Spatial registration is the primary challenge affecting target tracking accuracy, especially for the aerial moving platform and sea target tracking. In this environment, it is important to account for both the errors in sensor observations and the variations in platform attitude. In order to solve the problem of complex types of errors in the tracking of sea targets by aerial moving platforms, a new spatial registration algorithm is proposed. Through separating and analyzing observation data, the influence of sensor observation error and attitude error on observation data is obtained, and a systematic error consistency matrix is established. Based on observation information from multiple platforms, accurate tracking of sea targets can be accomplished without estimating systematic error. In order to verify the effectiveness of the algorithm, we carried out simulation experiments and practical experiments on the lake, which showed that the new algorithm was more efficient than traditional algorithms.

Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy.

Cooperative observation optimization for maritime targets is crucial for improving marine monitoring precision. Existing research predominantly focuses on homogeneous platform cooperative observation optimization under random error influences, while neglecting the collaborative optimization challenges of heterogeneous platforms affected by systematic errors. To address these limitations, this paper proposes a heterogeneous unmanned platform cooperative observation optimization method based on Adaptive Enhanced Dung Beetle Optimization (AEDBO) algorithm. First, we derive optimal observation configurations for the aerial unmanned platform, maritime cooperative unmanned platform, and targets under azimuth systematic error impacts using an attitude correction algorithm. Subsequently, we design AEDBO by integrating an improved Tent chaotic mapping and centroid opposition-based learning strategy to enhance population diversity. An adaptive convergence factor and nonlinear ball-rolling dung beetle population decline model are introduced to balance global exploration and local exploitation capabilities, while Cauchy-Gaussian mutation strategies are employed to prevent premature convergence. Finally, AEDBO is applied to aerial unmanned platform trajectory optimization. Experimental results demonstrate that compared with DBO, AEDBO achieves improved optimization accuracy across 15 benchmark functions. In both safe and hazardous zone scenarios, optimized trajectories reduce target tracking errors to below 10 m, with optimal observation configurations validated through practical experiments. This study establishes a novel theoretical framework and optimization toolkit for heterogeneous unmanned platform cooperative observation.

The satellite navigation system of Unmanned Aerial Vehicles (UAVs) is susceptible to external interference in a complex environment, resulting in the loss of their own geodetic coordinate information. A spatial registration method for multi-UAVs based on a cooperative platform in a geodesic coordinate information-free environment is proposed to solve this problem. The mutual observation information between UAVs is approximated by the observation information of the cooperative platform. Indirect observation information of the target can be obtained on account of mutual observation. On the basis of this, a close-range spatial registration algorithm without the geodetic coordinate information of UAVs is designed by means of the right-angle translation method. Finally, the Kalman filtering technique is used to track maritime targets. In this paper, the proposed method is verified by a simulation experiment and a practical experiment. The proposed method is 90% effective in reducing systematic errors. The tracking accuracy after alignment is significantly better than that of the original trajectory.