###### Abstract
Most of the current spatial registration algorithms based on cooperative platforms only achieve accurate estimations of the attitude of the platform, but few research studies consider the sensor measurement error. Target tracking is a technique used to estimate the state of a target based on measurement information from sensors, so spatial registration is a prerequisite to perform target tracking. The filter is the essence of the target tracking algorithm. The Kalman filter (KF) [16] was first proposed to solve linear problems. For nonlinear problems, the extended Kalman filter (EKF) [17; 18] was first proposed, but due to its weak treatment of strongly nonlinear problems, the traceless Kalman filter (UKF) [19; 20], the volumetric Kalman filter (CKF) [21; 22], and the particle filter (PF) [23] appeared one after another. Although they have higher accuracy on nonlinear problems, the computational complexity is correspondingly much higher. Recently, the probabilistic hypothesis density filter (PHD) [24; 25; 26] has been used in multi-target tracking problems with multi-sensor bias, which has become a hot problem recently, but it is mostly applied only in the two-dimensional plane, and it also suffers from the same high computational complexity, which makes it difficult to accomplish the task of real-time tracking. All of the above are methods based on data information [27]. In addition, the UAV target detection and tracking method based on computer vision has gradually become popular [28]. When locating and tracking maritime targets in the maritime environment, UAVs generally use satellite navigation systems to provide their own high-precision geodetic coordinate information to complete the tracking of targets [29; 30; 31], but in the complex maritime environment [32; 33; 34], especially in the battlefield environment, the fragile satellite navigation system is susceptible to interference from threats such as electromagnetic pulse and intentional deception, which affects maritime target tracking and affects the accuracy of target tracking. [PERSON] et al. [35] proposed a multi-source UAV cluster collaborative positioning method using information geometry, which realizes the self-positioning problem of UAVs in non-ideal environments. Since both current abatement methods have their own limitations and there are few studies on multi-UAV spatial registration in a geodesic coordinate information-free environment, this paper designs a spatial registration method for multi-UAVs based on a cooperative platform in a geodesic coordinate information-free environment. Firstly, with the aid of a cooperative maritime platform that can move its position at any time, the mutual observation information among UAVs is approximated by the observation of UAVs in close proximity. Then, the right-angle translation method is used to obtain indirect observation information of UAVs on maritime targets based on the mutual observation information. Finally, an error estimation method is designed by combining direct and indirect observation information of UAVs on maritime targets. Accurate tracking of the maritime target is realized using the filtering algorithm. The remainder of the paper is organized as follows: Section 2 describes the problem of spatial registration and introduces the general environment. Section 3 proposes the target tracking method, including mutual UAV observation information based on a cooperative platform, the spatial registration method, and the maritime target tracking method. In Section 4, experimental verification is provided. Finally, conclusions are drawn in Section 5.