As a typical safety-critical system, satellite has extremely stringent requirements for reliability. However, some satellite components, especially attitude sensors, are prone to suffer from performance degradation or even drastic failure in space environment, which leads to serious threats to satellite. A series of fault diagnosis methods have been investigated to diagnose these failures. However, due to the increasingly complex structure and various operating conditions of satellite, it is still a challenging task to diagnose attitude sensor failures robustly and reliably. In addition, sophisticated diagnosis methods result in heavy calculations, which may not satisfy satellite autonomy requirements. Given these issues, this article proposes an integrated fault detection, isolation, and reconstruction (FDIR) scheme for attitude sensors, such as fiber-optic gyroscopes (FOGs) and star sensor, using an adaptive hybrid method. The parity equation (PE) approach is applied to detect gyros fault. On the basis of attitude kinematics model and sensor measurement equation, a novel adaptive extended Kalman filter (EKF) is developed simultaneously. Then, the chi-square test is established using an innovation sequence for detecting sensor fault. According to fault detection results, different reconstruction strategies are presented by autonomously tuning noise covariance parameters, which will be used to reconstruct the faults of gyros and star sensor. Combined with the PE approach and chi-square test, faults in gyroscopes and star sensor can be detected reliably. Then, using this adaptive algorithm, these failures can be isolated and reconstructed accurately. Finally, the effectiveness of the presented method is verified by simulating typical faults of gyros and star sensor.

The verticality of the borehole determines the firmness of the superstructure, so it is very important to measure the verticality of the borehole. However, the existing static manual measurement technology has low efficiency and poor accuracy. In order to realize dynamic measurement, reduce cost and improve measurement accuracy, this paper proposes the error mutual correction method for low-cost IMU according to the working characteristics of dynamic and static combination of drill pipe, and establishes an error online compensation model and verticality measurement model. The mutual compensation method of gyroscope and accelerometer errors suppresses the accumulation of measurement errors. In addition, the pile digger works in a complex environment, and a verticality measurement sensor that is waterproof, vibration-proof, high-temperature-proof, with online solution and wireless transmission is designed to improve the reliability of the device. Experiments show that the mutual correction verticality measurement method in this paper can achieve a measurement accuracy of 7% (3σ).

Remote sensing image object detection has been studied by many researchers in recent years using deep neural networks. However, optical remote sensing images contain many scenes with small and dense objects, resulting in a high rate of misrecognition. Firstly, in this work we selected a deep layer aggregation network with updated deformable convolution layers as the backbone to extract object features. The detection and classification of objects was based on the center-point network without non-maximum suppression. Secondly, the dynamic gradient adjustment embedded into the classification loss function was put forward to harmonize the quantity imbalance between easy and hard examples, as well as between positive and negative examples. Furthermore, the complete intersection over union (CIoU) loss function was selected as the objective function of bounding box regression, which achieves better convergence speed and accuracy. Finally, in order to validate the effectiveness and precision of the dynamic gradient adjustment network (DGANet), we conducted a series of experiments in remote sensing public datasets UCAS-AOD and LEVIR. The comparison experiments demonstrate that the DGANet achieves a more accurate detection result in optical remote sensing images.

A novel fiber-optic distributed acoustic sensor (DAS) utilizing a LiNbO₃ straight through waveguide phase modulator as phase generation carrier (PGC) modulation module for the detection of acoustic signal is presented. The sensitive principle and the phase demodulation method of the system based on phase-sensitive optical time domain reflectometer (Φ-OTDR) are described. This scheme solves the problems of low modulation frequency and unstable performance of piezoelectric transducer (PZT) in the traditional homodyne detection system and depends only on the pulse repetition frequency. The efficacy of the new approach is demonstrated experimentally, showing that the weak acoustic signal can be demodulated accurately. The noise level of the system is < 4.2×10⁻³ rad/√Hz, the signal to noise ratio (SNR) is > 16 dB, and the spatial resolution is 10 m, as well as a detection frequency can theoretically achieve 25 kHz at 2 km sensing fiber. It provides a new research idea for DAS and is expected to replace PZT to achieve a high-frequency response, which has good potential in the applications of low cost, long distance and high frequency detection.