Frequency Hopping Spread Spectrum (FHSS) signaling is used across many devices operating in both regulated and unregulated bands. In either situation, if there is a malicious device operating within these bands, or more simply a user operating out of the required specifications, the identification this user important to insure communication link integrity and interference mitigation. The identification of a user involves the grouping of that users signal transmissions, and the separation of those transmission from transmissions of other users in a shared frequency band. Traditional signal separation methods often require difficult to obtain hardware fingerprinting characteristics or approximate geo-location estimates. This work will consider the characteristics of FHSS signals that can be extracted directly from signal detection. From estimates of these hopping characteristics, novel source separation with classic clustering algorithms can be performed. Background knowledge derived from the time domain representation of received waveforms can improve these clustering methods with the novel application of cannot-link pairwise constraints to signal separation. For equivalent clustering accuracy, constraint-based clustering tolerates higher parameter estimation error, caused by diminishing received signal-to-noise ratio (SNR), for example. Additionally, prior work does not fully cover the implications of detecting and estimating FHSS signals using image segmentation on a Time-Frequency (TF) waterfall. This work will compare several methods of FHSS signal detection, and discuss how each method may effect estimation accuracy and signal separation quality. The use of constraint-based clustering is shown to provide higher clustering accuracy, resulting in more reliable separation and identification of active users in comparison to traditional clustering methods. / Master of Science / The expansion of technology in areas such as smart homes and appliances, personal devices, smart vehicles, and many others, leads to more and more devices using common wireless communication techniques such as WiFi and Bluetooth. While the number of wirelessly connected users expands, the range of frequencies that support wireless communications does not. It is therefore essential that each of these devices unselfishly share the available wireless resources. If a device is using more resources than the required limits, or causing interference with other’s communications, this device will impact many others negatively and therefore preventative action must be taken to prevent further disruption in the wireless environment. Before action can be taken however, the device must first be identified in a mixture of other wireless activity. To identify a specific device, first, a wireless receiver must be in close enough proximity to detect the power that the malicious device is emitting through its wireless communication. This thesis provides a method that can be used to identify a problem user based only off of its wireless transmission behavior. The performance of this identification is shown with respect to the received signal power which represents the necessary range that a listening device must be to identify and separate a problem user from other cooperative users that are communicating wirelessly.

When studying detection systems, parameters associated with the Receiver Operating Characteristic (ROC) curve are often estimated to assess system performance. In some applied settings it is often not possible to test the detection system with large numbers of stimuli. The resulting small sample statistics many have undesirable properties. The characteristics of these small sample ROC estimators were examined in a Monte Carlo simulation. Three popular ROC parameters were chosen for study. One of the parameters was a single parameter index of system performance, Area under the ROC curve. The other parameters, ROC intercept and slope, were considered as a pair. ROC intercept and slope were varied along with sample size and points on the certainty rating scale to form a four way factorial design. Several types of estimators were examined. For the parameter, Area under the curve, Maximum Likelihood (ML), three types of Least Squares (LS), and Distribution Free (DF) estimators were considered. Except for the DF estimator, the same estimators were considered for the parameters, intercept and slope. These estimators were compared with respect to three characteristics: bias, efficiency, and consistency. For Area under the curve, the ML estimator was the least biased. The DF estimator was the most efficient, and all the estimators except the DF estimator appeared to be consistent. For intercept and slope the LS estimator that minimized vertical error of the points from the ROC curve (line) was the least biased for both estimators. This LS estimator was also the most efficient. This estimator along with the ML estimator also appeared to be the most consistent. The other two estimators had no significant trend toward consistency. These results along with other findings, illustrate that different estimators may be "best" for different sample sizes and for different parameters. Therefore, researchers should carefully consider the characteristics of ROC estimators before using them as indices of system performance.

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The Advanced Airborne Test Instrumentation System (AATIS), one of the major instrumentation systems in use today by the Department of Defense (DoD), was developed in the late 1980's to improve and modernize its predecessor - the Airborne Test Instrumentation System (ATIS). Use of AATIS, by not only the Air Force but the Navy and Army, has improved instrumentation commonality and interoperability across multiple test programs. AATIS, developed by the same manufacturer as the DoD Common Airborne Instrumentation System (CAIS), has a common bus structure - enabling cross utilization of many components which will ease transition from one system to another. The objective of this paper is to provide an overview on the Advanced ATIS System and its logistics support concept. For system description, an overview is presented on the airborne system and related ground support equipment. A brief description is given on the three levels of maintenance being used or planned for by the using activities. Finally, a projection is presented on the utilization of this system for the next 3 years.

