机场场面多点定位技术是国际民航组织大力推行的新一代监视技术，由于其可靠性强、覆盖范围广和易于维护等，在民航场面监视系统中有广泛应用。然而传统多点定位方法在实际应用中存在定位模糊、对观测噪声敏感等问题，影响了监视的精准度和效率。本文将传统的定位算法与智能优化算法处理相结合，提出一种基于改进布谷鸟算法的高精度多点定位方法。首先利用传统的多点定位Chan算法获得布谷鸟算法的初始估计值以限制布谷鸟算法的初始搜索区域，提高算法的收敛速率；再利用Tent混沌映射提高种群多样性，有效产生新个体；最后考虑了随着进化代数增加种群的整体变化，设计了自适应缩放因子的计算公式，以平衡全局搜索能力和局部勘探能力，从而优化算法的求解性能，逼近全局最优解。实验结果表明，在假定的理想条件下，噪声方差最大时本文改进的布谷鸟算法相比于Chan算法和布谷鸟算法的均方误差分别降低了80%和66%以上，该方法对TDOA观测噪声具有很好的鲁棒性，获得了较高的定位精度。
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.
[1]CHAITANYA C, ESWARA D, GANESH L, et al.Performance analysis of hyperbolic multilateration using circular error probabilit[J]., 2016, 85:676-682
[2]王 超, 姚瑞玲.融合有向天线和的定位算法[J].计算机工程与设计, 2018, 39(06):1574-1578
[3]武喜萍, 杨红雨, 韩松臣.基于复杂网络的空中交通特征与延误传播分析[J].航空学报, 2017, 38(S1):113-119
[4]刘引川, 刘志勇, 林琳.基于的-报文有效性分析与测试[J].电子测量与仪器学报, 2018, 32(09):88-93
[5]赵向程.基于多点定位技术的机场场面监视研究[D].石家庄：河北科技大学, 2021.
[6]王振博.基于多点定位的目标轨迹跟踪方法研究[D].石家庄：河北科技大学, 2022.
[7]CHAN Y T, HO K C.A simple and efficient estimator for hyperbolic location[J].IEEE Transactions on Signal Processing, 1994, 42(8):1905-1915
[8]孙顶明.基于CHAN-Taylor的室内复杂环境UWB定位算法研究[D].南京：南京邮电大学, 2019.
[9]周 旋.基于TDOA的民航多点定位算法研究[D].昆明：昆明理工大学, 2018.
[10]王 力, 于 雷.一种用于机场场面监视的高精度位置解算方法[J].中国民航大学学报, 2021, 39(02):21-25
[11]CHAN Y, SHEN C, ZHANG K, et al.TDOA positioning method based on Taylor series expansion based on cuckoo search algorithm[C]//2020 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). IEEE, 2020: 986-989.
[12]MAN Y, CHEN G W, WEI Z S.TDOA positioning method based on mixed strategy sparrow search algorithm[C]//?2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS). IEEE, 2021: 1-6.
[13]WU P, SU S, ZUO Z, et al.Time difference of arrival (TDOA) localization combining weighted least squares and firefly algorithm[J].Sensors, 2019, 19(11):2554-
[14]王 田, 兰艳亭, 郭译凡, 等.改进的免疫粒子群算法在定位中的应用[J].无线电工程, 2023, 53(05):1199-1206
[15]陈 磊, 郑燊聪, 蒋禹齐, 等.基于改进混沌布谷鸟算法的风电场多机等值参数辨识方法[J].电力系统保护与控制, 2023, 51(20):99-106
[16]赵允坤, 马 强, 王 丽, 等.基于改进布谷鸟算法优化SVM的隧道围岩变形预测[J].中国科技信息, 2024, (18):110-113
[17]JIANG Y L, LIU M, CHEN T, et al.TDOA passive location based on cuckoo search algorithm[J].Journal of Shanghai Jiaotong University (Science), 2018, 23:368-375
[18]董朝阳, 路遥, 江未来, 等.基于布谷鸟搜索算法的一类变体飞行器容错控制[J].航空学报, 2015, 36(06):2047-2054
[19]GAO S Z, GAO Y, ZHANG Y, et al.Adaptive cuckoo algorithm with multiple search strategies[J]., 2021, 106:-
[20]吕 鑫, 慕晓冬, 张 钧, 等.混沌麻雀搜索优化算法[J].北京航空航天大学学报, 2021, 47(8):1712-1720
[21]张 娜, 赵泽丹, 包晓安, 等.基于改进的 混沌万有引力搜索算法[J].控制与决策, 2020, 35(4):893-900
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