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For target location and tracking in wireless communication systems, mobile target positioning and tracking play an important role. Since multi-sensor system can be used as an efficient solution to target positioning process, more accurate target location estimation and tracking results can be obtained. However, both the deployment of designed multi-sensor and location algorithm may affect the overall performance of position location. In this thesis, based on the time difference of arrival (TDOA), two closed-form least-square location methods, spherical-interpolation (SI) method and spherical-intersection (SX) method are used to estimate the target location. The two location methods are different from the usual process of iterative and nonlinear minimization. The locations of the target and the designed multiple sensors may yield geometric effects on location performance. The constraints and performance of the two location methods will first be introduced. To achieve real-time target tracking, the Kalman filtering structures are used to combine the SI and SX methods. Because these two positioning and tracking systems have different and complementary performance inside and outside the multi-sensor array, we consider using data fusion to improve location estimation results by using interacting multiple model (IMM) based estimator, in which internal filters running in parallel are designed as the SX-KF1 and the SI-KF2. However, due to the time-varying characteristics of measurement noises, we propose an adjusting scheme for measurement noise variance assignment in the Kalman filters to obtain improved location estimation results. Simulation results are obtained by running Matlab program. In three-dimensional multi-sensor array scenarios, the results of moving target location estimation shows that the IMM-based estimators effectively improve the position performance.

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Ng Lai Sze. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 135-140). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Spherical Data Representation --- p.4 / Chapter 3 --- Rhombic Dodecahedron --- p.7 / Chapter 3.1 --- Introduction --- p.7 / Chapter 3.2 --- "Platonic Solids, Archimedean Solids and Dual Polyhedron" --- p.8 / Chapter 3.2.1 --- Platonic Solids --- p.8 / Chapter 3.2.2 --- Archimedean Solids --- p.10 / Chapter 3.2.3 --- Dual Polyhedron --- p.13 / Chapter 3.3 --- Rhombic Dodecahedron --- p.16 / Chapter 3.3.1 --- Basic Property of Rhombic Dodecahedron --- p.16 / Chapter 3.3.2 --- Construction of Rhombic Dodecahedron --- p.16 / Chapter 3.3.3 --- Advantages of Rhombic Dodecahedron --- p.16 / Chapter 3.4 --- Summary --- p.19 / Chapter 4 --- Subdivision Scheme --- p.21 / Chapter 4.1 --- Introduction --- p.21 / Chapter 4.2 --- Motivation --- p.22 / Chapter 4.3 --- Great Circle Subdivision --- p.22 / Chapter 4.3.1 --- Normal Space Analysis --- p.23 / Chapter 4.4 --- Small Circle Subdivision --- p.25 / Chapter 4.5 --- Skew Great Circle Subdivision --- p.27 / Chapter 4.6 --- Analysis --- p.28 / Chapter 4.6.1 --- Sampling Uniformity --- p.29 / Chapter 4.6.2 --- Area Uniformity --- p.32 / Chapter 4.6.3 --- Stretch Measurement --- p.35 / Chapter 4.6.4 --- Query Efficiency --- p.39 / Chapter 4.7 --- Summary --- p.40 / Chapter 5 --- Data Querying and Indexing --- p.42 / Chapter 5.1 --- Introduction --- p.42 / Chapter 5.2 --- Location of base polygon --- p.43 / Chapter 5.2.1 --- General Method --- p.43 / Chapter 5.2.2 --- Tailored Table Look Up Method --- p.45 / Chapter 5.3 --- Location of the subdivided area --- p.49 / Chapter 5.3.1 --- On Deriving the Indexing Equation --- p.50 / Chapter 5.4 --- Summary --- p.54 / Chapter 6 --- Environment Mapping --- p.56 / Chapter 6.1 --- Introduction --- p.56 / Chapter 6.2 --- Related Work --- p.57 / Chapter 6.3 --- Methodology --- p.58 / Chapter 6.4 --- Data Preparation --- p.59 / Chapter 6.4.1 --- Re-sampling of Data on Sphere --- p.60 / Chapter 6.4.2 --- Preparation of Texture --- p.65 / Chapter 6.5 --- Reflection and Refraction by environment mapping --- p.68 / Chapter 6.5.1 --- Location and Retrieval of Data --- p.68 / Chapter 6.5.2 --- Cg Implementation --- p.70 / Chapter 6.6 --- Experiments --- p.76 / Chapter 6.6.1 --- Experiment Setup --- p.76 / Chapter 6.6.2 --- Experiment Result and Analysis --- p.78 / Chapter 6.7 --- Summary --- p.89 / Chapter 7 --- Shadow Mapping --- p.92 / Chapter 7.1 --- Introduction --- p.92 / Chapter 7.2 --- Related Work --- p.93 / Chapter 7.3 --- Methodology --- p.95 / Chapter 7.4 --- Data Preparation --- p.97 / Chapter 7.5 --- Shadow Determination and Scene Illumination --- p.98 / Chapter 7.6 --- Experiments --- p.100 / Chapter 7.6.1 --- Experiment Setup --- p.100 / Chapter 7.6.2 --- Experiment Result and Analysis --- p.101 / Chapter 7.7 --- Summary --- p.107 / Chapter 8 --- Dynamic HDR Environment Sequences Sampling --- p.110 / Chapter 8.1 --- Introduction --- p.110 / Chapter 8.2 --- Related Work on HDR Distant Environment Map Sampling --- p.112 / Chapter 8.3 --- Static Sampling by Spherical Quad-Quad Tree --- p.114 / Chapter 8.3.1 --- Importance Metric --- p.117 / Chapter 8.4 --- Dynamic Sampling by Spherical Quad-Quad Tree --- p.121 / Chapter 8.5 --- Experiments --- p.125 / Chapter 8.5.1 --- Static Sampling --- p.125 / Chapter 8.5.2 --- Dynamic Sampling --- p.126 / Chapter 8.6 --- Summary --- p.132 / Chapter 9 --- Conclusion --- p.133 / Bibliography --- p.135

