###### Abstract  
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. target tracking; spatial registration; UAV; cooperative platform; coordinate transformation +
Footnote †: journal: Applied  
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Footnote †: journal: Applied  
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burden. The literature [7] proposes a method for spatial registration of a single sensor using batch nonlinear least squares estimation, which is shown to be statistically valid by the evaluation of the Clamero lower bound, but the method requires high-precision satellite coordinates of the sensor. [PERSON] and [PERSON] et al. [8] proposed an iterative design spatial registration algorithm based on expectation maximization (EM), but the model is too ideal and far from engineering applications. [PERSON] et al. [9] designed a spatial registration algorithm based on exact least squares for multiple dissimilar sensors, but the sensor data type and scenario environment are too simple. [PERSON] et al. [10] used a two-stage nonlinear least squares method to solve the spatial registration problem of asynchronous multiple sensors, but only the range and azimuth errors were analyzed, which is only applicable to a two-dimensional radar. [PERSON] et al. [11] used an exact great likelihood algorithm to achieve the spatial registration of two station coast radar systems based on noncooperative targets; the scenario posture is more realistic, but this method does not consider the attitude of the moving sensors and is only applicable in the two-dimensional plane. [PERSON] et al. [12] took the measurement error and attitude error of the sensors into account simultaneously. However, they did not consider the coupling of the sensor measurement error and the attitude error. In summary, the current spatial registration methods by direct observation of the target require additional information, such as platform geodesic coordinates or mutual observation information, to perform position alignment, and most of them only consider the sensor measurement error, ignoring the attitude error of the moving platform, which will also cause a large error in the measurement data. The second spatial registration method relies on a cooperative platform. This method relies on the priori information of the cooperative platform to firstly perform spatial registration, and then performing observation and data fusion of the target. [PERSON] et al. [13] proposed an error estimation method for multiple passive sensors using cooperative targets, but it requires a common coordinate system and is only applicable to a linear system. [PERSON] et al. [14] designed an attitude solution model based on the combination of photoelectric sensors and MEMS-IMU by observing auxiliary beacons, which can be treated as a cooperative platform. [PERSON] et al. [15] used optical sensors to compensate for the attitude error of the vehicle but did not consider the measurement error of the sensors.