Stopping and Restarting Adaptive Updates to Recursive Least-Squares Lattice Adaptive Filtering Algorithms

This paper reports several observations about stopping and restarting adaptive updates to recursive least-squares lattice (LSL) adaptive filtering algorithms. When updates are stopped, the adaptive filter becomes a fixed filter. Simulation examples demonstrate that large output error results from abruptly stopping or restarting adaptive updates. A remedy to the problem is to transition the adaptive updates to an off or on state gradually by driving the unknown system and the adaptive filter simultaneously to the all zero state. This is accomplished by setting the input signal to zero. The length (in number of samples) of the transition period is equal to the length of the adaptive filter. Simulation examples are given to illustrate the problem and the effectiveness of the proposed remedy