A space communications study Status report, 15 Dec. 1968 - 15 Mar. 1969

Harmonic distortion in frequency demodulator using feedback, delta modulation, recursive signal processing techniques, and multipath fadin

Fast adaptive algorithms for signal separation

LMS and RLS type algorithms are suggested for decorrelation of multi-channel systems outputs. These algorithms act as signal separators when applied to unknown linear combinations of the inputs. The performance of the suggested algorithms is compared with that of the conventional LMS and RLS algorithms that minimize the mean square error. It. is shown that the correlation matrix eigenvalue spread associated with the LMS decorrelator is always smaller than the eigenvalue spread corresponding to the conventional LMS. resulting in faster convergence speed for the decorrelator. A new RLS type decorrelator algorithm is suggested. The RLS decorrelator is shown to be faster than the LMS decorrelator. not affected by the eigenvalue spread, and comparable in speed with the conventional RLS algorithm. Convergence analysis by simulation shows that the RLS algorithms and the LMS decorrelator have wider regions of convergence than the conventional LMS

Implementation and evaluation of a dual-sensor time-adaptive EM algorithm for signal enhancement

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution August 1991This thesis describes the implementation and evaluation of an adaptive time-domain algorithm for signal enhancement from multiple-sensor observations. The algorithm is first derived as a noncausal time-domain algorithm, then converted into a causal, recursive form. A more computationally efficient gradient-based parameter estimation step is also presented. The results of several experiments using synthetic data are shown. These experiments first illustrate that the algorithm works on data meeting all the assumptions made by the algorithm, then provide a basis for comparing the performance of the algorithm against the performance of a noncausal frequency-domain algorithm solving the same problem. Finally, an evaluation is made of the performance of the simpler gradient-based parameter estimation step

A Novel Approach for Adaptive Signal Processing

Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method

Adaptive Quantizers for Estimation

In this paper, adaptive estimation based on noisy quantized observations is studied. A low complexity adaptive algorithm using a quantizer with adjustable input gain and offset is presented. Three possible scalar models for the parameter to be estimated are considered: constant, Wiener process and Wiener process with deterministic drift. After showing that the algorithm is asymptotically unbiased for estimating a constant, it is shown, in the three cases, that the asymptotic mean squared error depends on the Fisher information for the quantized measurements. It is also shown that the loss of performance due to quantization depends approximately on the ratio of the Fisher information for quantized and continuous measurements. At the end of the paper the theoretical results are validated through simulation under two different classes of noise, generalized Gaussian noise and Student's-t noise