Event Compression Using Recursive Least Squares Signal Processing.

This work presents a technique for time compressing the events in a multiple event signal using a recursive least squares adaptive linear prediction algorithm. Two event compressed signals are extracted from the update equations for the predictor one based on the prediction error and the other on the changes in the prediction coefficients as the data is processed. Using synthetic data containing three all-pole events, experiments are performed to illustrate the performance of the two signals derived from the prediction algorithm. These experiments examine the effects of initialization, white gaussian noise, interevent interference, filtering and decimation on the compressed events contained in the two signals. Author