Abstract: Airport surface multilateration technology is a new-generation surveillance technology vigorously promoted by the International Civil Aviation Organization. It is widely used in civil aviation surface surveillance systems due to its strong reliability, wide coverage, and ease of maintenance. However, traditional multilateration methods have problems such as positioning ambiguity and sensitivity to observation noise in practical applications, which affect the accuracy and efficiency of surveillance. In this paper, the traditional positioning algorithm is combined with the intelligent optimization algorithm, and a high-precision multilateration method based on the improved cuckoo search algorithm is proposed. Firstly, the traditional multilateration Chan algorithm is used to obtain the initial estimated value of the cuckoo search algorithm to limit the initial search area of the cuckoo search algorithm and improve the convergence rate of the algorithm. Then, the Tent chaotic map is used to improve the population diversity and effectively generate new individuals. Finally, considering the overall change of the population with the increase of the number of evolutionary generations, a calculation formula for the adaptive scaling factor is designed to balance the global search ability and the local exploration ability, so as to optimize the solution performance of the algorithm and approach the global optimal solution. The experimental results show that under the assumed ideal conditions, when the noise variance is the largest, the mean square error of the improved cuckoo search algorithm in this paper is reduced by more than 80% and 66% compared with the Chan algorithm and the cuckoo search algorithm respectively. This method has good robustness to TDOA observation noise and obtains high positioning accuracy.

Key words: Airport scene monitoring, Multi-point positioning technology, Time difference of arrival, Chan algorithm, Cuckoo algorithm