Micrometeoroid and Orbital Debris Impact Detection and Location Based on FBG Sensor Network Using Combined Artificial Neural Network and Mahalanobis Distance Method

Abstract

Micrometeoroid and orbital debris (MMOD) can cause serious impact damage to long-term on-orbit spacecraft. MMOD impact detection and location are essential to improve the safety and reliability of the on-orbit spacecraft. In this article, a combined artificial neural network (ANN) and Mahalanobis distance method for MMOD impact detection and location based on fiber Bragg grating (FBG) sensor network is proposed. The inputs of the proposed ANN are the Mahalanobis distances determined by the wavelength changes of the FBG sensors at impact points and reference points. A finite element model is developed to explore the relationship between the Mahalanobis distance and the actual distance, which proved to be nonlinear based on the finite element simulation results of MMOD impacts. Four FBG sensors were installed on the back of the test board and sampled at a frequency of 100 Hz using a four-channel FBG demodulator. A total of 360 impact experiments were carried out on this experiment system. The average location error of the proposed method is 0.89 cm, which is 39% and 31% lower compared with the Mahalanobis distance discriminant analysis method and the ANN method, respectively. The combined ANN and Mahalanobis distance method reduces the location errors and has higher accuracy of position prediction.