Measuring node importance in complex networks has great theoretical and practical significance for network stability and robustness. A variety of network centrality criteria have been presented to address this problem, but each of them focuses only on certain aspects and results in loss of information. Therefore, this paper proposes a relatively comprehensive and effective method to evaluate node importance in complex networks using a multicriteria decision‐making method. This method not only takes into account degree centrality, closeness centrality, and betweenness centrality, but also uses an entropy weighting method to calculate the weight of each criterion, which can overcome the influence of the subjective factor. To illustrate the effectiveness and feasibility of the proposed method, four experiments were conducted to rank node importance on four real networks. The experimental results showed that the proposed method can rank node importance more comprehensively and accurately than a single centrality criterion.

The strong non‐linear relationship between the range rate and the target state can be introduced using the range rate to track a target. A linear measurement equation can be constructed based on the geometrical relationship between the range rate and the velocity components. Then, the linear Kalman filtering (KF) algorithm can be used. To improve the performance of the converted measurement method, a novel multiplicative unbiased converted measurement KF algorithm with range rate (UCMKF‐R) is developed. To eliminate the conversion bias, one‐step prediction estimation is used to replace the position measurement to calculate the converted measurement error covariance in the UCMKF‐R algorithm, which removes the correlation between the converted measurement error covariance and the measurement noise. Thus, a Decorrelated UCMKF‐R (DUCMKF‐R) is proposed. The experimental results show that the measurement conversion of the DUCMKF‐R algorithm is unbiased, consistent and has an estimation bias that is close to zero. The proposed UCMKF‐R and DUCMKF‐R algorithms are compared with the state‐of‐the‐art approaches, namely, the Sequential Extended KF algorithm, the Sequential Unscented KF algorithm, and the Converted Measurement KF with Range Rate algorithm. The experimental results show that the proposed algorithms have good performance.

The problem of maneuvering target tracking is a hot issue in the field of target tracking. Due to the range rate measurement containing the maneuvering information of target, it has the important practical significance to study how to use the range rate measurement to improve the effect of maneuvering target tracking. In the framework of interacting multiple model algorithm, the range rate measurement is used to update target state estimate and the probability of motion model to improve the tracking performance. As the measurement equation including the range rate measurement is strongly nonlinear, square root cubature Kalman filter algorithm is selected as the filter in interacting multiple model algorithm. The normal acceleration is deduced from the range rate with the reality constraint. And through Monte Carlo simulation, the empirical distribution functions of the normal acceleration statistics corresponding to different motion models are obtained. Their approximate distribution functions are obtained by the use of the expectation maximization algorithm with Gaussian mixture model. Then the probability distribution and probability distribution of measurement prediction residual are combined into a new likelihood function to improve the efficiency of updating the model probability. The experimental results show that the interacting multiple model algorithm proposed in this article has the smaller root mean square error of position and velocity and has the smaller average Kullback-Leibler divergence of model probability during the motion model stable phase.

With the widespread application of unmanned aerial vehicles (UAVs) in various fields, anomaly detection in flight data has become increasingly important. However, temporal variations and temporal correlations in flight data challenging to achieve accurate anomaly detection. To address these issues, the multiscale Transformers with contrastive learning for UAV anomaly detection (MTCL-UAV) is proposed to achieve adaptive multiscale modeling. The model is based on the mixture-of-experts (MoEs) architecture, and each MoE block includes a router, expert networks (ENs), and an aggregator. The router performs a temporal decomposition to select optimal scales for each sample, and the aggregator fuses the outputs of the ENs into an integrated representation. To learn the temporal correlations of flight data and improve the model’s representation, a dual attention mechanism enhanced by contrastive learning (CL-DAM) is introduced, which captures not only intrapatch and interpatch correlation relationships but also neighborhood relationships among patches. Experiments on a real flight dataset demonstrate that MTCL-UAV not only achieves superior performance compared to other methods but also exhibits strong robustness. The code is available at https://github.com/SteelHu/MTCL-UAV

In order to reveal the mechanism of Category II rotor-body-slung-load coupled oscillation (RBSLCO) with the frequency range of 2.5~8 Hz, a novel nonlinear rigid-elastic coupled model is presented for the helicopter and slung load system (HSLS) with explicit formulation. The slung load system model is coupled with the current rigid-elastic coupled helicopter model, considering fuselage hook point rigid-elastic coupled movements, cable stretching, and hook point force from the slung load system. The results show that carrying the heaviest load is the vital state for Category II RBSLCO. As slung load mass ratio increases, rotor-fuselage coupling becomes stronger and the oscillation frequency shifts slightly, causing a maximum of 15% reduction in stability margin. In addition, even when the load is lightweight, another form of Category II RBSLCO may appear involving fuselage bending and cable stretching. This Category II RBSLCO behaves like the vertical bouncing but is divided into a high-frequency anti-phase oscillation and a relatively low-frequency in-phase oscillation.