In the field of aerial photography, UAVs can be used as an ideal carrier of sensors. It is meaningful to plan its coverage path combined with the sensor model. Previous works mainly used decentralized coverage to realize UAV formation cooperative operation, but for the aerial photography scene of a single task, the decentralized coverage algorithm is not effective. In this paper, a centralized coverage algorithm of UAV formation for aerial photography is proposed, which is divided into two sub-problems: coverage path planning and formation control. In the coverage path planning, due to high repetition rate and excessive number of turns of the traditional PRM algorithm, the constraint of the number of turns is introduced, and the back-and-forth mode is used to improve it. In formation control, in order to more accurately evaluate the performance of the coverage path, it refers to the consensus theory and combines the artificial potential field method to guide the reverse-V UAV formation to follow the coverage path. Multi-group simulations show that the proposed algorithm is feasible to implement the concentrated coverage for aerial photography under the comprehensive consideration of path length, number of turns, calculation time, coverage rate and repeating rate.

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

The components detection of a failed satellite is an important work in space on-orbit service. However, the current detection methods for failed satellite components do not consider the effects of low illumination and small targets on the detection accuracy of components at the same time, and most of the datasets used are manually designed. This article proposes a detection method based on image enhancement and an improved Faster R-CNN for small components of a failed satellite in low illumination. Firstly, the dataset of failed satellite components dataset containing low illumination scenarios is established in a simulated real space environment. Secondly, an image enhancement based on reflection model and principal component analysis is proposed, which further enhances images while conserving richer details. Finally, an improved Faster R-CNN for small components is proposed. To improve the detection accuracy of small components, the original Faster R-CNN is improved by modifying three modules: backbone, region proposal network, and RoI pooling layer respectively (i.e., M-HRNet, IoU-balanced sampling, RoI Align). Experimental results show that the proposed method can accurately detect all the small components on satellite capture plane, and the detection performance for low illumination and small components is improved significantly compared to the state-of-the-art methods and original Faster R-CNN.

Objective: This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference. Methods: In contrast to previous state-of-the-art approaches, TFAN does not require any prior knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. TFAN was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent databases (2,099 recordings and 52,180 beats). And further testing of performance was conducted on databases with three levels of increasing difficulty (LEVEL-I, -II and -III). Results: TFAN achieved a superior F1 score for all 12 databases except for `Test-B', with an average of 96.72%, compared to 94.56% for logistic regression hidden semi-Markov model (LR-HSMM) and 94.18% for bidirectional gated recurrent neural network (BiGRNN). Moreover, TFAN achieved an overall F1 score of 99.21%, 94.17%, 91.31% on LEVEL-I, -II and -III databases respectively, compared to 98.37%, 87.56%, 78.46% for LR-HSMM and 99.01%, 92.63%, 88.45% for BiGRNN. Conclusion: TFAN therefore provides a substantial improvement on heart sound segmentation while using less parameters compared to BiGRNN. Significance: The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.

Navigation grade inertial measurement units (IMUs) should be calibrated after Inertial Navigation Systems (INSs) are assembled and be re-calibrated after certain periods of time. The multi-position calibration methods with advantage of not requiring high-precision equipment are widely discussed. However, the existing multi-position calibration methods for IMU are based on the model of linear scale factors. To improve the precision of INS, the nonlinear scale factors should be calibrated accurately. This paper proposes an optimized multi-position calibration method with nonlinear scale factor for IMU, and the optimal calibration motion of IMU has been designed based on the analysis of sensitivity of the cost function to the calibration parameters. Besides, in order to improve the accuracy and robustness of the optimization, an estimation method on initial values is presented to solve the problem of setting initial values for iterative methods. Simulations and experiments show that the proposed method outperforms the calibration method without nonlinear scale factors. The navigation accuracy of INS can be improved by up to 17% in lab conditions and 12% in the moving vehicle experiment, respectively.

High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.