Application of state-variable techniques to optimal feature extraction--- Multichannel analog data

In problems of pattern recognition and signal detection, one of the most important tasks is that of finding practical ways of preprocessing the data to eliminate complexity, redundancy, and irrelevancy. In this paper it is assumed that a vector wavefonn is received during an interval[t_o, t_f]. The waveform is considered to be a sample of a nonstationary vector random process containing a signal process and a noise process consisting of both white and colored noise. The optimum set of weighting functions is found for integrating the received waveform to extract those features that best reveal the presence of the signal. The solution is also shown to be the optimum one for estimating signal strength. A practical scheme for obtaining the optimum weighting functions is derived using state variables, and worked examples are presented.