Collins, Leslie MBjornstad, Joel Nils2020-01-272020-03-122019https://hdl.handle.net/10161/19839<p>One issue associated with all aspects of the signal processing and decision making fields is that signals of interest are corrupted by noise. This work specifically considers scenarios where the primary noise source is external to an array of receivers and is diffuse. Spatially diffuse noise is considered in three scenarios: noisefield estimation, array calibration using diffuse noise as a source of opportunity, and detection of buried threats using Ground Penetrating Radar (GPR).</p><p>Modeling the ocean acoustic noise field is impractical as the noise seen by a receiver is dependent on the position of distant shipping (a major contributing source of low frequency noise) as well as the temperature, pressure, salinity and bathymetry of the ocean. Measuring the noise field using a standard towed array is also not practical due the inability of a line array to distinguish signals arriving at different elevations as well the presence of the well-known left/right ambiguity. A method to estimate the noisefield by fusing data from a traditional towed array and two small-aperture planar arrays is developed. The resulting noise field estimates can be used to produce synthetic covariance matrices that exhibit parity performance with measured covariance matrices when used in a Matched Subspace Detector.</p><p>For a phased array to function effectively, the positions of the array elements must be well calibrated. Previous efforts in the literature have primarily focused on use of discrete sources for calibration. The approach taken here focuses on using spatially oversampled, overlapping sub-arrays. The distance between elements is determine using The geometry of each individual sub-array is determined using Maximum Likelihood estimates of the interelement distances and determining the geometry of each sub array using Multidimensional Scaling. The overlapping sub-arrays are then combined into a single array. The algorithm developed in this work performs well in simulation. Limitations in the experimental setup preclude drawing firm conclusions based on an in-air test of the algorithm.</p><p>Ground penetrating radar (GPR) is one of the most successful methods to detect landmines and other buried threats. GPR images, however, are very noisy as the propagation path through soil is quite complex. It is a challenging problem to classify GPR images as threats or non-threats. Successful buried threat classification algorithm rely on a handcrafted feature descriptor paired with a machine learning classifier. In this work the state-of-the-art Spatial Edge Descriptor (SED) feature was implemented as a neural network. This implementation allows the feature and the classifier to be trained simultaneously and expanded with minimal intervention from a designer. Impediments to training this novel network were identified and a modified network proposed that surpasses the performance of the baseline SED algorithm.</p><p>These cases demonstrate the practicality of mitigating or using diffuse background noise to achieve desired engineering results.</p>Electrical engineeringArray CalibrationDiffuse NoiseLandmine detectionMachine learningNeural networksSignal processingDissertation