International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / Parameterized data from ARTM channel sounding test flights are used to derive a parameter-based estimate of the scattering function. The Doppler bandwidth and Doppler spread, important measures of channel dynamics, can be estimated from such a scattering function. Data collected from ARTM Flight 11 suggest that the Doppler bandwidth is larger than 6.7 Hz. Even for very small collections of parameterized data, surprising agreement is shown to non-parametric scattering function estimates. This confirms modeling assumptions and offers a way to achieve significant reductions of storage requirements.

This thesis describes the use of scattering functions to characterize time-varying multipath radio channels. Channel Impulse responses were measured at Edwards Air Force Base (EAFB) and scattering functions generated from the impulse response data. From the scattering functions we compute the corresponding Doppler power spectrum and multipath intensity profile. These functions completely characterize the signal delay and the time varying nature of the channel in question and are used by systems engineers to design reliable communications links. We observe from our results that flight paths with ample reflectors exhibit significant multipath events. We also examine the bit error rate (BER) performance of a reduced-complexity equalizer for a truncated version of the pulse amplitude modulation (PAM) representation of SOQPSK-TG in a multipath channel. Since this reduced-complexity equalizer is based on the maximum likelihood (ML) principle, we expect it to perform optimally than any of the filter-based equalizers used in estimating received SOQPSK-TG symbols. As such we present a comparison between this ML detector and a minimum mean square error (MMSE) equalizer for the same example channel. The example channel used was motivated by the statistical channel characterizations described in thisthesis. Our analysis shows that the ML equalizer outperforms the MMSE equalizer in estimating received SOQPSK-TG symbols.

Remote sensing is the collection of information about an object from a distance without physically being in contact with it. The type of remote sensing of interest here is in the form of digital images of water bodies acquired by satellite. The advantage over traditional sampling techniques is that data can be gathered quickly over large ranges, and be available for immediate analysis. Remote sensing is a powerful technique for the monitoring of water bodies. To interpret the remotely sensed data, however, knowledge of the optical properties of the water constituents is needed. One of the most important of these is the volume scattering function, which describes the angular distribution of light scattered by a sample. This thesis presents the first measurements of volume scattering functions for Australian waters. Measurements were made on around 40 different samples taken from several locations in the Gippsland lakes and the Great Barrier Reef. The measurements were made by modifying an existing static light scattering spectrometer in order to accurately measure the volume scattering functions. The development of the apparatus, its calibration and automation, and the application of a complex series of post-acquisition data corrections, are all discussed. In order to extrapolate the data over the full angular range, the data was analysed using theoretical curves calculated for multi-modal size distributions using Mie light scattering theory applied to each data set. From the Mie fits the scattering and backscattering coefficients were calculated. These were compared with scattering coefficients measured using in situ sensors ac-9 and Hydroscat-6, and with values from the literature. The effect of chlorophyll a concentrations on the scattering coefficients was examined, and a brief investigation of the polarisation properties of the samples was also undertaken. Finally the angular effects on the relationship between the backscattering coefficient and the volume scattering function were investigated. This is important as in situ backscattering sensors often assume that measuring at a single fixed-angle is a good approximation for calculating the backscattering coefficient. This assumption is tested, and the optimal measurement angle determined.

