This thesis investigates techniques and algorithms for mitigating radio frequency interference (RFI) affecting radio astronomy observations. In the past radio astronomy has generally been performed in radio-quiet geographical locations and unused parts of the radio spectrum, including small protected frequency bands. The increasing use of the entire spectrum and global transmitters such as satellites are forcing the astronomy community to begin implementing active interference cancelling. The amount of harmful interference affecting observations will also increase as future instruments such as the Square Kilometre Array (SKA) are required to use larger bandwidths to reach up to 100 times the current sensitivity levels, and as spectral line observations require observing in bands licensed to other spectrum users. Particular attention is paid to interference cancellation algorithms which make use of reference beams. This has proven to be successful in removing interference from the contaminated astronomical data. Reference antenna cancellers are closely analysed, leading to filters and techniques that can offer improved RFI excision for some important applications. It is shown that pre- and post-correlation reference antenna cancellers give similar results, and an important aspect of the cancellers is the use of a second reference signal when the reference interference-to-noise ratio is low. These modified filters can theoretically offer infinite interference suppression in the voltage domain, equivalent to that of post-correlation interference cancellers, and their internal structure can offer an understanding of the residual RFI and added receiver noise components of a variety of reference antenna techniques. The effect of variable geometric delays is also considered and various filters are compared as a function of the geometric fringe rate.

Research presented in this thesis provides a substantial leap from the study of interesting device physics to fully adaptive analog networks and lays a solid foundation for future development of large-scale, compact, low-power adaptive parallel analog computation systems. The investigation described here started with observation of this potential learning capability and led to the first derivation and characterization of the floating-gate pFET correlation learning rule. Starting with two synapses sharing the same error signal, we progressed from phase correlation experiments through correlation experiments involving harmonically related sinusoids, culminating in learning the Fourier series coefficients of a square wave cite{kn:Dugger2000}. Extending these earlier two-input node experiments to the general case of correlated inputs required dealing with weight decay naturally exhibited by the learning rule. We introduced a source-follower floating-gate synapse as an improvement over our earlier source-degenerated floating-gate synapse in terms of relative weight decay cite{kn:Dugger2004}. A larger network of source-follower floating-gate synapses was fabricated and an FPGA-controlled testboard was designed and built. This more sophisticated system provides an excellent framework for exploring applications to multi-input, multi-node adaptive filtering applications. Adaptive channel equalization provided a practical test-case illustrating the use of these adaptive systems in solving real-world problems. The same system could easily be applied to noise and echo cancellation in communication systems and system identification tasks in optimal control problems. We envision the commercialization of these adaptive analog VLSI systems as practical products within a couple of years.

Thesis (Ph.D.)--University of Central Florida, 2008. / Adviser: Wasfy B. Mikhael. Includes bibliographical references (p. 152-166).

(Uncorrected OCR) Abstract Abstract of thesis entitled Robust Statistics Based Adaptive Filtering Algorithms For Impulsive Noise Suppression Submitted by Yuexian Zou for the degree of Doctor of Philosophy at The University of Hong Kong in May 2000 The behavior of an adaptive filter is inherently decided by how its estimation error and the cost function are formulated under certain assumption of the involving signal statistics. This dissertation is concerned with the development of robust adaptive filtering in an impulsive noise environment based on the linear transversal filter (LTF) and the lattice-ladder filer (LLF) structures. Combining the linear adaptive filtering theory and robust statistics estimation techniques, two new cost functions, called the mean M -estimate error (MME) and the sum of weighted M -estimate error (SWME), are proposed. They can be taken as the generalizations of the well-known mean squared error (MSE) and the sum of weighted squares error (SWSE) cost functions when the involving signals are Gaussian. Based on the SWME cost function, the resulting optimal weight vector is governed by an M-estimate normal equation and a recursive least M -estimate (RLM) algorithm is derived. The RLM algorithm preserves the fast initial convergence, lower steady-state 11 Abstract derived. The RLM algorithm preserves the fast initial convergence, lower steady-state error and the robustness to the sudden system change of the recursive least squares (RLS) algorithm under Gaussian noise alone. Meanwhile, it has the ability to suppress impulse noise both in the desired and input signals. In addition, using the MME cost function, stochastic gradient based adaptive algorithms, named the least mean Mestimate (LMM) and its transform dOlnain version, the transform domain least mean Mestimate (TLMM) algorithms have been developed. The LMM and TLMM algorithms can be taken as the generalizations of the least-mean square (LMS) and transform domain normalized LMS (TLMS) algorithms, respectively. These two robust algorithms give similar performance as the LMS and TLMS algorithms under Gaussian noise alone and are able to suppress impulse noise appearing in the desired and input signals. It is noted that the performance and the computational complexity of the RLM, LMM and TLMM algorithms have a close relationship with the estimate of the threshold parameters for the M-estimate functions. A robust and effective recursive method has been suggested in this dissertation to estimate the variance of the estimation error and the required threshold parameters with certain confidence to suppress the impulsive noise. The mean and mean square convergence performances of the RLM and the LMM algorithms are evaluated, respectively, when the impulse noise is assumed to be contaminated Gaussian distribution. Motivated by the desirable features of the lattice-ladder filter, a new robust adaptive gradient lattice-ladder filtering algorithm is developed by minimizing an MME cost function together with an embedded robust impulse suppressing process, especially for impulses appearing in the filter input. The resultant robust gradient lattice-robust 111 Abstract normalized LMS (RGAL-RNLMS) algorithm perfonns comparably to the conventional GAL-NLMS algorithm under Gaussian noise alone; meanwhile, it has the capability of suppressing the adverse effects due to impulses in the input and the desired signals. The additional computational complexity compared to the GAL-NLMS algorithm is of O(Nw log Nw) + O(NfI log N,J . Extensive computer simulation studies are undertaken to evaluate the performance of the RLM, LMM, TLMM and the RGAL-RNLMS algorithms under the additive noise with either a contaminated Gaussian distribution or the symmetric alpha-stable (SaS ) distributions. The results substantiate the analysis and demonstrate the effectiveness and robustness of the developed robust adaptive filtering algorithms in suppressing impulsive noise both in the input and the desired signals of the adaptive filter. In conclusion, the proposed approaches in this dissertation present an attempt for developing robust adaptive filtering algorithms in impulsive noise environments and can be viewed as an extension of the linear adaptive filter theory. They may become reasonable and effective tools to solve adaptive filtering problems in a non-Gaussian environment in practice. IV / abstract / toc / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

