The purpose of this study is to undertake the analysis of underwater detection and tracking using the Doppler phase-shift effects to enhance the detection capability. The fundamental principle is owing to the fact that the M-sequence may result in a better distinction to the echo returning from a moving target than the traditional LFM signal, in that the matched filter using M-sequence may need to estimate and compensate the doppler shift due to the moving target. The experiments were carried out in two harbors: True Love Pier of Kaohsiung Harbor (TLPKH) and Woods Hole Hrabor (WHH). The TLPKH is an inner harbor, with sediment being mud, while the WHH is an open types, suitable for target detection. The results from WHH experiment has shown that when the results from M-sequence and traditional LFM signal were compared, the M-sequence yields much better capability both in detection and estimation of the speed of the moving target along the beam axis. However, the signals from TLPKH were too weak for analysis, therefore, the data from TLPKH were used to analyze the environmental noise, transmission loss, which were combined with estimated values for sonar parameters to conduct the sonar performance analysis in an harborenvironment.

This thesis presents a simulation of an LFM pulse-Doppler radar for surface-to-air applications and compares the performance of multiple CFAR detectors in processing the resulting range-Doppler maps. Each CFAR detector is reviewed and simulated. Their effectiveness in reducing target masking is analyzed. In addition, a new CFAR detector, the RDWT-CA-CFAR detector, is developed that uses the CDF 5/3 wavelet to decompose the range-data of the range-Doppler map along the range dimension and filter the target data from the reference cells, as a means to reduce or eliminate target masking. The QccPack library is used to perform RDWT functions. It is shown that the novel RDWT-CA-CFAR detector performs better in processing range-Doppler maps when compared to the other robust CFAR detectors covered in this project.

abstract: For synthetic aperture radar (SAR) image formation processing, the chirp scaling algorithm (CSA) has gained considerable attention mainly because of its excellent target focusing ability, optimized processing steps, and ease of implementation. In particular, unlike the range Doppler and range migration algorithms, the CSA is easy to implement since it does not require interpolation, and it can be used on both stripmap and spotlight SAR systems. Another transform that can be used to enhance the processing of SAR image formation is the fractional Fourier transform (FRFT). This transform has been recently introduced to the signal processing community, and it has shown many promising applications in the realm of SAR signal processing, specifically because of its close association to the Wigner distribution and ambiguity function. The objective of this work is to improve the application of the FRFT in order to enhance the implementation of the CSA for SAR processing. This will be achieved by processing real phase-history data from the RADARSAT-1 satellite, a multi-mode SAR platform operating in the C-band, providing imagery with resolution between 8 and 100 meters at incidence angles of 10 through 59 degrees. The phase-history data will be processed into imagery using the conventional chirp scaling algorithm. The results will then be compared using a new implementation of the CSA based on the use of the FRFT, combined with traditional SAR focusing techniques, to enhance the algorithm's focusing ability, thereby increasing the peak-to-sidelobe ratio of the focused targets. The FRFT can also be used to provide focusing enhancements at extended ranges. / Dissertation/Thesis / M.S. Electrical Engineering 2011

Opportunities to use synthetic aperture radar (SAR) in scientific studies and military operations are expanding with the development of small SAR systems that can be operated on small unmanned air vehicles (UAV)s. While the nimble nature of small UAVs make them an attractive platform for many reasons, small UAVs are also more prone to deviate from a linear course due autopilot errors and external forces such as turbulence and wind. Thus, motion compensation and improved processing algorithms are required to properly focus the SAR images. The work of this dissertation overcomes some of the challenges and addresses some of the opportunities of operating SAR on small UAVs. Several contributions to SAR backprojection processing for UAV SARs are developed including: 1. The derivation of a novel SAR backprojection algorithm that accounts for motion during the pulse that is appropriate for narrow or ultra-wide-band SAR. 2. A compensation method for SAR backprojection to enable radiometrically accurate image processing. 3. The design and implementation of a real-time backprojection processor on a commercially available GPU that takes advantage of the GPU texture cache. 4. A new autofocus method that improves the image focus by estimating motion measurement errors in three dimensions, correcting for both amplitude and phase errors caused by inaccurate motion parameters. 5. A generalization of factorized backprojection, which we call the Dually Factorized Backprojection method, that factorizes the correlation integral in both slow-time and fast-time in order to efficiently account for general motion during the transmit of an LFM-CW pulse. Much of this work was conducted in support of the Characterization of Arctic Sea Ice Experiment (CASIE), and the appendices provide substantial contributions for this project as well, including: 1. My work in designing and implementing the digital receiver and controller board for the microASAR which was used for CASIE. 2. A description of how the GPU backprojection was used to improved the CASIE imagery. 3. A description of a sample SAR data set from CASIE provided to the public to promote further SAR research.