Helium-3 surface spin echo spectroscopy (HeSE) has been used to measure the diffusive dynamics of adsorbates on close-packed metal surfaces, namely hydrogen on Cu(111), Pd(111) and Ru(0001), carbon and oxygen on Ru(0001), and oxygen on Cu(111). Chapter 2 reviews the HeSE technique and describes the relevant dynamical models and statistical methods used to interpret data in later chapters. The performance of the ionizing detector is analysed, with a focus on the signal-to-noise ratio. In Chapter 3 expressions for the classical intermediate scattering function (ISF) are introduced for open and closed systems. The effects of corrugation and surface-perpendicular motion on the amplitude of different components in the ISF are modelled analytically and compared with simulation. The exact ISF for a particle on a flat surface, obeying the Generalized Langevin Equation with exponential memory friction, is calculated analytically. In Chapter 4 the analytical ISF is calculated for quantum Brownian motion and for coherent tunneling dynamics in a tight binding system. The bounce method for calculating quantum mechanical hopping rates in dissipative systems is applied to model diffusion of hydrogen on Ru(0001). Chapter 5 presents the first HeSE measurements of carbon and oxygen diffusion. C/Ru(0001) diffusion is assigned to a small carbon cluster. The jump rate has an activation energy $E_{A}=292\pm7\,$meV in the temperature range $550\,\textrm{K}\leq T \leq 1300\,$K. Oxygen diffusion is significantly slower. By comparison of literature data with the new HeSE results, the activation energy for oxygen diffusion at low coverage is estimated as $650\pm10$meV. Oxygen measurements at high coverage $\theta\approx0.22\,$ML are consistent with strong mutual O-O interactions. Surface diffusion is also observed after exposing Cu(111) to oxygen. Chapter 6 presents low-coverage measurements of protium (H) and deuterium (D) diffusion on Ru(0001), Pd(111) and Cu(111). In the quantum activated regime there is evidence for multiple jumps in all three systems, suggesting a low dynamical friction. The measurements on Ru(0001) indicate that the deep tunneling rate is much slower for D than for H.

Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, it has investigated the problems of (i) the Instantaneous Frequency (IF) estimation of Linear Frequency-Modulated (LFM) signals corrupted in complex-valued zero-mean Multiplicative Noise (MN), and (ii) the Underdetermined Blind Source Separation (UBSS) of LFM signals, while focusing onto the fast-growing area of Wireless Communications (WCom). A common problem in the issue of signal estimation is the estimation of the frequency of Frequency-Modulated signals which are seen in many engineering and real-life applications. Accurate frequency estimation leads to accurate recovery of the true information. In some applications, the random amplitude modulation shows up when the medium is dispersive and/or when the assumption of point target is not valid; the original signal is considered to be corrupted by an MN process thus seriously aecting the recovery of the information-bearing frequency. The IF estimation of nonstationary signals corrupted by complex-valued zero-mean MN was investigated in this research. We have proposed a Second-Order Statistics approach, rather than a Higher-Order Statistics approach, for IF estimation using Time-Frequency Distributions (TFDs). The main assumption was that the autocorrelation function of the MN is real-valued but not necessarily positive (i.e. the spectrum of the MN is symmetric but does not necessary has the highest peak at zero frequency). The estimation performance was analyzed in terms of bias and variance, and compared between four dierent TFDs: Wigner-Ville Distribution, Spectrogram, Choi-Williams Distribution and Modified B Distribution. To further improve the estimation, we proposed to use the Multiple Signal Classification algorithm and showed its better performance. It was shown that the Modified B Distribution performance was the best for Signal-to-Noise Ratio less than 10dB. In the issue of signal separation, a new research direction called Blind Source Separation (BSS) has emerged over the last decade. BSS is a fundamental technique in array signal processing aiming at recovering unobserved signals or sources from observed mixtures exploiting only the assumption of mutual independence between the signals. The term "blind" indicates that neither the structure of the mixtures nor the source signals are known to the receivers. Applications of BSS are seen in, for example, radar and sonar, communications, speech processing, biomedical signal processing. In the case of nonstationary signals, a TF structure forcing approach was introduced by Belouchrani and Amin by defining the Spatial Time- Frequency Distribution (STFD), which combines both TF diversity and spatial diversity. The benefit of STFD in an environment of nonstationary signals is the direct exploitation of the information brought by the nonstationarity of the signals. A drawback of most BSS algorithms is that they fail to separate sources in situations where there are more sources than sensors, referred to as UBSS. The UBSS of nonstationary signals was investigated in this research. We have presented a new approach for blind separation of nonstationary sources using their TFDs. The separation algorithm is based on a vector clustering procedure that estimates the source TFDs by grouping together the TF points corresponding to "closely spaced" spatial directions. Simulations illustrate the performances of the proposed method for the underdetermined blind separation of FM signals. The method developed in this research represents a new research direction for solving the UBSS problem. The successful results obtained in the research development of the above two problems has led to a conclusion that TFSP is useful for WCom. Future research directions were also proposed.