Algorithms for real time trend detection

FIR (finite-impulse response) median hybrid (FMH)-filter-based algorithms for real-time detection are developed. The algorithms are designed so that trends are gradually refined as data become available. Special attention is paid to the detection of sharp edges in trends. The algorithms are based on five- or three-point median operations taken over the outputs of linear subfilters or some other auxiliary outputs. The noise attenuation and the edge preserving abilities of several FMH trend-detection filters are analyzed. The results of the detection show that the in-place growing FMH-filter-based trend detector has significant advantages over the other methods. The trend filtering concept can also be successfully applied to the filtering of the beginning and end of a finite-length data sequence.<<ETX>>