An SM-WRLS algorithm with an efficient test for innovation: simulation studies and complexity issues

A strategy is developed which can be applied to any version, adaptive or non-adaptive, of the set membership weighted recursive least squares (SM-WRLS) algorithm to improve the computational efficiency. A significant reduction in computational complexity can be achieved by employing a suboptimal test for information content in the incoming data. The main issue is to avoid the computations of an O(m ²) checking procedure, where m is the number of parameters to be estimated, which is required to check for the existence of useful data. Since most of the time these computations result in the rejection of incoming data, a more efficient test which reduces the complexity of the algorithm to O(m) is presented