Thesis(M.S.)--Auburn University, 2006. / Abstract. Vita. Includes bibliographic references (p.84-85).

"Author biographies are available in the expanded on-line version of this article [http://www.insidegnss.com/auto/julyaug09-tetewsky-final.pdf]" / "July/August 2009." Web site title: Making Sense of GPS Inter-Signal Corrections : Satellite Calibration Parameters in Legacy and Modernized Ionosphere Correction Algorithms.

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M.Ing. / The Graphics Processor Unit (GPU) has been in the gaming industry for several years now. Of late though programmers and scientists have started to use the parallel processing or stream processing capabilities of the GPU in general numerical applications. The Monte Carlo method is a processing intensive methods, as it evaluates systems with stochastic components. The stochastic components require several iterations of the systems to develop an idea of how the systems reacts to the stochastic inputs. The stream processing capabilities of GPUs are used for the analysis of such systems. Evaluating low-cost Inertial Measurement Units (IMU) for utilisation in Inertial Navigation Systems (INS) is a processing intensive process. The non-deterministic or stochastic error components of the IMUs output signal requires multiple simulation runs to properly evaluate the IMUs performance when applied as input to an INS. The GPU makes use of stream processing, which allows simultaneous execution of the same algorithm on multiple data sets. Accordingly Monte Carlo techniques are applied to create trajectories for multiple possible outputs of the INS based on stochastically varying inputs from the IMU. The processing power of the GPU allows simultaneous Monte Carlo analysis of several IMUs. Each IMU requires a sensor error model, which entails calibration of each IMU to obtain numerical values for the main error sources of lowcost IMUs namely scale factor, non-orthogonality, bias, random walk and white noise. Three low-cost MEMS IMUs was calibrated to obtain numerical values for their sensor error models. Simultaneous Monte Carlo analysis of each of the IMUs is then done on the GPU with a resulting circular error probability plot. The circular error probability indicates the accuracy and precision of each IMU relative to a reference trajectory and the other IMUs trajectories. Results obtained indicate the GPU to be an alternative processing platform, for large amounts of data, to that of the CPU. Monte Carlo simulations on the GPU was performed 200 % faster than Monte Carlo simulations on the CPU. Results obtained from the Monte Carlo simulations, indicated the Random Walk error to be the main source of error in low-cost IMUs. The CEP results was used to determine the e ect of the various error sources on the INS output.

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A 6-Degree Of Freedom (DOF) numeric model and computer simulation along with the 1/10th scale physical model of the Rapidly Deployable Stable Platform (RDSP) are being developed at Florida Atlantic University in response to military needs for ocean platforms with improved sea keeping characteristics. The RDSP is a self deployable spar platform with two distinct modes of operation enabling long distance transit and superior seakeeping. The focus of this research is the development of a Dynamic Position (DP) and motion mitigation system for the RDSP. This will be accomplished though the validation of the mathematical simulation, development of a novel propulsion system, and implementation of a PID controller. The result of this research is an assessment of the response characteristics of the RDSP that quantifies the performance of the propulsion system coupled with active control providing a solid basis for further controller development and operational testing. / by Sean P. Marikle. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Despite significant progress in GPS/INS integration-based direct geo-referencing (DGR) technology over the past decade, its performance still needs to be improved in terms of accuracy and tolerance to GPS outages. This is mainly due to the limited geometric strength of the GPS satellite constellation, the quality of INS and the system integration technology. This research is focused on pseudolite (PL) augmentation to enhance the geometric strength of the GPS satellite constellation, and the Neural Network (NN) aided Kalman filter (KF) system integration algorithm to improve the geo-referencing system's performance during GPS outages. The main research contributions are summarized as below: a) Systematic errors introduced by pseudolites have been investigated. Theoretical and numerical analyses reveal that errors of troposphere delay modelling, differential nonlinearity and pseudolite location are sensitive to pseudolite receiver geometry. Their effect on final positioning solutions can be minimised by selecting optimal pseudolite and receiver locations, which is referred to as geometry design. Optimal geometry design for pseudolite augmented systems has been proposed based on simulation results in airborne surveying scenarios. b) Nonlinear geometry bias, or nonlinearity, exists in single difference processes when the unit vectors from the reference and user receivers to a satellite or pseudolite are non-parallel. Similar to long baseline differential GPS (DGPS), nonlinearity is a serious issue in pseudolite augmentation. A Projected Single Difference (PSD) method has been introduced to eliminate nonlinear geometry bias. An optimized expression has been derived to calculate the direction of project vectors, and the advantages of applying PSD in pseudolite augmented airborne DGPS have been demonstrated. c) A new method for pseudolite tropospheric delay modelling has been proposed, which is based on single-differenced GPS tropospheric delay models. The performance of different models has been investigated through simulations and field testing. The advantages and limitations of each method have been analysed. It is determined that the Bouska model performs relatively well in all ranges and elevations if the meteorological parameters in the models can be accurately collected. d) An adaptive pseudolite tropospheric delay modelling method has been developed to reduce modelling error by estimating meteorological parameters in real-time, using GPS and pseudolite measurements. Test results show that pseudolite tropospheric delay modelling errors can be effectively mitigated by the proposed method. e) A novel geo-referencing system based on GPS/PL/INS integration has been developed as an alternative to existing GPS/INS systems. With the inclusion of pseudolite signals to enhance availability and geometry strength of GPS signals, the continuity and precision of the GPS/INS system can be significantly improved. Flight trials have been conducted to evaluate the system performance for airborne mapping. The results show that the accuracy and reliability of the geo-referenced solution can be improved with the deployment of one or more pseudolites. f) Two KF and NN hybrid methods have been proposed to improve geo-referenced results during GPS outages. As the KF prediction diverges without measurement update, the performance of a GPS/INS integrated system degrades rapidly during GPS outages. Neural networks can overcome this limitation of KF. The first method uses NN to map vehicle manoeuvres with KF measurement in a loosely coupled GPS/INS system. In the second method, an NN is trained to map INS measurements with selected KF error states in a tightly coupled GPS/INS system when GPS signals are available. These training results can be used to modify KF time updates. Optimal input/output and NN structure have been investigated. Field tests show that the proposed hybrid methods can dramatically improve geo-referenced solutions during GPS outages.