The Group Research department in Tait Electronics has a reconfigurable platform for MIMO research. In particular, the platform has an adaptive multivariate DFE with the LMS algorithm currently implemented. The LMS algorithm has been simulated and optimised for implementation on the FPGA. The main objective of the research is to investigate an alternative, the RLS algorithm by comparing its performance to the LMS algorithm. The RLS algorithm is known to be more complex than the LMS algorithm but offers the potential for improved performance due to its fast-converging nature. This thesis provides a performance investigation of these adaptive filter algorithms on the MIMO system for the purpose of real-time implementation on the Tait platform. In addition to performance investigation, stability analysis and a feasibility study is shown for the RLS algorithm on the FPGA. The research is industry based and is supported by FRST.

New adaptive-filtering algorithms, also known as adaptation algorithms, are proposed. The new algorithms can be broadly classified into two categories, namely, steepest-descent and Newton-type adaptation algorithms. Several new methods have been used to bring about improvements regarding the speed of convergence, steady-state misalignment, robustness with respect to impulsive noise, re-adaptation capability, and computational load of the proposed algorithms. In chapters 2, 3, and 8, several adaptation algorithms are developed that belong to the steepest-descent family. The algorithms of chapters 2 and 3 use two error bounds with the aim of reducing the computational load, achieving robust performance with respect to impulsive noise, good tracking capability and significantly reduced steady-state misalignment. The error bounds can be either prespecified or estimated using an update formula that incorporates a modified variance estimator. Analyses pertaining to the steady-state mean-square error (MSE) of some of these algorithms are also presented. The algorithms in chapter 8 use a so-called \textit{iterative/shrinkage method} to obtain a variable step size by which improved convergence characteristics can be achieved compared to those in other state-of-the-art competing algorithms. Several adaptation algorithms that belong to the Newton family are developed in chapters 4-6 with the aim of achieving robust performance with respect to impulsive noise, reduced steady-state misalignment, and good tracking capability without compromising the initial speed of convergence. The algorithm in chapter 4 imposes a bound on the $L_1$ norm of the gain vector in the crosscorrelation update formula to achieve robust performance with respect to impulsive noise in stationary environments. In addition to that, a variable forgetting factor is also used to achieve good tracking performance for applications in nonstationary environments. The algorithm in chapter 5 is developed to achieve a reduced steady-state misalignment and improved convergence speed and a reduced computational load. The algorithm in chapter 6 is essentially an extension of the algorithm in chapter 5 designed to achieve robust performance with respect to impulsive noise and reduced computational load. Analyses concerning the asymptotic stability and steady-state MSE of these algorithms are also presented. An algorithm that minimizes Reny's entropy of the error signal is developed in chapter 7 with the aim of achieving faster convergence and reduced steady-state misalignment compared to those in other algorithms of this family. Simulation results are presented that demonstrate the superior convergence characteristics of the proposed algorithms with respect to state-of-the-art competing algorithms of the same family in network-echo cancelation, acoustic-echo cancelation, system-identification, interference-cancelation, time-series prediction, and time-series filtering applications. In addition, simulation results concerning system-identification applications are also used to verify the accuracy of the MSE analyses presented. / Graduate

Thesis (PhD)--University of South Australia, 2001

This thesis investigates techniques and algorithms for mitigating radio frequency interference (RFI) affecting radio astronomy observations. In the past radio astronomy has generally been performed in radio-quiet geographical locations and unused parts of the radio spectrum, including small protected frequency bands. The increasing use of the entire spectrum and global transmitters such as satellites are forcing the astronomy community to begin implementing active interference cancelling. The amount of harmful interference affecting observations will also increase as future instruments such as the Square Kilometre Array (SKA) are required to use larger bandwidths to reach up to 100 times the current sensitivity levels, and as spectral line observations require observing in bands licensed to other spectrum users. Particular attention is paid to interference cancellation algorithms which make use of reference beams. This has proven to be successful in removing interference from the contaminated astronomical data. Reference antenna cancellers are closely analysed, leading to filters and techniques that can offer improved RFI excision for some important applications. It is shown that pre- and post-correlation reference antenna cancellers give similar results, and an important aspect of the cancellers is the use of a second reference signal when the reference interference-to-noise ratio is low. These modified filters can theoretically offer infinite interference suppression in the voltage domain, equivalent to that of post-correlation interference cancellers, and their internal structure can offer an understanding of the residual RFI and added receiver noise components of a variety of reference antenna techniques. The effect of variable geometric delays is also considered and various filters are compared as a function of the geometric fringe rate.

Thesis (Ph. D.)--Ohio University, June, 1990. / Title from PDF t.p.

Thesis (Ph. D.)--Ohio University, June, 1990. / Title from PDF t.p.