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The Kalman filter is an important component of optimal estimation theory. It has applications in a wide range of high performance control systems including navigational, fire control, and targeting systems. The Kalman filter, however, has not been utilized to its full potential due to the limitations of its inherent computational intensiveness which requires "off-line" processing or allows only low bandwidth real-time applications. The recent advances in VLSI circuit technology have created the opportunity to design algorithms and data structures for direct implementation in integrated circuits. A systolic architecture is a concept which allows the construction of massively parallel systems in integrated circuits and has been utilized as a means of achieving high data rates. A systolic system consists of a set of interconnected processing elements, each capable of performing some simple operation. The design of a processing element in an orthogonal systolic architecture will be investigated using the state of the art in VLSI technology. The goal is to create a high speed, high precision processing element which is adaptive to a highly configurable systolic architecture. In order to achieve the necessary high computational throughput, the arithmetic unit of the processing element will be implemented using the Logarithmic Number System. The Systolic architecture approach will be used in an attempt to implement a Kalman filtering system with both a high sampling rate and a small package size. The design of such a Kalman filter would enable this filtering technology to be applied to the areas of process control, computer vision, and robotics.

In the past, passive SONAR range tracking systems have used Extended Kalman filters to process nonlinear time-delay measurements. This approach has several flaws due to the inherent divergence problems of Extended Kalman filters. This paper discusses a new approach which uses a prefilter to linearize the measurements so that they can be processed by a standard Kalman filter. The approach is subsequently expanded for use with an adaptive Kalman filter which allows source maneuvers to be tracked. A new approach to passive Doppler velocity tracking is also proposed which uses a dedicated Kalman filter to track random fluctuations in the sources center frequency. This dedicated tracker simplifies the problem so that it can be handled by a basic adaptive Kalman filter. / Master of Science

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The method of Pseudo-Linear Identification (PLID) is developed for application in an adaptive control loop. The effects of noise are investigated for the case of full-order system identification, and the results are applied to the use of PLID as a reduced-order system estimator. A self-tuning regulator (STR) is constructed using PLID and the effects of reducing the expected order of the system are demonstrated. A second adaptive control algorithm is presented wherein the STR controller is varied to achieve some degree of closeness to a given model (model-reference adaptive control). / Master of Science

Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1976 / Bibliography: leaves 145-148. / by Stuart C. Wright. / M.S. / M.S. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / Spatial multiplexing (SM) systems have received significant attention because the architecture offers high spectral efficiency. However, relatively little research exists on optimization of SM systems in the presence of jamming. In a spatially uncoded SM system, such as V-BLAST, the channel state information is assumed to be unavailable a priori at both transmitter and receiver. Here, Kalman filtering is used to estimate the Rayleigh fading channel at the receiver. The spatial correlation of the jammer plus noise is also estimated, and spatial whitening to reject the jammers is employed in both the Kalman channel estimator and detector. To avoid the exponential complexity of maximum-likelihood (ML) detection, the QRD-M algorithm is employed. In contrast to sphere decoding, QRD-M has fixed decoding complexity of order O(M), and is thus attractive for hardware implementation. The performance of the joint Kalman filter channel estimator, spatial whitener and QRD-M detector is verfied by simulations.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / An acoustic modem employing direct-sequence spread-spectrum (DSSS) signaling is considered with LDPC coding. The underwater acoustic channel is tracked using a Kalman filter which requires accurate data decisions. To improve KF performance and reduce the overall error rate, joint iterative LDPC decoding and channel estimation is proposed based on a factor graph and sum-product algorithm approximation. In this scheme, the decoder posterior log likelihood ratios (LLRs) provide data decisions for the KF. Decoder extrinsic LLRs are similarly incorporated into the detector LLRs to yield improved priors for decoding. Error rate simulations of the overall modem are provided for a shallow-water channel model with Ricean/Rayleigh fading.

A typical petro-chemical or oil-refining plant is known to operate with hundreds if not thousands of control loops. All critical loops are primarily required to operate at their respective optimal levels in order for the plant to run efficiently. With such a large number of vital loops, it is difficult for engineers to monitor and maintain these loops with the intention that they are operating under optimum conditions at all times. Parts of processes are interactive, more so nowadays with increasing integration, requiring the use of a more advanced protocol of control systems. The most widely applied advanced process control system is the Model Predictive Controller (MPC). The success of these controllers is noted in the large number of applications worldwide. These controllers rely on a process model in order to predict future plant responses. Naturally, the performance of model-based controllers is intimately linked to the quality of the process models. Industrial project experience has shown that the most difficult and time-consuming work in an MPC project is modeling and identification. With time, the performance of these controllers degrades due to changes in feed, working regime as well as plant configuration. One of the causes of controller degradation is this degradation of process models. If a discrepancy between the controller’s plant model and the plant itself exists, controller performance may be adversely affected. It is important to detect these changes and re-identify the plant model to maintain control performance over time. In order to avoid the time-consuming process of complete model identification, a model validation tool is developed which provides a model quality indication based on real-time plant data. The focus has been on developing a method that is simple to implement but still robust. The techniques and algorithms presented are developed as far as possible to resemble an on-line software environment and are capable of running parallel to the process in real time. These techniques are based on parametric (regression) and nonparametric (correlation) analyses which complement each other in identifying problems -iiwithin on-line models. These methods pinpoint the precise location of a mismatch. This implies that only a few inputs have to be perturbed in the re-identification process and only the degraded portion of the model is to be updated. This work is carried out for the benefit of SASOL, exclusively focused on the Secunda plant which has a large number of model predictive controllers that are required to be maintained for optimal economic benefit. The efficacy of the methodology developed is illustrated in several simulation studies with the key intention to mirror occurrences present in industrial processes. The methods were also tested on an industrial application. The key results and shortfalls of the methodology are documented. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2010.

The desire for precise polymer property control, minimum wastage through grade transitions, and early instrument fault detection, has led to a significant effort in the modelling and control of ethylene polymerisation world-wide. Control is difficult due to complex inter-relationships between variables and long response times from gas to solid phase. The approach in this study involves modelling using the kinetic equations. This forms the basis of a scheme for real-time kinetic parameter identification and Kalman filtering of the reactor gas composition. The scheme was constructed off-line and tested on several industrial polymer grades using historical plant data. The scheme was also converted into a form for use on the linear low-density polyethylene plant, Poly 2, at POLlFIN Limited. There proved to be no difficulty in the identification step, but the Kalman filter requires more tuning for reliable fault detection. The software has been commissioned on-line and results from the POLlFIN plant match the off-line model exactly. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.