Electrical cross coupling is regarded as a major obstacle to achieving high-performance miniature fiber-optic gyroscopes (FOGs), because it can cause dead bands, which are critical errors in FOGs. Using a differential photodiode amplifier has proven to be effective in rejecting coupled interference. However, the conventional three-op-amp instrumentation amplifier cannot provide a miniature FOG’s bandwidth requirements, because of the large photodiode capacitance and parasitic capacitance. We present a high-performance, fully differential photodiode amplifier, where the bandwidth limitations are removed by applying a reverse bias to the photodiode and replacing the feedback resistor with a modified tee-network and a DC cancellation loop. For an experimental FOG with a 300 m fiber coil, we demonstrate a fully differential photodiode amplifier with 880 kΩ gain and 3.5 MHz bandwidth. In the FOG performance test, it not only reduces the angular random walk and bias drift, but also eliminates the approximately 1°/h dead band observed in the same FOG using a PINFET receiver, demonstrating its effectiveness in suppressing coupled interference.

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 geomagnetic aided inertial navigation algorithm has become one of the current research hotspots in the field of aircraft. It can overcome the shortcomings of pure inertial navigation error accumulation over time, and achieve long-term, autonomous, and all-weather navigation. However, the traditional iterative closest contour point (ICCP) algorithm requires high sensor performance, has poor tolerance to noise, and has a complex algorithm with poor real-time performance. The timeliness of the traditional magnetic contour matching (MAGCOM) algorithm is related to the search area, and the matching search area is fixed, and the algorithm is inflexible. To address the above problems, this paper proposes a geomagnetic navigation method based on coarse estimation of navigation error and search area adaption. When the vehicle flies into the geomagnetic database region, the navigation error is roughly estimated based on the error characteristics of the inertial navigation system. Considering the influence of noise and uncertainty, three times the value of the estimated error is used as the edge length of the geomagnetic matching search area, and geomagnetic matching is performed for positioning. The product of velocity and time provided by the inertial navigation system is used as a constraint to determine whether the geomagnetic matching results are abnormal. If multiple consecutive geomagnetic matching results are normal, the navigation system automatically reduces the matching search area and search step to improve the matching accuracy and timeliness. Simulation experiments show that the accuracy of the adaptive search area geomagnetic matching method proposed in this paper can reach within 200 meters.

Aiming at the inability to achieve high-precision real-time magnetic heading angle estimation under single degree of freedom rotation, a novel attitude measurement while drilling system is designed, which consists of a single axis fiber optic gyroscope, a tri-axial MEMS accelerometer and a tri-axial magnetometer. The magnetometer bias calibration model based on uniform rotation is established and verification experiments are carried out. The uniform rotation experiment shows that six calibration results fluctuate within 5% of the mean. A real-time estimation model of the magnetic heading angle under random rotation is established, of which the theoretical error is less than 0.38°. Three real-time estimation experiments of magnetic heading angle are carried out, and the overall estimation error did not exceed 0.35°, and the average estimation error did not exceed 0.3°. Under continuous rotation of large angle, the bias estimation of the X-axis and Y-axis magnetometers can be achieved. The results show that the estimated value of the bias of the magnetometer under dynamic conditions can provide a reference for the initial magnetic heading angle measurement under static conditions. The experimental results prove the effectiveness of the proposed method, which provides a reference for the further improvement of the attitude measurement while drilling system.

In order to study the Neutron induced single-event upsets (SEU) in SRAM-based FPGAs (SFPGAs), experiments have been conducted for validating its sensitivity. To increase the reliability of SFPGA designs in the presence of Neutron induced SEU, an analytical approach based on probabilistic transfer matrix (PTM) is introduced to estimate the relative reliability of designs mapped into SFPGAs. Moreover, the proposed method is able to obtain the error sensitivity rating of basic nodes in SFPGAs. Software is designed to implement the mitigation method. Results of neutron radiation experiment on a digital filter circuit design illustrate that impact induced by the neutron cannot be ignored, and partial triple modular redundancy (TMR) on the sensitive sub-circuits indicated by the proposed analysis method is effective for SEU mitigation with less area overhead than that of full TMR.

Image registration is a basic task in computer vision, for its wide potential applications in image stitching, stereo vision, motion estimation, and etc. Most current methods achieve image registration by estimating a global homography matrix between candidate images with point-feature-based matching or direct prediction. However, as real-world 3D scenes have point-variant photograph distances (depth), a unified homography matrix is not sufficient to depict the specific pixel-wise relations between two images. Some researchers try to alleviate this problem by predicting multiple homography matrixes for different patches or segmentation areas in images; in this paper, we tackle this problem with further refinement, i.e. matching images with pixel-wise, depth-aware homography estimation. Firstly, we construct an efficient convolutional network, the DPH-Net , to predict the essential parameters causing image deviation, the rotation ( $R$ ) and translation ( $T$ ) of cameras. Then, we feed-in an image depth map for the calculation of initial pixel-wise homography matrixes, which are refined with an online optimization scheme. Finally, with the estimated pixel-specific homography parameters, pixel correspondences between candidate images can be easily computed for registration. Compared with state-of-the-art image registration algorithms, the proposed DPH-Net has the highest performance of 0.912 EPE and 0.977 SSIM, demonstrating the effectiveness of adding depth information and estimating pixel-wise homography into the image registration process.