# Ultrasonic Signal Processing Method for Dynamic Burning Rate Measurement Based on Improved Wavelet Thresholding and Extreme Value Feature Fitting

## TL;DR

This paper introduces a new ultrasonic signal processing method to accurately measure the burning rate of solid rocket fuel in noisy environments.

## Contribution

A novel adaptive thresholding and extreme value fitting method is proposed for improved ultrasonic signal processing in rocket fuel burning rate measurement.

## Key findings

- The proposed method achieves a 10 dB SNR improvement at −20 dB with a 0.83 correlation coefficient.
- Echo localization error remains below 1 μs across 12 SNR levels.
- Hardware-in-the-loop experiments show an SNR increase from −15 dB to 5.78 dB with low displacement and rate errors.

## Abstract

Ultrasonic measurement techniques are increasingly used to measure the burning rates of solid rocket fuel, but challenges arise due to noise and signal attenuation caused by the motor’s multi-layered structure. This paper proposes an adaptive thresholding method combined with a wavelet threshold function for effective ultrasonic signal denoising. Additionally, an extreme value feature fitting algorithm is introduced for accurate echo signal localization, even in low signal-to-noise ratio (SNR) conditions. Numerical simulations show a 10 dB improvement in SNR at −20 dB, with a correlation coefficient of 0.83 between the denoised and true signals. Echo localization tests across 12 SNR levels demonstrate a consistent error below 1 μs. Compared to other algorithms, the proposed method achieves higher precision, with a maximum displacement error of 0.74 mm. Hardware-in-the-loop experiments show an increase in SNR from −15 dB to 5.78 dB, with maximum displacement and rate errors of 0.9239 mm and 0.781 mm/s. In fuel-burning experiments, the burning rate curve closely matches the theoretical curve, with an initial fuel thickness error of only 0.12 mm, confirming the method’s effectiveness in complex environments.