No description available.

This thesis presents a multi-phase injection locking (IL) technique and its application in the locking range extension in multi-phase injection locking oscillators (ILOs) for Terahertz (THz) signal generation. The proposed technique can significantly increase the frequency locking range of a multi-phase injection locking oscillator compared to the conventional single-phase injection locking scheme. Based on the multi-phase IL technique and sub-harmonic ILOs, an “active frequency multiplier chain” architecture and a multi-ring system layout topology are also proposed to achieve scalable THz signal generation. As proof of concept, a cascaded 3-stage 3-phase 2nd-order sub-harmonic ILO chain is implemented in the IBM 9HP SiGe BiCMOS process. The design achieves a maximum output power of -16.6dBm at 498GHz, a phase noise of -87dBc/ Hz at 1MHz offset, and a total 5.1% frequency tuning range from 485.1GHz to 510.7GHz, which is the largest frequency tuning range among all the reported silicon-based THz oscillator sources in the 0.5THz band.

No description available.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / In this paper we study a new streaming Compressive Sensing (CS) technique that aims to replace high speed Analog to Digital Converters (ADC) for certain classes of signals and reduce the artifacts that arise from block processing when conventional CS is applied to continuous signals. We compare the performance of both streaming and block processing methods on several types of signals and quantify the signal reconstruction quality when packet loss is applied to the transmitted sampled data.

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Zoology / Doctoral / Doctor of Philosophy

<p>In recent years, deploying multiple antennas at both transmitter and receiver has appeared as a very promising technology. By exploiting the spatial domain, multiple-input multiple-output (MIMO) systems can support extremely high data rates as long as the environments can provide sufficiently rich scattering. To design high performance MIMO wireless systems and predict system performance under various circumstances, it is of great interest to have accurate MIMO wireless channel models for different scenarios. In this thesis, we characterize and model MIMO radio propagation channels based on indoor MIMO channel measurements.</p><p>The recent development on MIMO radio channel modeling is briefly reviewed in this thesis. The models are categorized into non-physical and physical models, and discussed respectively. The non-physical models primarily rely on the statistical characteristics of MIMO channels obtained from the measured data, while the physical models describe the MIMO channel (or its distribution) via some physical parameters. We also briefly mention the MIMO channel modeling work within the IEEE 802.11n and 3GPP/3GPP2 standardization work.</p><p>For the narrowband case, a non line-of-sight (NLOS) indoor MIMO channel model is presented. The model is based on a Kronecker structure of the channel covariance matrix and the fact that the channel is complex Gaussian. It is extended to line-of-sight (LOS) scenario by estimating and modeling the dominant component separately. For the wideband case, two NLOS MIMO channel models are proposed. The first model uses the average power delay profile and the Kronecker structure of the second order moments of each channel tap to model the wideband MIMO channel, while the second model combines a simple single-input single-output (SISO) model with the same Kronecker structure of the second order moments. Monte-Carlo simulations are used to generate indoor MIMO channel realizations according to the above models. The results are compared with the measured data and good agreement has been observed.</p><p>Under the assumption of spatial wide sense stationary, a lower bound of the maximum Kronecker model errors is obtained by employing a combination of grid search and semidefinite programming to explore the feasible region. Numerical examples show that the bound is tight for moderate number of grid points. By comparing the worst case model errors with the model errors obtained from the measured channels, we find that the channel correlation matrix in these measurements can, indeed, be well approximated by the Kronecker product of the correlation matrix at the transmitter and the receiver.</p><p>To model wideband MIMO channels, it is important to investigate the angular statistics on both the tap and cluster levels. Based on 5~GHz indoor wireless channel measurements, a frequency domain space alternating generalized expectation maximization (FD-SAGE) algorithm is employed to estimate the multipath components from the measured data. We then manually identify the clusters of the multipaths and calculate the tap and cluster angular spreads (ASs) for each identified cluster. It is found that for the 100 MHz channels, the average tap AS is just a few degrees less than the cluster AS and the difference diminishes for small channel bandwidth.</p>