Environmentally adaptive noise estimation for active sonar

Noise is frequently encountered when processing data from the natural environment, and is of particular concern for remote-sensing applications where the accuracy of data gathered is limited by the noise present. Rather than merely accepting that sonar noise results in unavoidable error in active sonar systems, this research explores various methodologies to reduce the detrimental effect of noise. Our approach is to analyse the statistics of sonar noise in trial data, collected by a long-range active sonar system in a shallow water environment, and apply this knowledge to target detection. Our detectors are evaluated against imulated targets in simulated noise, simulated targets embedded in noise-only trial data, and trial data containing real targets. First, we demonstrate that the Weibull and K-distributions offer good models of sonar noise in a cluttered environment, and that the K-distribution achieves the greatest accuracy in the tail of the distribution. We demonstrate the limitations of the Kolmogorov-Smirnov goodness-of-fit test in the context of detection by thresholding, and investigate the upper-tail Anderson-Darling test for goodness-of-fit analysis. The upper-tail Anderson-Darling test is shown to be more suitable in the context of detection by thresholding, as it is sensitive to the far-right tail of the distribution, which is of particular interest for detection at low false alarm rates. We have also produced tables of critical values for K-distributed data evaluated by the upper-tail Anderson-Darling test. Having established suitable models for sonar noise, we develop a number of detection statistics. These are based on the box-car detector, and the generalized likelihood ratio test with a Rician target model. Our performance analysis shows that both types of detector benefit from the use of the noise model provided by the K-distribution. We also demonstrate that for weak signals, our GLRT detectors are able to achieve greater probability of detection than the box-car detectors. The GLRT detectors are also easily extended to use more than one sample in a single test, an approach that we show to increase probability of detection when processing simulated targets. A fundamental difficulty in estimating model parameters is the small sample size. Many of the pings in our trial data overlap, covering the same region of the sea. It is therefore possible to make use of samples from multiple pings of a region, increasing the sample size. For static targets, the GLRT detector is easily extended to multi-ping processing, but this is not as easy for moving targets. We derive a new method of combining noise estimates over multiple pings. This calculation can be applied to either static or moving targets, and is also shown to be useful for generating clutter maps. We then perform a brief performance analysis on trial data containing real targets, where we show that in order to perform well, the GLRT detector requires a more accurate model of the target than the Rician distribution is able to provide. Despite this, we show that both GLRT and box-car detectors, when using the K-distribution as a noise model, can achieve a small improvement in the probability of detection by combining estimates of the noise parameters over multiple pings.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Maximum entropy based analysis of a DS/SSMA diversity system

D.Ing.This thesis sets out to propose and analyze a cellular Direct Sequence Spread Spectrum Multiple Access (DSjSSMA) system for the Indoor Wireless Communication (IWC) Nakagami fading channel. The up- and downlink of the system implement Differential Phase Shift Keying (DPSK) and Coherent Phase Shift Keying (CPSK) as modulation schemes respectively, and are analyzed using Maximum Entropy (MaxEnt) principles due to its reliability and accuracy. As a means to enhance system capacity and performance, different forms of diversity are investigated; for the up- and downlink, respectively, RAKE reception and Maximum Ratio Combining (MRC) diversity together with Forward Error Control (FEC) coding are assumed. Further, the validity of the Gaussian Assumption (GA) is quantified and investigated under fading and non-fading conditions by calculating the missing information, using Minimum Relative Entropy (MRE) principles between the Inter- User Interference (IUI) distribution and a Gaussian distribution of equal variance

Ultra-Wideband Wireless Channels - Estimation, Modeling and Material Characterization

This licentiate thesis is focused on the characterization of ultra-wideband wireless channels. The thesis presents results on ultra-wideband communications as well as on the ultra-wideband characterization of materials. The communications related work consisted in the measurement and modeling of outdoor scenarios envisioned for infostation systems. By infostation, we mean a communication system covering a small area, i.e., ranging up to 20 m, where mobile users can pass by or stop while receiving large amounts of data in a short period of time. Considering the expected (but perhaps overly optimistic) 480 Mbps for UWB systems, it should be possible to download a complete DVD in roughly two minutes, which is something not realizable with any of the current wireless technologies. Channel models, commonly based on measurements, can be used to evaluate the performance of such systems. We therefore, we started by performing measurements at one of the scenarios where infostation systems can exist in the future, namely, petrol stations. The idealized model, was one that could correctly describe the continuous evolution of the channel impulse response for a moving user within the system’s range, and therefore it was deemed necessary to track the multipath components defining the impulse responses along a path of several meters. To solve this problem we designed a novel high-resolution scatterer detection method, which is described in Paper I, capable of tracking individual multipath components for a moving user by identifying the originating point scatterers in a two dimensional geometrical space. The same paper also gives insight on some properties of clusters of scatterers, such as their direction-selective radiated power. The scatterer detection method described in Paper I provided us with the required tools to create the channel model described in Paper II. The proposed channel model has a geometrical basis, i.e., each realization of the channel is based on a virtual map containing point scatterers that contribute to the impulse response by multipath components. Some of the particular characteristics of the model include non-stationary effects, such as shadowing and cluster’s visibility regions. At the end of Paper II, in a simple validation step, the output of the channel model showed a good match with the measured impulse responses. The second part of our work, documented in Paper III, consisted on the dielectric characterization of soil samples using microwave measurements. This project was made in cooperation with the Department of Physical Geography and Ecosystem Analysis at Lund University, which had been developing research work on methane emissions from the wetlands in Zackenberg, Greenland. In recent years, a lot of attention has been put into the understanding of the methane emissions from soils, since methane is a greenhouse gas 20 times stronger than carbon dioxide. However, whereas the methane emissions from natural soils are well documented, the reason behind this effect is an open issue. The usage of microwave measurements to monitor soil samples, aims to address this problem by capturing the sub-surface changes in the soil during gas emissions. An experiment consisting on the monitoring of a soil sample was performed, and a good correlation was found between the variations of the microwave signals and the methane emissions. In addition, the soil dielectric constant was calculated, and from that, the volumetric fractions of the soil constituents which provided useful data for the elaboration of models to describe the gas emission triggering mechanisms. Based on this laboratory experiment, a complete soil monitoring system was created and is at the time of writing running at Zackenberg, Greenland