Conceptual tools for signal processing systems analysis

Signal processing is the extraction of relevant data by the exploitation of external information or postulates regarding: the input signal, its noise and interference environment, and the nature and intended use of the output data. Successful implementations do not depend primarily on advanced mathematical techniques and powerful processors. Only a clear visualization of the input scenario and of the output application can reveal those features of the signal and interference, and that a priori information can be exploited to enhance the output function. The key challenge is the strategic decision of what to do, not the tactic of how to do it. The author gives a simple introduction to some of the pertinent strategic principles. The author discusses signal space and state space, feature matching, weighted coherent signal summation, application dependent weighting, and limiting conditions