Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.

Measuring energy spectra of TeV gamma-ray emission from the Cygnus region of our galaxy with Milagro

High energy gamma rays can provide fundamental clues to the origins of cosmic rays. In this thesis, TeV gamma-ray emission from the Cygnus region is studied. Previously the Milagro experiment detected five TeV gamma-ray sources in this region and a significant excess of TeV gamma rays whose origin is still unclear. To better understand the diffuse excess the separation of sources and diffuse emission is studied using the latest and most sensitive data set of the Milagro experiment. In addition, a newly developed technique is applied that allows the energy spectrum of the TeV gamma rays to be reconstructed using Milagro data. No conclusive statement can be made about the spectrum of the diffuse emission from the Cygnus region because of its low significance of 2.2 σ above the background in the studied data sample. The entire Cygnus region emission is best fit with a power law with a spectral index of α=2.40 (68% confidence interval: 1.35-2.92) and a exponential cutoff energy of 31.6 TeV (10.0-251.2 TeV). In the case of a simple power law assumption without a cutoff energy the best fit yields a spectral index of α=2.97 (68% confidence interval: 2.83-3.10). Neither of these best fits are in good agreement with the data. The best spectral fit to the TeV emission from MGRO J2019+37, the brightest source in the Cygnus region, yields a spectral index of α=2.30 (68% confidence interval: 1.40-2.70) with a cutoff energy of 50.1 TeV (68% confidence interval: 17.8-251.2 TeV) and a spectral index of α=2.75 (68% confidence interval: 2.65-2.85) when no exponential cutoff energy is assumed. According to the present analysis, MGRO J2019+37 contributes 25% to the differential flux from the entire Cygnus at 15 TeV.

Four-window technique for measuring optical-phase-space-time-frequency tomography

A new approach, the four-window technique, was developed to measure optical phase-space-time-frequency tomography (OPSTFT). The four-window technique is based on balanced heterodyne detection with two local oscillator (LO) fields. This technique can provide independent control of position, momentum, time and frequency resolution. The OPSTFT is a Wigner distribution function of two independent Fourier transform pairs, phase-space and time-frequency. The OPSTFT can be applied for early disease detection.