Remote Vibration Estimation Using Displaced-Phase-Center Antenna SAR for Strong Clutter Environments

It has been previously demonstrated that it is possible to perform remote vibrometry using synthetic aperture radar (SAR) in conjunction with the discrete fractional Fourier transform (DFrFT). Specifically, the DFrFT estimates the chirp parameters (related to the instantaneous acceleration of a vibrating object) of a slow-time signal associated with the SAR image. However, ground clutter surrounding a vibrating object introduces uncertainties in the estimate of the chirp parameter retrieved via the DFrFT method. To overcome this shortcoming, various techniques based on subspace decomposition of the SAR slow-time signal have been developed. Nonetheless, the effectiveness of these techniques is limited to values of signal-to-clutter ratio ≥5 dB. In this paper, a new vibrometry technique based on displaced-phase-center antenna (DPCA) SAR is proposed. The main characteristic of a DPCA-SAR is that the clutter signal can be canceled, ideally, while retaining information on the instantaneous position and velocity of a target. In this paper, a novel method based on the extended Kalman filter (EKF) is introduced for performing vibrometry using the slow-time signal of a DPCA-SAR. The DPCA-SAR signal model for a vibrating target, the mathematical characterization of the EKF technique, and vibration estimation results for various types of vibration dynamics are presented

Remote Vibration Estimation Using Displaced Phase Center Antenna SAR in a Strong Clutter Environment

Synthetic aperture radar (SAR) is a ubiquitous remote sensing platform that is used for numerous applications. In its most common con\ufb01guration, SAR produces a high resolution, two-dimensional image of a scene of interest. An underlying assumption when creating this high resolution image is that all targets in the ground scene are stationary throughout the duration of the image collection. If a target is not static, but instead vibrating, it introduces a modulation on the returned radar signal termed the micro-Doppler e\ufb00ect. The ability to estimate the targets vibration frequency and vibration amplitude by exploiting the micro-Doppler e\ufb00ect, all while in a high clutter environment can provide strategic information for target identi\ufb01cation and target condition/status. This thesis discusses one method that processes the non-stationary signal of interest generated by the vibrating target in displaced phase center antenna (DPCA)-SAR in high clutter. The method is based on the extended Kalman \ufb01lter (EKF) \ufb01rst proposed by Dr. Wang in his PhD dissertation titled Time-frequency Methods for Vibration Estimation Using Synthetic Aperture Radar [24]. Previously, EKF method could accurately estimate the target\u27s vibration frequency for single component sinusoidal vibrations. In addition, the target\u27s vibration amplitude and position could be tracked throughout the duration of the aperture for single component sinusoidal vibrations. This thesis presents a modi\ufb01cation to the EKF method, which improves the EKF method\u27s overall performance. This modi\ufb01cation improves the tracking capability of single component vibrations and provides reliable position tracking for several other di\ufb00erent types of vibration dynamics. In addition, the EKF method is more reliable at higher noise levels. More speci\ufb01cally, for a single component vibration, the mean square error (MSE) of the original method is .2279, while the MSE of the method presented in this paper is .1503. Therefore, the method presented in this paper improves the position estimate of the vibrating target by 34% when SNR = 15 dB. For the multicomponent vibrations, the mean square error of the estimated target position is reduced b 76% when SNR = 15 dB. The original EKF method and the modi\ufb01ed EKF method as well as simulations for various target vibration dynamics are provided in this thesis.\u2

Efficient and Robust Algorithms for Statistical Inference in Gene Regulatory Networks

