Tao Chao

A novel fixed-time cooperative guidance law is developed to enable multiple flight vehicles to simultaneously intercept various target motions with desired impact angle, including stationary, constant-velocity moving and manoeuvring targets, with a unified guidance structure that requires no mode switching or target motion classification. First, a fixed-time distributed cooperative guidance law is developed using the theory of multi-intelligence cooperative control, which is formulated along the line of sight (LOS) direction. This approach ensures that the impact time is regulated, enabling multiple flight vehicles to intercept the target simultaneously within a fixed time. In the second phase, employing a sliding manifold and a fixed-time reaching law, the normal acceleration of each flight vehicle is designed to ensure that the LOS angle converges to its target value within a fixed duration. Unknown components of the target’s acceleration are estimated via fixed-time observers and incorporated into the guidance commands, enhancing precision and robustness in the guidance process. In conclusion, the proposed approach proves the effectiveness and superiority through numerical simulations, including various target motions, switching communication topology, robustness against uncertainties based on Monte Carlo experiment and comparative studies.

In this paper, a sliding mode guidance law for impact angle control without violating a seeker’s field-of-view limits is proposed against targets with various motions and unknown acceleration, including stationary, constant-velocity moving and manoeuvring targets. To develop the guidance law, the kinematic conditions for engagement geometry are defined, and the sliding mode control is applied to satisfy the homing constraint and impact angle control. Then, the relation between look angle, the desired line-of-sight angle and desired impact angle is established to guarantee the target in the field of the missile’s view. The stability of the proposed approach is analysed using Lyapunov theory. Furthermore, the look angle is examined to verify the field-of-view constraint, and a capturability analysis is conducted. To evaluate the performance of the proposed law, numerical simulations demonstrate that the proposed approach achieves satisfactory miss distance and impact angle error while adhering to the field-of-view limit.

A cooperative guidance law is proposed in a two-on-two engagement scenario with large-heading-errors by choosing zero-effort miss distance as a sliding surface, which consists of an attacker, a protector, a defender, and a target, based on fixed-time sliding mode control theory. Based on the nonlinear method of fixed-time sliding mode control, the performance of the cooperative guidance law remains satisfactory even with large-heading-errors scenarios where the linearization-based approaches might be invalid. By virtue of this law, the attacker pursues the target with the assistance of the protector, which can intercept the defender in the engagement scenario. Further-more, if the attacker is intercepted by the defender, the guidance law of the protector could guarantee that the protector attacks the target. A robust adaptive term is included in the guidance law to deal with the case of the unknown disturbance upper bound of the defender-target team. Finally, the feasibility of the guidance law is verified by nonlinear numerical simulations, and the superiority of it is illustrated by comparing with the linearization guidance law.

State estimation is widely used in practical engineering. However, the observation obtained by sensors may be corrupted by abnormal injected data, abnormal measurement noise, and random observation loss. Modeling uncertainty may lead to unknown process noise covariance matrices (PNCMs). The standard Cubature Kalman Filter (CKF) is poorly robust to abnormal observation and model uncertainty. Other advanced methods do not simultaneously consider that PNCM is unknown and observations contain abnormal injected data, abnormal measurement noise, and observation loss. Therefore, this paper presents an improved robust CKF using the variational Bayesian (VB) method. Especially, this paper models the observation model as a mixture of two Gaussian sub-models. One Gaussian distribution model with unknown mean and precision is used to model observation to improve the robustness for abnormal injected data, and another Gaussian distribution with variance that can be adaptively adjusted is used to model observation when measurement noise may be abnormal. In addition, the maximum likelihood estimation method is used to determine whether the observation is missing. The observation loss will be supplemented, and the introduced errors will be compensated by adjusting the parameters of the observation model. The VB method is used to estimate states and unknown parameters in real-time. Finally, simulation and indoor target-tracking experiments show that our method performs better than other methods in accuracy.

Due to the complementarity between LiDAR point clouds and images, fusing LiDAR data can significantly enhance the accuracy and robustness of visual simultaneous localization and mapping (SLAM) systems. Cameras are smaller and less expensive than LiDAR, making them easier to deploy. Building on these properties, we propose a novel depth map-assisted monocular visual-inertial odometry (DM-VIO) that fuses image and LiDAR data through the reuse of prior LiDAR depth maps. Existing monocular visual SLAM methods often suffer from inaccurate depth estimation, while many LiDAR-based depth estimation methods fail to fully leverage depth variation. To address this, we propose a depth estimation method based on radial basis function (RBF) interpolation. Local point clouds, extracted from the prior depth map near visual features, are projected onto two principal directions using principal component analysis (PCA). The compressed point clouds are then used to construct interpolation functions to estimate the depth of visual features. Additionally, we introduce a visual-LiDAR fusion constraint method. This method uses semantic information from images to track associations among LiDAR point clouds projected onto images. Experimental results demonstrate that the proposed method significantly improves the accuracy and robustness of monocular visual-inertial odometry, especially in challenging environments such as low-light and large-scale outdoor scenarios.