The SeaWinds Calibration Ground Station (CGS) is a passive ground station used to receive and sample transmissions from the SeaWinds scatterometer. During post processing, the received transmissions are characterized in order to verify proper instrument operation and to eliminate error in satellite telemetry and in data products generated from processing SeaWinds data. Sources of instrument error include uncertainties in transmitted power, pulse timing, and carrier frequency drift. Identifying these errors prevents their propagation to data products. A key aspect of this analysis involves accurately estimating the parameters of the SeaWinds transmissions. As better parameter estimates are researched and developed, the scatterometer can be more finely calibrated and better characterized, allowing improved accuracy of environmental measurements. This work explores several methods to estimate SeaWinds frequency parameters by parametrically modeling the signal as a series of linear FM pulses. Improved frequency estimates are obtained by transforming the signal into appropriate signal spaces. These methods are compared and their tradeoffs revealed. SNR regions are assigned to each method to mark appropriate performance bounds, and improvements over previous SeaWinds data analysis methods are shown. Finally, recent estimates of SeaWinds parameters are disclosed. This analysis helps to advance the level to which future scatterometer instruments may be calibrated, providing the potential for more accurate scatterometer data products.

Brigham Young University has developed a low-cost, light-weight, and low power consumption SAR for flight on a small unmanned aerial vehicle (UAV) at low altitudes. This micro-SAR, or uSAR, consumes only 18 watts of power, ideal for application on a small UAV. To meet these constraints, a linear frequency modulation-continuous wave (LFM-CW) transmit signal is utilized. Use of an LFM-CW signal introduces some differences from the typical strip map SAR processing model that must be addressed in signal processing algorithms. This thesis presents a derivation of the LFM-CW signal model and the associated image processing algorithms used for the uSAR developed at BYU. A data simulator for the BYU LFM-CW SAR is detailed and results are provided for the case when the simulated data are processed using the uSAR algorithms. Data processing schemes are discussed, including compression, receive signal phase detection, interference filtering and auto-focusing. Finally, data collected from the instrument itself are processed and presented.

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<p>The inverse problem of modeling biochemical processes mathematically from measured time course data falls into the category of system identification and parameter estimation. Analyzing the time course data would provide valuable insights into the model structure and dynamics of the biochemical system. Based on the types of biochemical reactions, such as metabolic networks and genetic networks, several modeling frameworks have been proposed, developed and proved effective, including the Michaelis-Menten equation, the Biochemical System Theory (BST), etc. One bottleneck in analyzing the obtained data is the estimation of parameter values within the system model.</p> <p>As most models for molecular biological systems are nonlinear with respect to both parameters and system state variables, estimation of parameters in these models from experimental measurement data is thus a nonlinear estimation problem. In principle, all algorithms for nonlinear optimization can be used to deal with this problem, for example, the Gauss-Newton iteration method and its variants. However, these methods do not take the special structures of biological system models into account. When the number of parameters to be determined increases, it will be challenging and computationally expensive to apply these conventional methods.</p> <p>In this research, several methods are proposed for estimating parameters in two classes of widely used biological system models: the S-system model and the linear fractional model (LFM), by utilizing their structure specialties. For the S-system, two estimation methods are designed. 1) Based on the two-term structure (production and degradation) of the model, an alternating iterative least squares method is proposed. 2) A separation nonlinear least squares method is proposed to deal with the partially linear structure of the model. For the LFM, two estimation methods are provided. 1) The separation nonlinear least squares method can also be adopted to treat the partially linear structure of the LFM, and moreover a modified iterative version is included. 2) A special strategy using the separation principle and the weighted least squares method is implemented to turn the cost function into a quadratic form and thus the estimates for parameters can be analytically solved. Simulation results have demonstrated the effectiveness of the proposed methods, which have shown better performance in terms of estimation accuracy and computation time, compared with those conventional nonlinear estimation methods.</p>