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Spherical Harmonics (SPHARM) and Singular Value Decomposition (SVD) utilize the orthogonal relations of its parameters to represent and process images. The process involve mapping of the image from its original parameter domain to a new domain where the processing is performed. This process induces distortion and smoothing is required. The image now mapped to the new parameter domain is descripted using SPHARM and SVD using one at a time. The least significant values for the SPHARM coefficients and singular values of SVD are truncated which induces compression in the reconstructed image keeping the memory allocation in view. In this thesis, we have applied SPHARM and SVD tools to represent and reconstruct an image. The image is first mapped to the unit sphere (a sphere with unit radius). The image gets distorted that is maximum at the north and south poles, for which smoothing is approached by leaving 0.15*π space blank at each pole where no mapping is done. Sampling is performed for the θ and φ parameters and the image is represented using spherical harmonics and its coefficients are calculated. The same is then repeated for the SVD and singular values are computed. Reconstruction is performed using the calculated parameters, but defined over some finite domain, which is done by truncating the SPHARM coefficients and the singular values inducing image compression. Results are formulated for the various truncation choices and analyzed and finally it is concluded that SPHARM is better as compared with SVD as compression tool as there is not much difference in the quality of the reconstructed image with both tools, though SVD seem better quality wise, but with much higher memory allocation than SPHARM.

The ARG (Acoustic Research Group) at the University of Canterbury has a goal to image objects buried under the seafloor. In order to image these objects, accurate models and a good understanding of the acoustic parameters of the seafloor are needed. The relevant acoustic parameters to model the seafloor include: physical classification, speed of sound, acoustic impedance, density, and roughness. Acquiring accurate acoustic parameters for the seafloor is a challenging task that requires different approaches depending on the environment, especially in locations with low visibility. This thesis looks at a non-invasive method of acoustically examining the roughness spectrum and acoustic impedance of the seafloor. To achieve these goals, spherical transducers with a continuous bandwidth between 30 kHz and 130 kHz were chosen to examine the frequency dependent specular and diffused components of the reflection from rough seafloor surfaces. Using spherical transducers allows the surface scattering to be examined independently of the transducer beam-pattern. This thesis examines the range and validity of the tools required to make these measurements and presents empirical results measured in the controlled environment of a cylindrical 3.5m diameter by 2m deep sonar test tank. Using results from the sonar test tank, this thesis demonstrates that the acoustic impedance, speed of sound, and density of the material of the seafloor can be measured from a single seafloor realisation measured at multiple angles, provided the seafloor material has a critical angle. Additionally, this thesis demonstrates that the spectrum of the specular and diffused components of the rough-surface reflection requires averaging multiple seafloor realisations to be statistically relevant. As well as describing the benefits of using spherical transducers, this thesis presents some of the problems and potential solutions for future applications.

Thesis (doctoral)--University of Helsinki, 2007. / Includes bibliographical references. Also available on the World Wide Web.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 35-36). Also available in electronic version.