Sensor Data Prediction in Missile Flight Tests

Abstract

Sensor data from missile flights are highly valuable, as a test requires considerable resources, but some sensors may be detached or fail to collect data. Remotely acquired missile sensor data are incomplete, and the correlations between the missile data are complex, which results in the prediction of sensor data being difficult. This article proposes a deep learning-based prediction network combined with the wavelet analysis method. The proposed network includes an imputer network and a prediction network. In the imputer network, the data are decomposed using wavelet transform, and the generative adversarial networks assist the decomposed data in reproducing the detailed information. The prediction network consists of long short-term memory with an attention and dilation network for accurate prediction. In the test, the actual sensor data from missile flights were used. For the performance evaluation, the test was conducted from the data with no missing values to the data with five different missing rates. The test results showed that the proposed system predicts the missile sensor most accurately in all cases. In the frequency analysis, the proposed system has similar frequency responses to the actual sensors and showed that the proposed system accurately predicted the sensors in both tendency and frequency aspects.

Model-free control for autonomous prevention of adverse events in robotics

“…To address these difficulties, several steering control methods and novel needle designs have been proposed that accurately control the needle tip to reach desired targets (Hadjerci et al, 2016;Sprang et al, 2016;Li et al, 2017b;Fallahi et al, 2017). There have been efforts to autonomously predict adverse situations and correct for faults using sensor readings in unmanned vehicles (Huang et al, 2022;Ryu et al, 2022); however, much work is still needed for safe autonomous predictions of adverse events in robotic surgery. While reducing targeting errors is almost a solved problem, preventing and controlling adverse events such as needle buckling and tissue displacements before occurrence still remain a significant challenge to be addressed in real time (Leibinger et al, 2016;de Baere et al, 2022).…”