Highly adaptive time delay target signal automatic extraction method

Abstract

The invention discloses a highly adaptive time delay target signal automatic extraction method, which designs general atoms, does not need to construct a huge redundant dictionary in advance during sparse decomposition, sets atomic characteristic parameters as bat individual position vectors, and constructs flexible atoms by using an improved bat algorithm to perform optimal position search, and further achieves standardized optimal matching atom search; a sparse decomposition dual-threshold iteration stop criterion is adopted, wherein a residual l-2 norm threshold is adaptively determined by background noise intensity, namely, a l-2 norm not containing a target signal sequence ns; the iteration number threshold value is set by the user according to the real-time requirement. The application of double criteria can ensure the signal extraction precision, improve the iteration speed and effectively avoid excessive decomposition. The method does not depend on complex prior knowledge and subsequent manual judgment and screening, other components of the signal can be reserved to the maximum extent while the target signal is effectively extracted, and the method is a fully-automatic non-stationary time delay signal extraction method.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a method for automatically identifying and extracting time-delayed target signals in background noise. Background technique [0002] Non-stationary time-delay target signal extraction is an important research content of modern signal processing, and has important applications in vibration signal detection, earthquake monitoring, biomedical signal detection and other fields. [0003] The existing signal extraction methods mainly include: wavelet decomposition, Hilbert-Huang transform, variational mode decomposition (VMD) and signal sparse decomposition algorithm. [0004] Wavelet decomposition is an important means to deal with time-delayed signals, but it is necessary to choose the appropriate wavelet basis function and the number of decomposition layers, otherwise the extracted signal may not be the target signal. The Hilbert-Huang transform has a good effect on the e...

AI Technical Summary

Problems solved by technology

However, the traditional signal sparse decomposition algorithm needs to perform matching pursuit in the pre-built redundant dictionary to determine the optimal matching atom, and the algorithm efficiency is not high enough.

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