A 50 MHz Frequency Modulated Continuous Wave (FMCW) radar system, developed at the University of Saskatchewan to provide improved spatial and temporal resolution measurements of auroral E-region plasma processes, introduces ambiguous spectral information, due to spectral ghosting, for scattering events in which multiple radar echoes are detected. This thesis identifies two Linearly Frequency Modulated (LFM) radar waveforms used by the FMCW system as the source of the ghosting. An analysis procedure designed to counteract the spectral ghosting problem is developed but is not an ideal solution, and therefore replacement of the LFM waveforms is recommended.<p> A detailed investigation of alternative radar waveforms using the Ambiguity Function and Ambiguity Diagram techniques is performed. A frequency coded continuous wave radar waveform based on a composite Costas sequence is proposed as a successor to the LFM waveforms. The composite Costas radar waveform will conserve the spatial and temporal resolutions extended by the LFM waveforms and preclude any spectral ghosting. Implementing the proposed radar waveform and avoiding receiver saturation issues with the mono-static FMCW radar system in which both the transmitting and receiving antenna arrays are simultaneously and continuously active and geographically co-located is also discussed.<p> In addition to this, two 50 MHz backscatter events are presented in this thesis to demonstrate the effectiveness of the FMCW system, notwithstanding the spectral ghosting complication. The first event from November 21, 2009 is identified as a Type 1 instability and the second from September 13, 2009 is identified as a Type 2 instability which lasted for ~ 16 minutes. Linear plasma fluid theory is used to provide a brief interpretation of both scattering events.

<p>The inverse problem of modeling biochemical processes mathematically from measured time course data falls into the category of system identification and parameter estimation. Analyzing the time course data would provide valuable insights into the model structure and dynamics of the biochemical system. Based on the types of biochemical reactions, such as metabolic networks and genetic networks, several modeling frameworks have been proposed, developed and proved effective, including the Michaelis-Menten equation, the Biochemical System Theory (BST), etc. One bottleneck in analyzing the obtained data is the estimation of parameter values within the system model.</p> <p>As most models for molecular biological systems are nonlinear with respect to both parameters and system state variables, estimation of parameters in these models from experimental measurement data is thus a nonlinear estimation problem. In principle, all algorithms for nonlinear optimization can be used to deal with this problem, for example, the Gauss-Newton iteration method and its variants. However, these methods do not take the special structures of biological system models into account. When the number of parameters to be determined increases, it will be challenging and computationally expensive to apply these conventional methods.</p> <p>In this research, several methods are proposed for estimating parameters in two classes of widely used biological system models: the S-system model and the linear fractional model (LFM), by utilizing their structure specialties. For the S-system, two estimation methods are designed. 1) Based on the two-term structure (production and degradation) of the model, an alternating iterative least squares method is proposed. 2) A separation nonlinear least squares method is proposed to deal with the partially linear structure of the model. For the LFM, two estimation methods are provided. 1) The separation nonlinear least squares method can also be adopted to treat the partially linear structure of the LFM, and moreover a modified iterative version is included. 2) A special strategy using the separation principle and the weighted least squares method is implemented to turn the cost function into a quadratic form and thus the estimates for parameters can be analytically solved. Simulation results have demonstrated the effectiveness of the proposed methods, which have shown better performance in terms of estimation accuracy and computation time, compared with those conventional nonlinear estimation methods.</p>