Jiongqi Wang

Multi-sensor data fusion is essential for accurate target tracking and trajectory reconstruction. However, common forms of systematic error in multi-sensor observations, including constant biases, linear drifts, and saturating exponential drifts, can degrade measurement consistency and trajectory estimation accuracy. Within the B-spline-constrained Error Model Best Estimate of Trajectory (EMBET) framework, B-spline coefficients and systematic-error parameters may produce similar observation responses, allowing part of the systematic-error response to be absorbed into the spline-coefficient correction and thereby weakening the identifiability of the systematic-error parameters. To avoid the weak-identifiability mechanism associated with the joint parametric estimation of trajectory and systematic-error terms, a residual-driven ResCompFormer method is proposed for systematic-error compensation and target trajectory reconstruction. First, a B-spline-constrained EMBET model is established to analyze the coupling between B-spline coefficients and systematic-error parameters. Systematic-error estimation is then removed from the joint EMBET parameter-estimation problem and reformulated as observation-domain error-sequence prediction, and ResCompFormer is employed to capture temporal dependencies and cross-channel correlations in multi-sensor residuals. The predicted errors are fed back to correct the observations, followed by iterative trajectory re-estimation. Simulation results confirm the systematic-error absorption mechanism and show that the proposed method outperforms the considered model-driven and data-driven methods in both systematic-error compensation and trajectory reconstruction, including iterative and stepwise EMBET variants. Additional experiments demonstrate the robustness of the proposed method to variations in systematic-error characteristics and sensor availability.

In angles-only initial relative orbit determination for non-cooperative targets, many existing methods require multiple angle measurements over a long time span or rely on iterative optimization. As a result, it is difficult to achieve fast initialization from a small number of measurements at the beginning of on-orbit navigation. To address this issue, this paper proposes a non-iterative initial relative orbit determination algorithm in a spherical coordinate framework. First, the relative motion equations are derived in detail in a spherical coordinate system centered on the line-of-sight direction, including the exact nonlinear two-body model and a linear time-invariant model applicable to near-circular orbits. Second, a non-iterative estimation algorithm that requires no prior state information is developed. The algorithm only needs to perform singular value decomposition on a single 6 × 6 matrix and solve a univariate polynomial. In the absence of camera offsets or orbital maneuvers, the initial relative state of the target can be obtained rapidly using only four angle measurements. Simulation results show that, compared with the classical iterative method, the proposed algorithm significantly improves computational efficiency while maintaining comparable or even superior estimation accuracy. It also exhibits excellent numerical stability and noise robustness, making it suitable for fast initialization in angles-only relative navigation of non-cooperative targets.

The unknown disturbances in complex environments often cause stochastic errors and uncertain inputs in measurement instruments, especially in the fields of target tracking and autonomous navigation. However, traditional Set-based state estimation and Kalman filter approaches are inadequate for modeling the noise-mixed scenario, and the robustness of the estimation outcomes is rather poor. To solve the problem, this article presents a method for target state probabilistic interval estimation, utilizing the set membership estimation approach based on probabilistic zonotopes. Initially, the proposed transformation operator effectively conducts reasonable transformation on the mixed interference of uncertain variables and stochastic errors. Subsequently, the set membership filtering is employed to predict and update the target state. Moreover, the probability assignment is proposed to convert the state estimation into probabilistic interval estimation. Finally, the robust state estimation algorithm framework based on probabilistic zonotopes is summarized. Experiments demonstrate the effectiveness and feasibility of our proposed method.

With the extensive application of unmanned vehicles and drones in logistics, inspection, and other scenarios, the planning and decision-making capabilities of consumer unmanned electronic systems have attracted significant research attention. When multiple performance indicators are considered simultaneously, the decision-making process transforms into a multi-objective optimization problem. In such problems, a single optimal solution typically does not exist; instead, decision-makers are presented with a set of Pareto optimal solutions, commonly referred to as the solution pool. However, the size of this solution pool grows exponentially with the number of objectives, making manual management impractical. Consequently, there is an increasing demand for effective methods to extract a small subset of representative solutions from the pool to enable effective and manageable decision-making. Motivated by this challenge, this paper proposes a pruning method for the Pareto optimal set based on the Borda count. The method first determines the optimal size of the representative subset using a diversity metric, followed by the establishment of preference rankings to identify representative solutions. This study first takes into account the inherent conflict among objectives and provides a theoretical analysis of the optimality of the Borda count method from the perspective of the probability of the Condorcet paradox. It demonstrates that this method minimizes errors arising from preference aggregation when multiple objective functions are considered. Subsequently, simulations on 12 test instances confirm the significant advantages of the two pruning frameworks proposed in this paper over the two existing methods.