No abstract

Different from traditional aircraft, hypersonic glide vehicles (HGVs) possess stronger maneuverability and a higher flight speed (generally higher than 5 Mach), making trajectory prediction very complicated. Several works have been conducted in this field, which usually analyze the motion characteristics of the HGV first and then use a Kalman filter to track and predict the trajectory. In this way, the accuracy of prediction depends on how to model the control parameters of the target vehicle. The core idea of this paper mainly concerns treating the HGV trajectory prediction as a multivariate time series forecasting problem since HGV trajectories are special multivariate time series with nonperiodic temporal patterns. Moreover, capturing the hidden dependencies between time steps and different time series ensures the accuracy and robustness of predictions. Recently, recurrent neural networks have been widely used in predicting data with temporal patterns between several time steps; however, they fail in capturing nonperiodic temporal patterns. Therefore, we propose a brand-new model named the dual-channel and bidirectional neural network (DCBNN) to intelligently predict the trajectory of hypersonic vehicles in undetectable areas, especially for those with complex maneuver models. DCBNN is constructed with a nonlinear component and a linear component to collect both nonlinear and linear features from input data to improve robustness. Moreover, a dual-branch architecture is utilized in the nonlinear component to capture the complex and mixed dependencies between long-term and short-term patterns. Experiments reveal that the proposed method is effective and intelligent.

In this paper, a novel robust distributed consensus control scheme based on event-triggered adaptive sliding mode control is proposed for multiagent systems with unknown disturbances in a leader-follower framework. First, an adaptive multivariate disturbance observer is utilized to compensate for the disturbance of each agent. Next, a distributed consensus control protocol is constructed via integral sliding mode control, in which a novel adaptive law is designed for the switching gain to overcome the unknown perturbations. An event-triggered strategy is designed to update the control input. Furthermore, the feasibility of the proposed scheme is rigorously analyzed by Lyapunov theory, and a lower bound expression for the inter-event time is derived to guarantee that Zeno behavior can be excluded. The proposed nonlinear consensus algorithm is remarkable in that it does not require any information about the bounds of the disturbances. Finally, compared with existing methods, the proposed algorithm is validated through detailed numerical simulations. In addition, the proposed algorithm is applied to a group of UAVs in this paper, and the results show that it has more application value.

The autonomous navigation of the spacecrafts in High Elliptic Orbit (HEO), Geostationary Earth Orbit (GEO) and Geostationary Transfer Orbit (GTO) based on Global Navigation Satellite System (GNSS) are considered feasible in many studies. With the completion of BeiDou Navigation Satellite System with Global Coverage (BDS-3) in 2020, there are at least 130 satellites providing Position, Navigation, and Timing (PNT) services. In this paper, considering the latest CZ-5(Y3) launch scenario of Shijian-20 GEO spacecraft via Super-Synchronous Transfer Orbit (SSTO) in December 2019, the navigation performance based on the latest BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), Galileo Navigation Satellite System (Galileo) and GLObal NAvigation Satellite System (GLONASS) satellites in 2020 is evaluated, including the number of visible satellites, carrier to noise ratio, Doppler, and Position Dilution of Precision (PDOP). The simulation results show that the GEO/Inclined Geo-Synchronous Orbit (IGSO) navigation satellites of BDS-3 can effectively increase the number of visible satellites and improve the PDOP in the whole launch process of a typical GEO spacecraft, including SSTO and GEO, especially for the GEO spacecraft on the opposite side of Asia-Pacific region. The navigation performance of high orbit spacecrafts based on multi-GNSSs can be significantly improved by the employment of BDS-3. This provides a feasible solution for autonomous navigation of various high orbit spacecrafts, such as SSTO, MEO, GEO, and even Lunar Transfer Orbit (LTO) for the lunar exploration mission.

In this paper, a numerically stable method of signal subspace estimation based on Householder multistage Wiener filter (HMSWF) is proposed. Numerical stability of the method lies on the fact that the Householder matrix in HMSWF ensures the unitary blocking operation and significantly strengthens the orthogonality of basis vectors, especially in the finite-precision implementation. In the following, we analyze the numerical stability of HMSWF and MSWF based on the correlation subtractive structure (CSS-MSWF) by establishing the equivalence between the forward recursion of MSWF and the Arnoldi algorithm in numerical linear algebra. Besides, the equivalence between HMSWF and the Householder QR decomposition (QRD) on the Krylov matrix underlying in MSWF is directly established. Based on the relationship, two theoretical upper bounds of the orthogonality error of basis vectors in signal subspace are obtained and it is demonstrated that the orthogonality of basis vectors based on HMSWF is perfectly preserved by the numerically well-behaved Householder matrix, and the corresponding signal subspace estimation is much more numerically stable than that based on CSS-MSWF. Simulations show the numerical stability of the proposed method of signal subspace estimation by HMSWF. CitationZhuang X B, Cui X W, Lu M Q, et al. Numerically stable method of signal subspace estimation based on multistage Wiener filter.