A modified Gaussian sum approach to estimation of non-Gaussiansignals

A Gaussian sum estimation algorithm has previously been developed to deal with noise processes that are non-Gaussian. Inherent in this algorithm is a serious growing memory problem that causes the number of terms in the Gaussian sum to increase exponentially at each iteration. A modified Gaussian sum estimation algorithm using an adaptive filter is developed that avoids the growing memory problem of the previous algorithm while providing effective state estimation. The adaptive filter is comprised of a fixed set of estimators operating in parallel with each individual estimate possessing its own corresponding weighting term. A simulation example illustrates the new non-Gaussian estimation technique     

Stabilized Linear FM Generator

A technique for generating high-linearity chirp (linear FM) signals is presented. It uses a delay line and mixer to sense the slope of the output of a swept oscillator, and the difference from desired slope is fed back to correct the oscillator frequency. The technique also improves the coherence of the oscillator. An analysis is performed of the amount of linearization that can be achieved for three possible loop configurations. Experimental results are presented of tests run on a backward wave oscillator (BWO) operating over a bandwidth of 240 MHz with time duration of 120 ?s. The results indicate a linearity corresponding to a phase error (from parabolic) of 30 degrees (peak) and coherence within 5 degrees.     

A necessary condition for effective performance of the multiplemodel adaptive estimator

Effective adaptive estimation for a general linear system driven by an input modeled by a randomly switching Gaussian process is considered. The performance of the multiple model adaptive estimator (MMAE) is, in some cases, unexpectedly hampered by a necessary condition not satisfied by the linear system. This key dependency for effective MMAE performance is based on a particular property of the DC gain of the linear system     

Stretch: A Time-Transformation Technique

Stretch is a passive, linear, time-variant technique for performing temporal operations on many classes of signals. The technique employs three dispersive networks and a mixer. Signal slowdown, speedup, or time reversal can be attained by choice of network slopes. These temporal operations are performed within a signal "window," and the duration of the window is determined by the network time-bandwidth products. Both heuristic argumentation and rigorous analysis are presented, as are the results of a simple laboratory experiment.