Two new algorithms for tracking arterial parameters in nonstationary noise conditions

Two new algorithms with reduced sensitivity to the changing environment are applied to tracking arterial circulation parameters. They are variants of the Least-Squares (LS) algorithm with Variable Forgetting factor (LSVF), and of the Constant Forgetting factor-Covariance Modification (CFCM) LS algorithm, devised to overcome their main practical deficiencies related to noise level sensitivity and the high number of design variables, respectively. To this end, adaptive mechanisms are incorporated to estimate observation noise variance in LSVF and the rate of change for the different parameters in CFCM. Specific computer simulation experiments are presented to compare their effectiveness with the original counterparts and to provide guidelines for their optimal tuning at different noise levels. Moreover, algorithm performance degradation, consequent on changes in the noise level compared to that assumed during the tuning phase, is analyzed. In particular, it is shown that, when the noise level changes with respect to the tuning value, the new LSVF algorithm is much more robust than the original one, whose performance degrades rapidly. The new CFCM algorithm is characterized by a reduced number of design variables with respect to its original counterpart. Nevertheless, it can be preferred only when low noise signals are used for estimation.