This thesis presents methods for improving the detection processing of active sonar systems. Measures to compensate for or even exploit particular characteristics of the detection problem for these systems are considered. Reverberation is the result of scattering of the transmitted signal from non-target features. Multipath and variability are particularly pronounced for underwater sound signals because propagation is very sensitive to spatial and temporal temperature variations. Another problem is the low pulse repetition rate due to the relatively low speed of sound. This low data rate reduces tracking and detection performance. / Reverberation often arises as the sum of many small contributions so that received data has a multivariate Gaussian distribution. Estimating the large numbers of parameters in the distribution requires a lot of data. This data is not available because of the low data rate. Representing the scattering as an autoregressive process reduced the data requirement but at some cost to modelling accuracy. A coupled estimator algorithm is developed to estimate the parameters. Detection performance is compared to other models and estimators that assume Gaussian statistics. / To counter multipath distortion the delays and strength of the paths are estimated using a version of the expectation maximisation (EM) algorithm. The magnitude of path amplitudes is then used to decide if a target is present. The EM algorithm is also suggested as a way to find the likely amplitude of reverberation from a few large scatterers that that form non-Gaussian reverberation. / Non-parametric methods are considered for detection of short duration incoherent signals in a duct. These detectors compare the ranks of the data in a region being tested for target present to another region assumed to have no target. Simulations are used to explore performance and what happens when the independent samples assumption is violated by the presence of reverberation. / More data can improve detection. Exploiting data from multiple transmissions is difficult because the slow speed of sound allows targets to move out of detection cells between transmissions. Tracking the movements of potential targets can counter this problem. The usefulness of Integrated Probabalistic Data Association (IPDA), which calculates a probability of true track as well as track properties, is considered as a detection algorithm. Improvements when multiple receivers are used as well as limitations when sensor positions are uncertain are investigated.

Traditionally radio spectrum was considered a commodity to be allocated in a fixed and centralized manner, but now the technical community and the regulators approach it as a shared resource that can be flexibly and intelligently shared between competing entities. In this thesis we focus on novel strategies to sense and access the radio spectrum within the framework of Opportunistic Spectrum Access via Cognitive Radio Networks (CRNs). In the first part we develop novel transmit opportunity detection methods that effectively exploit the gray space present in packet based networks. Our methods proactively detect the maximum safe transmit power that does not significantly affect the primary network nodes via an implicit feedback mechanism from the Primary network to the Secondary network. A novel use of packet interarrival duration is developed to robustly perform change detection in the primary network's Quality of Service. The methods are validated on real world IEEE 802.11 WLANs. In the second part we study the inferential use of Goodness-of-Fit tests for spectrum sensing applications. We provide the first comprehensive framework for decision fusion of an ensemble of goodness-of-fit tests through use of p-values. Also, we introduce a generalized Phi-divergence statistic to formulate goodness-of-fit tests that are tunable via a single parameter. We show that under uncertainty in the noise statistics or non-Gaussianity in the noise, the performance of such non-parametric tests is significantly superior to that of conventional spectrum sensing methods. Additionally, we describe a collaborative spatially separated version of the test for robust combining of tests in a distributed spectrum sensing setting. In the third part we develop the sequential energy detection problem for spectrum sensing and formulate a novel Sequential Energy Detector. Through extensive simulations we demonstrate that our doubly hierarchical sequential testing architecture delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection and False Alarm. We also demonstrate the throughput gains for a case study of sensing ATSC television signals in IEEE 802.22 systems. / text