Angular accelerometer signal adaptive denoising method based on wavelet analysis

Abstract

The present invention provides an angular accelerometer signal adaptive denoising method based on wavelet analysis. Compared with a conventional wavelet analysis denoising method, the method of the present invention comprises the steps of calculating the difference quotient of the wavelet energy entropies and the energy entropies of a signal with noise and a noisy signal to determine an optimal wavelet decomposition level of the signal with noise, applying the median filtering to evaluate a noise width and a noise standard deviation, and combining a 3 sigma criterion to set a threshold value to thereby carry out the adaptive filtering. At the aspect of the threshold value selection, the method has better effects by being compared with a conventional heuristic sure criterion, a maximum and minimum value criterion and a penalty criterion. The above methods have better effects on the denoising processing of a periodic signal and a high-dynamic signal of a molecule type liquid-ring angular accelerometer.

Description

technical field [0001] The invention relates to a wavelet analysis-based adaptive denoising method for angular accelerometer signals, which belongs to the technical field of signal processing. Background technique [0002] Angular motion widely exists in various fields of sea, land, air and sky, and is one of the most basic motions in nature. In terms of the characterization of the dynamic characteristics of angular motion, angular acceleration can characterize the high-order characteristics of angular motion more directly, faster and more accurately. With the wide application of high and new technology in the military field, future warfare will face joint operations with all-round, high-intensity, and multi-category weapons. Therefore, under complex working conditions such as multiple disturbances, variable loads, and large overloads, measuring angular acceleration signals with high precision, high dynamics, and high reliability can improve the comprehensive combat effecti...

AI Technical Summary

Problems solved by technology

For the molecular liquid ring angular accelerometer, there is no clear system model in China, so the Kalman filter algorithm cannot be used for signal processing

However, using the commonly used mean and median filtering algorithms, even if the sliding window is adjusted, it is impossible to smooth the noise and preserve the local high-frequency signal at the same time, and the degree of adaptive denoising is low, resulting in a decrease in signal reliability.

It can be seen that no effective solution has been proposed for the use of molecular liquid ring angular accelerometers in complex environments and the processing of high dynamic signals.

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