For the underwater long baseline (LBL) positioning systems, the traditional distance intersection algorithm simplifies the sound speed to a constant, and calculates the underwater target position parameters with a nonlinear iteration. However, due to the complex underwater environment, the sound speed changes with time and space, and then the acoustic propagation path is actually a curve, which inevitably causes some errors to the traditional distance intersection positioning algorithm. To reduce the position error caused by the uncertain underwater sound speed, a new time of arrival (TOA) intersection underwater positioning algorithm of LBL system is proposed. Firstly, combined with the vertical layered model of the underwater sound speed, an implicit positioning model of TOA intersection is constructed through the constant gradient acoustic ray tracing. And then an optimization function based on the overall TOA residual square sum is advanced to solve the position parameters for the underwater target. Moreover, the parti-cle swarm optimization (PSO) algorithm is replaced with the traditional nonlinear least square method to optimize the implicit positioning model of TOA intersection. Compared with the traditional distance intersection positioning model, the TOA intersection positioning model is more suitable for the engineering practice and the optimization algorithm is more effective. Simulation results show that the proposed methods in this paper can effectively improve the positioning accuracy for the underwater target.

This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that operates under constraints, including communication base station time windows, battery levels, and task uniqueness. To solve the above model, we propose an enhanced algorithm through integrating Dueling Deep Q-Network (Dueling DQN) into adaptive large neighborhood search (ALNS), referred to as Dueling DQN-ALNS. The Dueling DQN component develops a method to update strategy weights, while the action space defines the destruction and selection strategies for the ALNS scheduling solution across different time windows. Meanwhile, we design a two-stage algorithm framework consisting of centralized offline training and decentralized online scheduling. Compared to traditionally optimized search algorithms, the proposed algorithm could continuously and dynamically interact with the environment to acquire state information about the scheduling solution. The solution ability of Dueling DQN is 3.75% higher than that of the Ant Colony Optimization (ACO) algorithm, 5.9% higher than that of the basic ALNS algorithm, and 9.37% higher than that of the differential evolution algorithm (DE). This verified its efficiency and advantages in the scheduling problem of return communication tasks for UAVs.

自主导航是未来航天器的发展趋势，利用由太阳敏感器、月球敏感器和红外地球敏感器组成的一体化多传感器天文观测平台，构建了多传感器的天文测量模型，结合航天器动力学模型，分析了自主导航系统的可观测性. 基于UKF滤波算法，建立多传感器自主导航算法，并采用原始测量信息和单点解析定位信息两种模式分别进行了仿真验证. 仿真结果表明，基于原始天文测量信息的自主导航模式，其导航精度要高于单点解析定位数据的自主导航精度，在无系统误差情况下，导航精度约为200m. 采用系统误差估计的自主导航方法后，常值系统误差对导航精度基本不产生影响.

Long-endurance unmanned aerial vehicles (LE-UAVs) are extensively used due to their vast coverage and significant payload capacities. However, their limited autonomous intelligence necessitates the intervention of ground control resources (GCRs), which include one or more operators, during mission execution. The performance of these missions is notably affected by the varying effectiveness of different GCRs and their fatigue levels. Current research on multi-UAV mission planning inadequately addresses these critical factors. To tackle this practical issue, we present an integrated optimization problem for multi-LE-UAV mission planning combined with heterogeneous GCR allocation. This problem extends traditional multi-UAV cooperative mission planning by incorporating GCR allocation decisions. The coupling of mission planning decisions with GCR allocation decisions increases the dimensionality of the decision space, rendering the problem more complex. By analyzing the problem’s characteristics, we develop a mixed-integer linear programming model. To effectively solve this problem, we propose a bilevel programming algorithm based on a hybrid genetic algorithm framework. Numerical experiments demonstrate that our proposed algorithm effectively solves the problem, outperforming the advanced optimization toolkit CPLEX. Remarkably, for larger-scale instances, our algorithm achieves superior solutions within 10 s compared with CPLEX’s 2 h runtime.

Non-Gaussian dynamic processes are ubiquitous due to the presence of non-Gaussian distributed variables. Therefore, fault detection of non-Gaussian dynamic processes plays a vital role to maintain the safe operation of systems and symmetry of data distribution. In this paper, a dynamic generalized likelihood ratio (DGLR)-based fault detection method is proposed for non-Gaussian dynamic processes. Different from the conventional principal component analysis (PCA)-based, dynamic PCA-based, and PCA-based GLR fault detection methods, the novelty of the proposed method is that the GLR is extended to non-Gaussian dynamic processes, and the randomized algorithm is integrated for threshold setting to attenuate the influence of non-Gaussian. The application scope of these methods is also discussed. The proposed method is compared with four existing fault detection methods on a numerical simulation and the continuous stirred-tank reactor (CSTR) process. The achieved results show that the proposed method is able to significantly improve the detection performance in terms of fault detection rate and prompt response to faults.