Signal Processing Commons^™

Space-Time Adaptive Processing With Multi-Staged Wiener Filter And Principal Component Signal Dependent Algorithms, Zheng N. Zhou

Master's Theses

Space-time Adaptive Processing (STAP) is a two-dimensional filtering technique for antenna array with multiple spatial channels. The name "space-time" describes the coupling of these spatial channels with pulse-Doppler waveforms. Applications for STAP includes ground moving target indicator (GMTI) for airborne radar systems.

Today, there are strong interests to develop STAP algorithms for operations in “sample starved” environments, where intense environmental interference can reduce STAP capacity to detect and track ground targets. Careful applications of STAP can effectively overcome these conditions by suppressing these interferences and maximize the signal to interference plus noise ratio (SINR). The Multi-stage Wiener filter (MWF) and …

Charge Injection And Clock Feedthrough, Jonathan Yu

Master's Theses

Turning off a transistor introduces an error voltage in switched-capacitor circuits. Circuits such as analog-to-digital converters (ADC), digital-to-analog converters (DAC), and CMOS image sensor pixels are limited in performance due to the effects known as charge injection and clock feedthrough. Charge injection occurs in a switched-capacitor circuit when the transistor turns off and disperses channel charge into the source and drain. The source, which is the sampling capacitor, experiences an error in the sampled voltage due to the incoming channel charge. Simultaneously, the coupling due to gate-source overlap capacitance also contributes to the total error voltage, which is known as …

Detection And Tracking Of Stealthy Targets Using Particle Filters, Philip M. Losie

Master's Theses

In recent years, the particle filter has gained prominence in the area of target tracking because it is robust to non-linear target motion and non-Gaussian additive noise. Traditional track filters, such as the Kalman filter, have been well studied for linear tracking applications, but perform poorly for non-linear applications. The particle filter has been shown to perform well in non-linear applications. The particle filter method is computationally intensive and advances in processor speed and computational power have allowed this method to be implemented in real-time tracking applications. This thesis explores the use of particle filters to detect and track stealthy …

Sensor Integration For Low-Cost Crash Avoidance, Stephane M. Roussel

Master's Theses

This report is a summary of the development of sensor integration for low-cost crash avoidance for over-land commercial trucks. The goal of the project was to build and test a system composed of low-cost commercially available sensors arranged on a truck trailer to monitor the environment around the truck. The system combines the data from each sensor to increase the reliability of the sensor using a probabilistic data fusion approach. A combination of ultrasonic and magnetoresistive sensors was used in this study. In addition, Radar and digital imaging were investigated as reference signals and possible candidates for additional sensor integration. …

Robust Unconstrained Face Detection And Lip Localization Using Gabor Filters, Robert E. Hursig

Master's Theses

Automatic speech recognition (ASR) is a well-researched field of study aimed at augmenting the man-machine interface through interpretation of the spoken word. From in-car voice recognition systems to automated telephone directories, automatic speech recognition technology is becoming increasingly abundant in today’s technological world. Nonetheless, traditional audio-only ASR system performance degrades when employed in noisy environments such as moving vehicles. To improve system performance under these conditions, visual speech information can be incorporated into the ASR system, yielding what is known as audio-video speech recognition (AVASR). A majority of AVASR research focuses on lip parameters extraction within controlled environments, but these …

An Approach Based On Wavelet Decomposition And Neural Network For Ecg Noise Reduction, Suranai Poungponsri

Master's Theses

Electrocardiogram (ECG) signal processing has been the subject of intense research in the past years, due to its strategic place in the detection of several cardiac pathologies. However, ECG signal is frequently corrupted with different types of noises such as 60Hz power line interference, baseline drift, electrode movement and motion artifact, etc. In this thesis, a hybrid two-stage model based on the combination of wavelet decomposition and artificial neural network is proposed for ECG noise reduction based on excellent localization features: wavelet transform and the adaptive learning ability of neural network. Results from the simulations validate the effectiveness of this …

Synthetic Aperture Radar Imaging Simulated In Matlab, Matthew Schlutz

Master's Theses

This thesis further develops a method from ongoing thesis projects with the goal of generating images using synthetic aperture radar (SAR) simulations coded in MATLAB. The project is supervised by Dr. John Saghri and sponsored by Raytheon Space and Airborne Systems. SAR is a type of imaging radar in which the relative movement of the antenna with respect to the target is utilized. Through the simultaneous processing of the radar reflections over the movement of the antenna via the range Doppler algorithm (RDA), the superior resolution of a theoretical wider antenna, termed synthetic aperture, is obtained. The long term goal …

Time-Frequency Analysis Of Intracardiac Electrogram, Erik Brockman

Master's Theses

The Cardiac Rhythm Management Division of St. Jude Medical specializes in the development of implantable cardioverter defibrillators that improve the quality of life for patients diagnosed with a variety of cardiac arrhythmias, especially for patients prone to sudden cardiac death. With the goal to improve detection of cardiac arrhythmias, this study explored the value in time-frequency analysis of intracardiac electrogram in four steps. The first two steps characterized, in the frequency domain, the waveforms that construct the cardiac cycle. The third step developed a new algorithm that putatively provides the least computationally expensive way to identifying cardiac waveforms in the …

Target Tracking Using Various Filters In Synthetic Aperture Radar Data And Imagery, Jessica L. Kiefer

Master's Theses

This thesis explores the use and accuracy of several discrete-time image filters for the purpose of target tracking in Synthetic Aperture Radar imagery. Both extended targets and point targets are used for tracking, showing the need for different types of filters for each target type.

Monte Carlo analysis is performed on the results of the extended target filter results to determine the absolute mean-squared error between the filter prediction of the target centroid and the actual location of the target centroid. Two different filters were chosen for the extended target: Kalman and H Infinity.

Both the Kalman and H Infinity …

Three-Dimensional Target Modeling With Synthetic Aperture Radar, John R. Hupton

Master's Theses

Conventional Synthetic Aperture Radar (SAR) offers high-resolution imaging of a target region in the range and cross-range dimensions along the ground plane. Little or no data is available in the range-altitude dimension, however, and target functions and models are limited to two-dimensional images. This thesis first investigates some existing methods for the computation of target reflectivity data in the deficient elevation domain, and a new method is then proposed for three-dimensional (3-D) SAR target feature extraction.

Simulations are implemented to test the decoupled least-squares technique for high-resolution spectral estimation of target reflectivity, and the accuracy of the technique is assessed. …