The objective of the thesis is concerned on the problem of detecting superpositional signals in a wireless channel. In many wireless systems, an observed signal is commonly represented as a linear combination of the transmitted signal with the interfering signals dispersed in space and time. These systems are generally known as the interference-limited systems. The mathematical model of these systems is generally referred as a superpositional model. A distinguished characteristic of signal transmission in a time-varying wireless channel is that the channel process is not known a priori. Reliable signal reception inherently requires exploiting the structure of the interfering signals under channel uncertainty. Our goal is to design computational efficient receivers for various interference-limited systems by using advanced statistical signal processing techniques. The thesis consists of four main parts. Firstly, we have proposed a novel Multi-Input Multi-Output (MIMO) signal detector, known as the neighbourhood exploring detector (NED). According to the maximum likelihood principle, the space time MIMO detection problem is equivalent to a NP-hard combinatorial optimization problem. The proposed detector is a sub-optimal maximum likelihood detector which eliminates exhaustive multidimensional searches. Secondly, we address the problem of signal synchronization for Global Positioning System (GPS) in a multipath environment. The problem of multipath mitigation constitutes a joint estimation of the unknown amplitudes, phases and time delays of the linearly combined signals. The complexity of the nonlinear joint estimation problem increases exponentially with the number of signals. We have proposed two robust GPS code acquisition systems with low complexities. Thirdly, we deal with the problem of multipath mitigation in the spatial domain. A GPS receiver integrated with the Inertial Navigation System (INS) and a multiple antenna array is considered. We have designed a software based GPS receiver which effectively estimates the directions of arrival and the time of arrival of the linearly combined signals. Finally, the problem of communications with unknown channel state information is investigated. Conventionally, the information theoretical communication problem and the channel estimation problem are decoupled. However the training sequence, which facilitates the estimation of the channel, reduces the throughput of the channel. We have analytically derived the optimal length of the training sequence which maximizes the mutual information in a block fading channel.

In this thesis, robust set-valued estimation is studied and its application to in-flight alignment of strapdown inertial navigation systems (SINS) with large heading uncertainty is performed. It is known that the performance of the Kalman filter is vulnerable to modeling errors. One of the estimation methods, which are robust against modeling errors, is robust set-valued estimation. In this approach, the filter calculates the set of all possible states, which are consistent with uncertainty inputs satisfying an integral quadratic constraint (IQC) for given measured system outputs. In this thesis, robust set-valued filter with deterministic input is derived. In-flight alignment of SINS with Kalman filtering using external measurements is a widely used technique to eliminate the initial errors. However, if the initial errors are large then the performance of standard Kalman filtering technique is degraded due to modeling error caused by linearization process. To solve this problem, a novel linear norm-bounded uncertain error model is proposed where the remaining second orders terms due to linearization process are considered as norm-bounded uncertainty regarding only the heading error is large. Using the uncertain error model, the robust set-valued filter is applied to in-flight alignment problem. The comparison of the Kalman filter and the robust filter is done on a simulated trajectory and a real-time data. The simulation results show that the modeling errors can be compensated to some extent in Kalman filter by increasing the process noise covariance matrix. However, for very large initial heading errors, the proposed method outperforms the Kalman filter.