Method for detecting weak pulse signals under mixed noise interference

Abstract

The invention discloses a method for detecting weak pulse signals. The method includes the following steps that: one-dimensional multi-scale wavelet decomposition is performed on measured signals, namely, the measured signals are decomposed into N layers through one-dimensional wavelet transform, and the high-frequency portions of the signals are reconstructed through utilizing a wavelet decomposition coefficient, so that N high-frequency reconstruction signals dj and one low-frequency reconstruction signal a are obtained; sliding kurtosis processing is performed on the reconstruction signals dj, so that a corresponding sliding kurtosis time sequence cj can be obtained; and superimposing synthesis and normalization processing are performed on the sliding kurtosis time sequence cj, so that a final sliding kurtosis time sequence c can be obtained. After being subjected to the one-dimensional multi-scale wavelet decomposition preprocessing, the measured signals embody different time-frequency domain characteristics in different decomposition scales, and when the sliding kurtosis processing is performed on the signals, a detection effect of multi-slide window synchronization action can be realized, and therefore, differential analysis and processing on noises, interferences and pulses to be detected mixed in measured signals can be benefitted.

Description

technical field [0001] The invention relates to the research field of signal processing methods, in particular to a method for detecting weak pulse signals under mixed noise interference. Background technique [0002] A weak signal usually refers to an extremely weak useful signal buried deep in noise interference. Weakness here mainly means that the energy or amplitude of the useful signal is submerged in the noise interference due to its small energy or amplitude relative to the noise interference. At present, weak signal processing technology has been widely used in weak periodic signal detection in communication engineering, electronic countermeasures, biomedicine and other fields, and has achieved remarkable results. In the field of practical engineering applications, there are still a large number of pulse signals to be detected, such as pulse echo signals, discharge pulse signals, vibration shock signals, and ultrasonic pulse signals generated in engineering applicati...

AI Technical Summary

Problems solved by technology

However, in practical applications, it is found that the background noise interference has a great influence on the determination of the optimal sliding window width, and different types, or even the same type of noise interference signals with different strengths, will affect the calculation results of the optimal sliding window width. Thus directly affecting the weak pulse signal detection results

Therefore, the detection performance of the existing sliding kurtosis algorithm for weak pulse signals will be significantly degraded under the interference of mixed noise, and the expected detection effect cannot be achieved.

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