In order to improve the ability of rotating turntable to resist the external disturbance in the single-axis rotation modulation inertial navigation system (SRMINS) and ensure smooth running of the turntable, we designed a dual closed-loop controller. The conventional proportional-integral-derivative (PID) algorithm was employed in the position loop, and a tracking differentiator based fuzzy PID algorithm was proposed and used in the speed loop, to realize the steady speed control of continuous rotation in the SRMINS. The introduction of the tracking differentiator (TD) guarantees the reliability of the differential signal extraction. Furthermore, the combination of the TD and the fuzzy adaptive PID controller leads to the smooth motion of the turntable without overshoot. The robustness and better stability margin of the system can also be achieved while the static error is eliminated and the performance of fuzzy control is improved. Simulation results demonstrate that the system has many merits such as fast response, no overshoot, and better robustness. It is also verified that regulating the PID parameters in real time by the fuzzy control rules effectively improves the dynamic and static performance of the system, and the requirement on stable rotation speed of the SRMINS is satisfied.

Over the last few decades, the decomposition-based multiobjective evolutionary algorithms (DMOEAs) have became one of the mainstreams for multiobjective optimization. However, there is not too much research on applying DMOEAs to uncertain problems until now. Usually, the uncertainty is modeled as additive noise in the objective space, which is the case this paper concentrates on. This paper first carries out experiments to examine the impact of noisy environments on DMOEAs. Then, four noise-handling techniques based upon the analyses of empirical results are proposed. First, a Pareto-based nadir point estimation strategy is put forward to provide a good normalization of each objective. Next, we introduce two adaptive sampling strategies that vary the number of samples used per solution based on the differences among neighboring solutions and their variance to control the tradeoff between exploration and exploitation. Finally, a mixed objective evaluation strategy and a mixed repair mechanism are proposed to alleviate the effects of noise and remedy the loss of diversity in the decision space, respectively. These features are embedded in two popular DMOEAs (i.e., MOEA/D and DMOEA-εC), and DMOEAs with these features are called noise-tolerant DMOEAs (NT-DMOEAs). NT-DMOEAs are compared with their various variants and four noise-tolerant multiobjective algorithms, including the improved NSGA-II, the classical algorithm Bayesian (1+1)-ES (BES), and the state-of-the-art algorithms MOP-EA and rolling tide evolutionary algorithm to show the superiority of proposed features on 17 benchmark problems with different strength levels of noise. Experimental studies demonstrate that two NT-DMOEAs, especially NT-DMOEA-εC, show remarkable advantages over competitors in the majority of test instances.

Performing long-duration navigation without the global navigation satellite system (GNSS) network is a challenging task, particularly for small unmanned aerial vehicles (UAVs) equipped with low-cost micro-electro-mechanical sensors. This study proposes a hybrid neural network that integrates self-attention mechanisms with long short-term memory (SALSTM) to enhance GNSS-denied navigation performance. The estimation task of GNSS-denied navigation is first modeled based on UAV aerodynamics and kinematics, enabling a precise definition of the inputs and outputs that SALSTM needs to map. A self-attention layer is inserted in multiple LSTM layers to capture long-range dependencies in subtle dynamic changes. The output layer is designed to generate state sequences, leveraging the recursive nature of LSTM to enforce state continuity constraints. The outputs of SALSTM are fused to enhance integrated navigation within an extended Kalman filter framework. The performance of the proposed method is evaluated using flight data obtained from field tests. The results demonstrate that SALSTM-enhanced integrated navigation achieves superior long-term stability and improves velocity and position estimation accuracy by more than 50% compared to the best existing methods.

Accurate vulnerable part detection based on full target detection results shows great importance in improving the damage effectiveness of the military drone. However, traditional object detection methods have difficulty in handling inaccurate full target bounding boxes and fail to model the semantic relationships between various class full targets and their key parts, resulting in low localization accuracy. The proposed approach includes a classwise feature recalibration module, which effectively models the dependencies between the prior knowledge obtained from the full target detector and the location of the key part. Additionally, an optimized spatial transformation module is designed to preprocess the input image and eliminate interfering objects. Furthermore, a carefully constructed loss function is employed, linking the classification branch with the regression branch, thereby emphasizing the importance of localization accuracy. Our proposed model surpasses the performance of existing state-of-the-art models, demonstrating a significant advantage with maximum improvements of +24.9%, +30.2%, and +28.3% in mean average precision on the standard test set, generalized test set, and real-world dataset, respectively. The effectiveness and robustness are also confirmed through extensive ablation studies.

Swarm intelligence for uncrewed aerial vehicles (UAVs) significantly improves the success rate of executing intricate tasks using “distributed platforms and aggregated effects”. However, the high experimental costs and safety risks constrain its development. This paper introduces SIGMA (Swarm Intelligence Generic simulator for Multi-UAVs), a high-fidelity distributed UAV swarm simulator for swarm intelligence algorithms. As an agent-based modeling simulator (ABMS), SIGMA has three key innovations: First, an automatic model tuning method improves aircraft dynamics fidelity. Second, a bidirectional discrete-event simulation (BiDES) architecture resolves the time alignment challenges in distributed systems. Third, a multi-agent learning toolbox ensures algorithm compatibility via an episodic training structure and a memory replay mechanism. In the verification part, the fidelity and scalability of the simulator are verified by quantitative simulations and experiments, and several successful applications demonstrate the practicality of the proposed simulator. Notice to Practitioners—The motivation for this paper stems from the need to develop a scalable and high-fidelity simulator for practical applications of swarm intelligence algorithms. Simulators developed based on game engines are widely used in swarm robotics due to their realistic 3D environments. However, as the number of nodes increases, the real-time performance and scalability of the simulators will decrease significantly. To achieve real-time simulation of large-scale swarms and improve swarm fidelity, SIGMA is proposed. It uses ABMS technology to achieve better performance and can effectively fit the learning tasks of UAV swarms.

The accurate estimation of flow angles is crucial for enhancing flight performance and aircraft safety. Flow angles of fixed-wing small unmanned aerial vehicles (SUAVs) are more vulnerable due to their low airspeed. Current flow angle measurement devices have not been widely implemented in SUAVs due to their substantial cost and size constraints. Moreover, there are no general estimation methods suitable for SUAVs based on their rudimentary sensor suite. This study presents a generalized optimization-assisted filter estimation (OAFE) method for estimating the relative velocity and flow angles of fixed-wing SUAVs based on a standard sensor suite. This OAFE method mainly consists of a cubature Kalman filter and an optimizer. The filter serves as the main loop with which to generate flow angles in real time by fusing the acceleration, angular rate, attitude, and airspeed. Without flow angle measurements, the optimizer generates approximate aerodynamic derivatives, which serve as pseudo-measurements with which to refine the performance of the filter. The results demonstrate that the estimated angle of attack and side slip angle displayed root mean square errors of around 0.11° and 0.24° in the simulation. The feasibility was also verified in field tests. The OAFE method does not require flow angle measurements, the prior acquisition of aerodynamic parameters, or model training, making it suitable for quick deployment on different SUAVs.