Inferring gene regulatory networks (GRNs) is of profound importance in the field of computational biology and bioinformatics. Understanding the gene-gene and gene- transcription factor (TF) interactions has the potential of providing an insight into the complex biological processes taking place in cells. High-throughput genomic and proteomic technologies have enabled the collection of large amounts of data in order to quantify the gene expressions and mapping DNA-protein interactions. This dissertation investigates the problem of network component analysis (NCA) which estimates the transcription factor activities (TFAs) and gene-TF interactions by making use of gene expression and Chip-chip data. Closed-form solutions are provided for estimation of TF-gene connectivity matrix which yields advantage over the existing state-of-the-art methods in terms of lower computational complexity and higher consistency. We present an iterative reweighted ℓ2 norm based algorithm to infer the network connectivity when the prior knowledge about the connections is incomplete. We present an NCA algorithm which has the ability to counteract the presence of outliers in the gene expression data and is therefore more robust. Closed-form solutions are derived for the estimation of TFAs and TF-gene interactions and the resulting algorithm is comparable to the fastest algorithms proposed so far with the additional advantages of robustness to outliers and higher reliability in the TFA estimation. Finally, we look at the inference of gene regulatory networks which which essentially resumes to the estimation of only the gene-gene interactions. Gene networks are known to be sparse and therefore an inference algorithm is proposed which imposes a sparsity constraint while estimating the connectivity matrix.The online estimation lowers the computational complexity and provides superior performance in terms of accuracy and scalability. This dissertation presents gene regulatory network inference algorithms which provide computationally efficient solutions in some very crucial scenarios and give advantage over the existing algorithms and therefore provide means to give better understanding of underlying cellular network. Hence, it serves as a building block in the accurate estimation of gene regulatory networks which will pave the way for finding cures to genetic diseases

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

Identification of cardiac signals in ambulatory ECG data

The Electrocardiogram (ECG) is the primary tool for monitoring heart function. ECG signals contain vital information about the heart which informs diagnosis and treatment of cardiac conditions. The diagnosis of many cardiac arrhythmias require long term and continuous ECG data, often while the participant engages in activity. Wearable ambulatory ECG (AECG) systems, such as the common Holter system, allow heart monitoring for hours or days. The technological trajectory of AECG systems aims towards continuous monitoring during a wide range of activities with data processed locally in real time and transmitted to a monitoring centre for further analysis. Furthermore, hierarchical decision systems will allow wearable systems to produce alerts or even interventions. These functions could be integrated into smartphones.A fundamental limitation of this technology is the ability to identify heart signal characteristics in ECG signals contaminated with high amplitude and non-stationary noise. Noise processing become more severe as activity levels increase, and this is also when many heart problems are present.This thesis focuses on the identification of heart signals in AECG data recorded during participant activity. In particular, it explored ECG filters to identify major heart conditions in noisy AECG data. Gold standard methods use Extended Kalman filters with extrapolation based on sum of Gaussian models. New methods are developed using linear Kalman filtering and extrapolation based on a sum of Principal Component basis signals. Unlike the gold standard methods, extrapolation is heartcycle by heartcycle. Several variants are explored where basic signals span one or two heartcycles, and applied to single or multi-channel ECG data.The proposed methods are extensively tested against standard databases or normal and abnormal ECG data and the performance is compared to gold standard methods. Two performance metrics are used: improvement in signal to noise ratio and the observability of clinically important features in the heart signal. In all tests the proposed method performs better, and often significantly better, than the gold standard methods. It is demonstrated that abnormal ECG signals can be identified in noisy AECG data

Non-GPS Navigation Using Vision-Aiding and Active Radio Range Measurements

The military depends on the Global Positioning System (GPS) for a wide array of advanced weaponry guidance and precision navigation systems. Lack of GPS access makes precision navigation very difficult. Inclusion of inertial sensors in existing navigation systems provides short-term precision navigation, but drifts significantly over long-term navigation. This thesis is motivated by the need for inertial sensor drift-constraint in degraded and denied GPS environments. The navigation system developed consists of inertial sensors, a simulated barometer, three Raytheon DH500 radios, and a stereo-camera image-aiding system. The Raytheon DH500 is a combat communication radio which also provides range measurements between radios. The measurements from each sensor are fused together with an extended Kalman filter to estimate the navigation trajectory. Residual monitoring and the Sage-Husa adaptive algorithm are individually tested in the Kalman filter range update algorithm to help improve the radio range positioning performance. The navigation system is shown to provide long-term inertial sensor drift-constraint with position errors as low as 3 meters