As one of the most critical issues for target track, $\alpha$ -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on $\alpha$ -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for $\alpha$ -jerk model.

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.

Path planning poses a complex optimization challenge essential for the safe operation and successful mission execution of unmanned aerial vehicles (UAVs). Developing objectives, constraints, and decision-making processes for three-dimensional path planning involving multiple UAVs presents substantial challenges within the multi-objective optimization community. Traditional modeling approaches primarily aim to minimize path length and collision risks, often overlooking the need for a quantitative assessment of communication quality among UAVs. This neglect causes an inadequate representation of their true cooperative capabilities. In addition, there is difficulty in achieving an optimal balance between convergence, diversity, and feasibility. Therefore, this study introduces a bi-objective, three-dimensional path planning model specifically designed for UAVs. This model features an objective function that quantitatively evaluates inter-UAV communication quality throughout their flights. To solve this problem, this study proposes the dual-population regularity evolution algorithm (DPREA), which incorporates an auto-switching regularity evolutionary reproduction operator known as autoRE. It conducts extensive experiments across six testing suites and three path-planning simulation cases to validate the effectiveness of DPREA. The simulation results showed that its performance in addressing constrained multi-objective problems is significantly superior or at least comparable to that of state-of-the-art algorithms in most instances.

Due to the influence of the complex underwater environment, the sound speed constantly changes, resulting in the acoustic signal propagation trajectory being curved, which greatly affects the positioning accuracy of the underwater long baseline (LBL) system. In this paper, an improved LBL positioning method based on a B-spline surface for fitting the effective sound speed table (ESST) is proposed. Firstly, according to the underwater sound speed profile, the discrete ESST of each measurement station is constructed before the positioning test, and then, the node position of the B-spline surface is optimized by particle swarm optimization (PSO) to accurately fit the discrete ESST. Based on this, the improved LBL positioning method is constructed. In the underwater positioning test, the effective sound speed can be quickly found by measuring the time of arrival (TOA) of the acoustic signal and the target depth, and moreover, the target position parameters can be quickly and accurately estimated. The numerical simulation results show that the improved positioning method proposed in this paper can effectively improve the LBL positioning accuracy and provide the theoretical basis and the technical support for the underwater navigation and positioning.

Unlike the space targets such as satellites whose observation distance is about the order of 100 km, the measurement distance of long baseline (LBL) underwater positioning system is much shorter. If the noncoincidence between the multiple beacons and the target center is not considered, this systematic error will cause a little larger positioning error. Therefore, aiming at the situation that multiple beacons are installed outside the target and the distance between the beacons and the target center is significant, a multi-beacon positioning model of the LBL underwater system is constructed in this paper. Furthermore, for the sound speed systematic error and the time of arrival (TOA) systematic error in the multi-beacon LBL system positioning measurement data, three identification models of systematic error are constructed. According to the positioning process of the LBL underwater system, the expression of the optimal test statistic D is derived based on the TOA residual. Finally, the optimal selection criterion is given, and the underwater target position and systematic error can be accurately estimated. Both of the numerical simulation and the underwater positioning test show that the appropriate identification model of systematic error in the measurement data can be selected according to the optimal selection criterion, and the underwater target positioning accuracy of the LBL system can be improved.

Narrow field-of-view (FOV) cameras enable long-range observations and have been often used in deep space exploration missions. To solve the problem of systematic error calibration for a narrow FOV camera, the sensitivity of the camera systematic errors to the angle between the stars is analyzed theoretically, based on a measurement system for observing the angle between stars. In addition, the systematic errors for a narrow FOV camera are classified into “Non-attitude Errors” and “Attitude Errors”. Furthermore, the on-orbit calibration methods for the two types of errors are researched. Simulations show that the proposed method is more effective in the on-orbit calibration of systematic errors for a narrow FOV camera than the traditional calibration methods.