Browsing by Author "Yamada, Randy Matthew"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Highly Robust and Efficient Estimators of Multivariate Location and Covariance with Applications to Array Processing and Financial Portfolio Optimization
    Fishbone, Justin Adam (Virginia Tech, 2021-12-21)
    Throughout stochastic data processing fields, mean and covariance matrices are commonly employed for purposes such as standardizing multivariate data through decorrelation. For practical applications, these matrices are usually estimated, and often, the data used for these estimates are non-Gaussian or may be corrupted by outliers or impulsive noise. To address this, robust estimators should be employed. However, in signal processing, where complex-valued data are common, the robust estimation techniques currently employed, such as M-estimators, provide limited robustness in the multivariate case. For this reason, this dissertation extends, to the complex-valued domain, the high-breakdown-point class of multivariate estimators called S-estimators. This dissertation defines S-estimators in the complex-valued context, and it defines their properties for complex-valued data. One major shortcoming of the leading high-breakdown-point multivariate estimators, such as the Rocke S-estimator and the smoothed hard rejection MM-estimator, is that they lack statistical efficiency at non-Gaussian distributions, which are common with real-world applications. This dissertation proposes a new tunable S-estimator, termed the Sq-estimator, for the general class of elliptically symmetric distributions—a class containing many common families such as the multivariate Gaussian, K-, W-, t-, Cauchy, Laplace, hyperbolic, variance gamma, and normal inverse Gaussian distributions. This dissertation demonstrates the diverse applicability and performance benefits of the Sq-estimator through theoretical analysis, empirical simulation, and the processing of real-world data. Through analytical and empirical means, the Sq-estimator is shown to generally provide higher maximum efficiency than the leading maximum-breakdown estimators, and it is also shown to generally be more stable with respect to initial conditions. To illustrate the theoretical benefits of the Sq for complex-valued applications, the efficiencies and influence functions of adaptive minimum variance distortionless response (MVDR) beamformers based on S- and M-estimators are compared. To illustrate the finite-sample performance benefits of the Sq-estimator, empirical simulation results of multiple signal classification (MUSIC) direction-of-arrival estimation are explored. Additionally, the optimal investment of real-world stock data is used to show the practical performance benefits of the Sq-estimator with respect to robustness to extreme events, estimation efficiency, and prediction performance.
  • Thumbnail Image
    Identification of Interfering Signals in Software Defined Radio Applications Using Sparse Signal Reconstruction Techniques
    Yamada, Randy Matthew (Virginia Tech, 2013-05-03)
    Software-defined radios have the agility and flexibility to tune performance parameters, allowing them to adapt to environmental changes, adapt to desired modes of operation, and provide varied functionality as needed.  Traditional software-defined radios use a combination of conditional processing and software-tuned hardware to enable these features and will critically sample the spectrum to ensure that only the required bandwidth is digitized.  While flexible, these systems are still constrained to perform only a single function at a time and digitize a single frequency sub-band at time, possibly limiting the radio's effectiveness. Radio systems commonly tune hardware manually or use software controls to digitize sub-bands as needed, critically sampling those sub-bands according to the Nyquist criterion.  Recent technology advancements have enabled efficient and cost-effective over-sampling of the spectrum, allowing all bandwidths of interest to be captured for processing simultaneously, a process known as band-sampling.  Simultaneous access to measurements from all of the frequency sub-bands enables both awareness of the spectrum and seamless operation between radio applications, which is critical to many applications.  Further, more information may be obtained for the spectral content of each sub-band from measurements of other sub-bands that could improve performance in applications such as detecting the presence of interference in weak signal measurements. This thesis presents a new method for confirming the source of detected energy in weak signal measurements by sampling them directly, then estimating their expected effects.  First, we assume that the detected signal is located within the frequency band as measured, and then we assume that the detected signal is, in fact, interference perceived as a result of signal aliasing.  By comparing the expected effects to the entire measurement and assuming the power spectral density of the digitized bandwidth is sparse, we demonstrate the capability to identify the true source of the detected energy.  We also demonstrate the ability of the method to identify interfering signals not by explicitly sampling them, but rather by measuring the signal aliases that they produce.  Finally, we demonstrate that by leveraging techniques developed in the field of Compressed Sensing, the method can recover signal aliases by analyzing less than 25 percent of the total spectrum.  
  • Thumbnail Image
    New Method for Directional Modulation Using Beamforming: Applications to Simultaneous Wireless Information and Power Transfer and Increased Secrecy Capacity
    Yamada, Randy Matthew (Virginia Tech, 2017-10-20)
    The proliferation of connected embedded devices has driven wireless communications into commercial, military, industrial, and personal systems. It is unreasonable to expect privacy and security to be inherent in these networks given the spatial density of these devices, limited spectral resources, and the broadcast nature of wireless communications systems. Communications for these systems must have sufficient information capacity and secrecy capacity while typically maintaining small size, light weight, and minimized power consumption. With increasing crowding of the electromagnetic spectrum, interference must be leveraged as an available resource. This work develops a new beamforming method for direction-dependent modulation that provides wireless communications devices with enhanced physical layer security and the ability to simultaneously communicate and harvest energy by exploiting co-channel interference. We propose a method that optimizes a set of time-varying array steering vectors to enable direction-dependent modulation, thus exploiting a new degree of freedom in the space-time-frequency paradigm. We formulate steering vector selection as a convex optimization problem for rapid computation given arbitrarily positioned array antenna elements. We show that this method allows us to spectrally separate co-channel interference from an information-bearing signal in the analog domain, enabling the energy from the interference to be diverted for harvesting during the digitization and decoding of the information-bearing signal. We also show that this method provides wireless communications devices with not only enhanced information capacity, but also enhanced secrecy capacity in a broadcast channel. By using the proposed method, we can increase the overall channel capacity in a broadcast system beyond the current state-of-the-art for wireless broadcast channels, which is based on static coding techniques. Further, we also increase the overall secrecy capacity of the system by enabling secrecy for each user in the system. In practical terms, this results in higher-rate, confidential messages delivered to multiple devices in a broadcast channel for a given power constraint. Finally, we corroborate these claims with simulation and experimental results for the proposed method.