This paper focuses on the cooperative localization of maneuvering targets on the ground from unmanned aerial vehicles (UAVs) with bearing-only measurements. A maneuvering target tracking algorithm is proposed based on the interactive multiple models (IMM) framework. In order to fully utilize the localization redundancies and improve the accuracy of target localization in multi-UAVs system, a belief factor for state fusion is proposed considering the positioning and measurement capabilities of each UAV. Moreover, a trajectory smoothing algorithm is proposed by the Quasi-uniform B-spline (QUBS) method, which is utilized for reducing the localization error caused by measurement noise. By calculating the discrete curvature of the maneuvering target trajectory, an adaptive identification method of turn rate is derived to alleviate the delay of the model switch. Besides, a maneuvering target tracking model is established and a unified framework based on the IMM method is proposed to improve the applicability of the maneuvering target tracking. Simulation results demonstrate that the proposed turn rate identification based on IMM (TRIIMM) algorithm outperforms the other algorithms in terms of tracking accuracy and takes the least time cost.

Aiming at cooperative navigation of multi‐aircrafts, an adaptive EKF‐SLAM algorithm for non‐fixed landmark is proposed. First, the aircraft motion model and angle measurement model are established. Second, the fixed landmark cooperative EKF‐SLAM algorithm is established. Furthermore, a distributed EKF‐SLAM algorithm is designed. Then, the adaptive EKF‐SLAM algorithm with non‐fixed landmark is proposed. Finally, simulation results verify the effectiveness of the proposed algorithm.

This article investigates the effect of distance-dependent noise on optimal transponder distance in a moving long baseline positioning system. First, according to the positioning model of the moving long baseline positioning system, the relationship between measurement noises and positioning error is established. Second, a cost function is proposed to evaluate the positioning performance. Then, based on the optimal formation, the optimal distance is deduced. Finally, the relationship between the optimal distance and the parameter for the distance-dependent error is proposed. By using this relationship, the effect of distance-dependent measurement noise on optimal distance is analyzed. Simulation examples illustrate the results.

Cooperative localization distinguishes itself from traditional localization methods by leveraging inter-node measurements among unknown nodes. This study proposes a novel dual-stage algorithm grounded in the theory of surveying adjustment (TOSA), which strategically integrates pairwise relative range measurements with cluster topological constraints. Initially, a range measurement error adjustment model is developed to mitigate range errors through cluster spatial relationship analysis. Subsequently, a TOSA-based optimization algorithm is proposed to realize a globally optimal estimation while simultaneously refining range measurements through the inherent robustness of the cluster network. Furthermore, a complete Euclidean range matrix derived from the TOSA approach is effectively incorporated into the multidimensional scaling (MDS) framework, thereby significantly enhancing the precision of relative topology. Topology centroid derived from TOSA is introduced to provide additional reference for affine transformation in coordinate registration, extending the applicability of cooperative localization. Simulation results validate the algorithm’s efficacy and superiority in enhancing localization accuracy and maintaining topology integrity. Moreover, an extended investigation of time complexity provides valuable insights for future research endeavors in large-scale network configurations.

This paper presents an enhanced robust filtering algorithm designed for integrated SINS/GNSS navigation systems operating under nonGaussian noise conditions. To address the challenges posed by heavy‐tailed noise distributions, a novel noise modelling framework based on Student's t‐distribution is developed, which provides superior outlier resilience compared to conventional Gaussian assumptions. Furthermore, a Gaussian mixture model representation is employed for both the one‐step predicted and likelihood probability density functions, enabling more accurate quantification of uncertainty. Additionally, a variational Bayesian‐based adaptive mechanism is employed for dynamic scale matrix optimisation, effectively mitigating the impact of process noise outliers. Extensive experimental validation, including Monte Carlo simulations and vehicular tests, demonstrates the algorithm's superior performance in SINS/GNSS integration scenarios. Comparative results indicate significant improvements in positioning accuracy and robust convergence characteristics relative to a decent number of iterations.

This paper presents a trajectory optimization algorithm for super-synchronous-transfer-orbit (SSTO) large launch systems by convex optimization. The payload of SSTO launch systems is typically a geostationary equatorial orbit (GEO) satellite, and the time and position of orbital injection are constrained, which is quite different from the case of general satellites. In this paper, the optimal control problem of SSTO large launch systems is formulated considering the terminal constraints including orbital elements and the time-position equation. To improve the computational performance of the algorithm, the terminal orbital element constraints are expressed in the perifocal coordinate system with second-order equations. And then, several convexification techniques and their modified strategies are applied to transform the original trajectory optimization problem into a series of convex optimization problems, which can be solved iteratively with high accuracy and computational efficiency. Considering the time-position constraint of the payload, the flight time updater design method is proposed to correct the error of time during the flight, which lays solid foundation for the subsequent flight phase, guaranteeing that the GEO satellite settles into the required position. Finally, simulation results indicate the high efficiency and accuracy and strong robustness of the proposed algorithm in different special situations including engine failure and time delay. The algorithm proposed in this paper has great development potential and application prospect in onboard trajectory optimization of SSTO launch missions and similar situations.