This signal processing technique uses a correntropy statistical model to filter noisy, weak, or distorted digital signals. Signal processing filters extract meaningful information from signals corrupted with noise, benefitting a variety of fields including digital communications, seismology, and biomedical engineering. In the digital communications field, for example, all cell towers use filters to improve their signal. In the US alone, cell phone companies have invested over $177 billion in cell towers since 2010, with over 300,000 cell towers in 2016. For most forms of noise, nonlinear filters provide optimal signal recovery. The Volterra series approximation is one attempt at creating nonlinear filter solutions, but the solutions it generates are complex and involve numerous coefficients. Other solutions include nonlinearly transforming an input and then computing a regression of the output. These solutions require a considerable amount of computation, making them impractical for real world applications. Researchers at the University of Florida have developed a signal processing technique that uses a statistical model based on nonlinear correntropy to filter digital signals. This processor filters weak or noisy input signals and outputs clear amplified signals.
Nonlinear correntropy filter that improves processing of noisy digital signals
This filter processes digital signals by using a correntropy statistical model. Creation of the correntropy filter involves generating a correntropy statistic based on a kernel function and determining filter weights based on this statistic. The filtering process starts when the processor receives an input that may include multiple, scattered, noisy, distorted, or low intensity signals. The processor then cleans up the input using the nonlinear correntropy filter and generates an output comprising a best-fit prediction of the actual signal without noise or distortions. This output signal is clearer and more amplified.
