hankel matrix. Ideally, the rank of this matrix is the total number of plane waves within the selected spatial window. However, the existence of noise and absence of seismic traces may increase the rank of Hankel matrix. Thus deflation could be an effective way for noise attenuation and trace exploration. In this thesis, SSA is conducted on seismic data, to find an optimal spatial sampling interval. Seismic reflectivity inversion is a deconvolution process which compresses the seismic wavelet and retrieves the reflectivity series from seismic records. It is a key technique for further inversion, as seismic reflectivity series are required to retrieve impedance and other elastic parameters. Sparseness is an important feature of the reflectivity series. Under the sparseness assumption, the location of a reflectivity indicates the position of an impedance contrast interface, and the amplitude indicates the reflection energy. When using L1 regulation as sparseness constraint, inverse problem becomes nonlinear. Therefore, it is presented as a Basis Pursuit Denosing (BPDN) or Least Absolute Shrinkage and Selection Operator (LASSO) optimal problem and solved by spectral projected gradient (SPG) algorithm. Migration is a key technique to image Earth’s subsurface structures by moving dipping reflections to their true subsurface locations and collapsing diffractions. Reverse time migration (RTM) is a depth migration method which constructs wavefields along the time axis. RTM extrapolates wavefields using a two-way wave equation in the time-space domain, and uses the adjoint operator, instead of the inverse operator, to migrate the record. To improve the signal-to-noise ratio and the resolution of RTM images, RTM may be implemented as a least-squares inverse problem with L1 norm constraint. In this way, the advantages of RTM itself, least-squares RTM, and L1 regularization are utilized to obtain a high-resolution, two-way wave equation-based depth migration image.Open Acces

Coherent Change Detection Under a Forest Canopy

Coherent change detection (CCD) is an established technique for remotely monitoring landscapes with minimal vegetation or buildings. By evaluating the local complex correlation between a pair of synthetic aperture radar (SAR) images acquired on repeat passes of an airborne or spaceborne imaging radar system, a map of the scene coherence is obtained. Subtle disturbances of the ground are detected as areas of low coherence in the surface clutter. This thesis investigates extending CCD to monitor the ground in a forest. It is formulated as a multichannel dual-layer coherence estimation problem, where the coherence of scattering from the ground is estimated after suppressing interference from the canopy by vertically beamforming multiple image channels acquired at slightly different grazing angles on each pass. This 3D SAR beamforming must preserve the phase of the ground response. The choice of operating wavelength is considered in terms of the trade-off between foliage penetration and change sensitivity. A framework for comparing the performance of different radar designs and beamforming algorithms, as well as assessing the sensitivity to error, is built around the random-volume-over-ground (RVOG) model of forest scattering. If the ground and volume scattering contributions in the received echo are of similar strength, it is shown that an L-band array of just three channels can provide enough volume attenuation to permit reasonable estimation of the ground coherence. The proposed method is demonstrated using an RVOG clutter simulation and a modified version of the physics-based SAR image simulator PolSARproSim. Receiver operating characteristics show that whilst ordinary single-channel CCD is unusable when a canopy is present, 3D SAR CCD permits reasonable detection performance. A novel polarimetric filtering algorithm is also proposed to remove contributions from the ground-trunk double-bounce scattering mechanism, which may mask changes on the ground near trees. To enable this kind of polarimetric processing, fully polarimetric data must be acquired and calibrated. Motivated by an interim version of the Ingara airborne imaging radar, which used a pair of helical antennas to acquire circularly polarised data, techniques for the estimation of polarimetric distortion in the circular basis are investigated. It is shown that the standard approach to estimating cross-talk in the linear basis, whereby expressions for the distortion of reflection-symmetric clutter are linearised and solved, cannot be adapted to the circular basis, because the first-order effects of individual cross-talk parameters cannot be distinguished. An alternative approach is proposed that uses ordinary and gridded trihedral corner reflectors, and optionally dihedrals, to iteratively estimate the channel imbalance and cross-talk parameters. Monte Carlo simulations show that the method reliably converges to the true parameter values. Ingara data is calibrated using the method, with broadly consistent parameter estimates obtained across flights. Genuine scene changes may be masked by coherence loss that arises when the bands of spatial frequencies supported by the two passes do not match. Trimming the spatial-frequency bands to their common area of support would remove these uncorrelated contributions, but the bands, and therefore the required trim, depend on the effective collection geometry at each pixel position. The precise dependence on local slope and collection geometry is derived in this thesis. Standard methods of SAR image formation use a flat focal plane and allow only a single global trim, which leads to spatially varying coherence loss when the terrain is undulating. An image-formation algorithm is detailed that exploits the flexibility offered by back-projection not only to focus the image onto a surface matched to the scene topography but also to allow spatially adaptive trimming. Improved coherence is demonstrated in simulation and using data from two airborne radar systems.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 202

Nonlinear Dimensionality Reduction Methods in Climate Data Analysis

Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three such techniques to the study of El Nino/Southern Oscillation variability in tropical Pacific sea surface temperatures and thermocline depth, comparing observational data with simulations from coupled atmosphere-ocean general circulation models from the CMIP3 multi-model ensemble. The three methods used here are a nonlinear principal component analysis (NLPCA) approach based on neural networks, the Isomap isometric mapping algorithm, and Hessian locally linear embedding. I use these three methods to examine El Nino variability in the different data sets and assess the suitability of these nonlinear dimensionality reduction approaches for climate data analysis. I conclude that although, for the application presented here, analysis using NLPCA, Isomap and Hessian locally linear embedding does not provide additional information beyond that already provided by principal component analysis, these methods are effective tools for exploratory data analysis.Comment: 273 pages, 76 figures; University of Bristol Ph.D. thesis; version with high-resolution figures available from http://www.skybluetrades.net/thesis/ian-ross-thesis.pdf (52Mb download

A room acoustics measurement system using non-invasive microphone arrays

This thesis summarises research into adaptive room correction for small rooms and pre-recorded material, for example music of films. A measurement system to predict the sound at a remote location within a room, without a microphone at that location was investigated. This would allow the sound within a room to be adaptively manipulated to ensure that all listeners received optimum sound, therefore increasing their enjoyment. The solution presented used small microphone arrays, mounted on the room's walls. A unique geometry and processing system was designed, incorporating three processing stages, temporal, spatial and spectral. The temporal processing identifies individual reflection arrival times from the recorded data. Spatial processing estimates the angles of arrival of the reflections so that the three-dimensional coordinates of the reflections' origin can be calculated. The spectral processing then estimates the frequency response of the reflection. These estimates allow a mathematical model of the room to be calculated, based on the acoustic measurements made in the actual room. The model can then be used to predict the sound at different locations within the room. A simulated model of a room was produced to allow fast development of algorithms. Measurements in real rooms were then conducted and analysed to verify the theoretical models developed and to aid further development of the system. Results from these measurements and simulations, for each processing stage are presented

Blind identification of acoustic systems and enhancement of reverberant speech

Imperial Users onl