GAN-FDSR: GAN-Based Fault Detection and System Reconfiguration Method

Abstract

Fault detection and exclusion are essential to ensure the integrity and reliability of the tightly coupled global navigation satellite system (GNSS)/inertial navigation system (INS) integrated navigation system. A fault detection and system reconfiguration scheme based on generative adversarial networks (GAN-FDSR) for tightly coupled systems is proposed in this paper. The chaotic characteristics of pseudo-range data are analyzed, and the raw data are reconstructed in phase space to improve the learning ability of the models for non-linearity. The trained model is used to calculate generation and discrimination scores to construct fault detection functions and detection thresholds while retaining the generated data for subsequent system reconfiguration. The influence of satellites on positioning accuracy of the system under different environments is discussed, and the system reconfiguration scheme is dynamically selected by calculating the relative differential precision of positioning (RDPOP) of the faulty satellites. Simulation experiments are conducted using the field test data to assess fault detection performance and positioning accuracy. The results show that the proposed method greatly improves the detection sensitivity of the system for small-amplitude faults and gradual faults, and effectively reduces the positioning error during faults.

Improved Population Intelligence Algorithm and BP Neural Network for Network Security Posture Prediction

“…A BP neural networkbased posture prediction model was proposed to optimize the model parameters by introducing a simulated annealing algorithm into the crowd search algorithm [17]. Some scholars have also proposed a differential WGAN-based posture prediction method [18]. The method uses a generating adversarial network (GAN) to simulate the development process of posture, introduces Wasserstein distance as the loss function of GAN, and adds a differential term to improve the prediction accuracy of posture values.…”