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Accuracy estimating of highly noisy signals digital processing using heuristic algorithms

Heuristic algorithms are often used as an alternative when solving problems of high computational complexity or lacking an exact solution, allowing to quickly obtain the desired result. Usually, they do not have a strict mathematical justification, but their application is justified in terms of practicality. Formally, algorithms that use approximate methods can be classified as heuristic. However, when applying them, the problem of determinism lack is often arises, which does not always allow one to evaluate the solution obtained accuracy. The paper considers a methodical approach to assessing the accuracy of heuristic algorithms designed to determine the useful signal shape and parameters on the strong noise component background. It is based on the method of analogy and consists in modeling an artificial signal with given parameters and a background noise interference similar in its characteristics to additive white Gaussian noise. In this case, the noise component is formed by software using a pseudo-random number sequence generator. Such generators are included in the packages of almost all high-level programming languages built-in functions. A comparative analysis of the real and artificial noise characteristics is presented, that shown the problem solving by numerical modeling possibility. The results of accuracy estimation in determining the artificial signal parameters, that is separated from the noise component using piecewise linear approximation and averaging heuristic algorithms, are obtained. The problem of empirical data smoothing with the discrete signal equivalent replacement by a quadratic functions whose parameters provide a piecewise parabolic approximation its shape is also considered. This procedure eliminates the residual signal bounce that inevitably occurs as a result of linearization and allows further recording at any sampling rate. Thus, the proposed approach allows us to quantify the accuracy of heuristic algorithms used in determining the expected signal parameters. Read more...