Fault Detection Based on Incremental Locally Linear Embedding for Satellite TX-I

A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental learning,an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-dimensional space.Then,telemetry data of Satellite TX-I are analyzed.Therefore,fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error(SPE)and SPE.Simulation results verify the fault detection scheme.

Satellite anomaly detection based on reconstruction discrepancy theory utilizing a new dual-branch reconstruction model

To enhance the accuracy of deep learning methods based on reconstruction discrepancy in satellite anomaly detection tasks,this study proposes a dual-branch reconstruction model(DBRM)and designs a comprehensive satellite anomaly detection framework around this model.Firstly,we introduce the temporal-channel mixer(TC-Mixer)module,which mainly comprises a self-attention layer for capturing long-range temporal dependencies in telemetry data,and two types of feed-forward networks(FFN)for extract-ing complex patterns in the temporal and channel dimension of telemetry data.This design endows the TC-Mixer module with robust capabilities for extracting complicated dependencies in telemetry data.Secondly,with the TC-Mixer module as the main component,we designed the DBRM.This model utilizes a shared latent representation layer,allowing the regeneration branch and forecasting branch of the DBRM to share most of the feature extraction network architecture.This approach significantly en-hances the model’s regression accuracy while reducing computational complexity.Thirdly,using the DBRM as the core network model,we devised a comprehensive satellite anomaly detection framework.This includes an anomaly criterion that considers the reconstruction discrepancy of both the regeneration and forecasting branches,the peak-over-threshold(POT)method for anomaly thresholding,and the MIC-based feature engineering method,etc.Finally,we conducted comparative experiments with several SOTA anomaly detection algorithms on two public and one private satellite anomaly detection datasets.The experimental results validate the effectiveness and superiority of our proposed method.

Adaptive entry guidance for the Tianwen-1 mission

To meet the requirements of the Tianwen-1 mission,adaptive entry guidance for entry vehicles,with low lift-to-drag ratios,limited control authority,and large initial state bias,was presented.Typically,the entry guidance law is divided into four distinct phases:trim angle-of-attack phase,range control phase,heading alignment phase,and trim-wing deployment phase.In the range control phase,the predictor–corrector guidance algorithm is improved by planning an on-board trajectory based on the Mars Science Laboratory(MSL)entry guidance algorithm.The nominal trajectory was designed and described using a combination of the downrange value and other states,such as drag acceleration and altitude rate.For a large initial state bias,the nominal downrange value was modified onboard by weighing the landing accuracy,control authority,and parachute deployment altitude.The biggest advantage of this approach is that it allows the successful correction of altitude errors and the avoidance of control saturation.An overview of the optimal trajectory design process,including a discussion of the design of the initial flight path angle,relevant event trigger,and transition conditions between the four phases,was also presented.Finally,telemetry data analysis and post-flight assessment results were used to illustrate the adaptive guidance law,create good conditions for subsequent parachute reduction and power reduction processes,and gauge the success of the mission.