In this paper, a sequential fusion filter estimator based on event-triggered (ET) multi-sensor nonlinear systems and Cubature Kalman filter, that is ETCKF-SF, is studied, in which the observation noise of each sensor is synchronously correlated, and the observation noise of each sensor is correlated with the process noise at the previous moment. Because the energy of each sensor in the network system is limited, in order to reduce unnecessary sensor energy loss, an ET mechanism is used to reduce the communication rate from the sensor to the fusion center. Sequential fusion filter estimator based on ET mechanism multi-sensor nonlinear systems and innovation analysis is studied, firstly. At the same time, in the design process of the sequential fusion filter, the estimator of the observation noise based on the ET mechanism will also be designed. Secondly, the numerical implementation of the proposed algorithm will be derived based on the third-degree spherical-radial rule Cubature Kalman filter (CKF). Finally, the proposed algorithm is compared with the extended Kalman filter (EKF) based on Taylor series expansion. The effectiveness of the proposed algorithm is verified by the comparison of simulation examples.

This paper presents a study on controlling the out-of-water motion of amphibious multi-rotor UAVs using a cascade control method based on the Active Disturbance Rejection Control (ADRC) algorithm. The aim is to overcome the challenges of time-varying model parameters and complex external disturbances. The research involves developing an underwater dynamic model and analyzing hydrodynamic forces to calculate theoretical inertial hydrodynamic forces and simulate viscous hydrodynamic forces. This establishes the relationship between viscous hydrodynamic forces and exit velocity. A complete air dynamic model is then established, selecting model parameters based on the center of mass position of the amphibious vehicle to enable switching from water to air. To address control algorithm instability caused by changes in model parameters, position and attitude controllers are built using the ADRC algorithm. The control effects are compared with traditional PID and sliding mode controllers (SMC) to verify the effectiveness and superiority of the proposed cascade ADRC control strategy. Experimental results show that our controller has stronger anti-interference than traditional PID and SMC controllers and can overcome control instability caused by changes in model parameters. Our research highlights the importance of using ADRC-based controllers for amphibious multi-rotor UAVs to achieve robust and stable control.

In aeronautical engineering, monitoring and diagnosing intershaft bearings of aeroengine is crucial for flight and life safety. Given that sensor signals are affected by strong noise environments, this study proposes a high-precision multisensor feature fusion fault diagnosis method. The method is based on the lightweight spatial enhancement convolutional module (SConv) with channel shuffling and combined with vision transformer (CSST-Net). First, the original 1-D time-series signals acquired from multiple sensors are converted into 2-D time-frequency images using wavelet transform. The data are then fed into the model by utilizing data layer feature fusion. Subsequently, the interaction of information can be better facilitated by introducing channel shuffling operation into the convolution module. In addition, the spatial enhancement algorithm (SEA) leverages human visual perception properties to extract deeper fault features from the samples. Finally, the global information are extracted by the vision transformer (VIT) to obtain the fault diagnosis results. By validating on aeroengine intershaft bearing and rolling bearing datasets and comparing with other classical and advanced fault diagnosis methods. The results show that the method proposed in this study is not only state-of-the-art but also extremely robust in strong noise environments. Code is available at: https://github.com/DABINB/CSST-Net .