一种自适应遗传优化的通信定位算法

doi:10.12382/bgxb.2024.0208

摘要/Abstract

摘要：

在复杂通信环境下,全球导航卫星系统(Global Navigation Satellite System,GNSS)难以为用户提供稳定且准确的位置信息,为解决受测量数据的不确定性而导致定位偏差问题,提出一种自适应遗传优化的通信定位(Enhanced Adaptive Genetic Location,EAGL)算法。建立一个基于到达时间差的定位模型来反映目标源位置与信号环境之间的关系,并对满足目标函数的可能解进行实数编码,同时建立适应度函数,用于计算每个个体的适应度值。对种群执行选择运算以及改进的自适应交叉、变异运算来提高种群基因型质量,避免陷入局部最优解的困境。通过迭代得到最高适应度值的个体的基因型,以获得目标源的准确坐标。仿真结果表明:所提算法的定位精度比基本遗传算法(Simple Genetic Algorithms,SGA)和Chan-Taylor算法更高,并且随着测量值误差的逐渐增大,EAGL算法在不同误差条件下表现出的误差波动最小;EAGL算法性能稳定,并能够实现较高精度的定位。

关键词: 通信定位, 遗传算法, 自适应, 交叉, 变异

Abstract:

In a complex communication environment,it is difficult for the global navigation satellite system (GNSS) to provide users with stable and accurate location information.An enhanced adaptive genetic location (EAGL) algorithm is proposed to solve the problem of positioning bias caused by the uncertainty of measured data.In the proposed algorithm,a localization model based on time difference of arrival is established to reflect the relationship between the location of target source and the signal environment.The possible solutions satisfying the objective function are encoded in real number,and the fitness function is established,which is used to calculate the fitness value of each individual.The selection operation is performed on the population,and the improved adaptive crossover and mutation operation are used to improve the genotype quality of the population and avoid falling into the dilemma of local optimal solution.The genotype of the individual with the highest fitness value is obtained by iteration to get the exact coordinates of a target source.The simulated results show that the positioning accuracy of the proposed algorithm is higher than those of the simple genetic algorithm (SGA) and Chan-Taylor algorithm.With the gradual increase in the error of the measured value,the error fluctuation of EAGL algorithm under different error conditions is the smallest.As a result,EAGL algorithm is stable and capable of achieving the high-precision positioning.

Key words: communication localization, genetic algorithm, adaptive, crossover, variation

刘芳, 刘亚男, 杜凯. 一种自适应遗传优化的通信定位算法[J]. 兵工学报, 2025, 46(4): 240208-.

LIU Fang, LIU Yanan, DU Kai. An Adaptive Genetic Optimization Algorithm for Communication Localization[J]. Acta Armamentarii, 2025, 46(4): 240208-.

图/表 10

图1 双曲线定位示意图

Fig.1 Schematic diagram of hyperbolic positioning

表1 不同迭代次数对比

Table 1 Comparison of different iterations

迭代次数	运行时间/s	定位误差/m
50	78.748	0.244
100	78.397	0.253
160	78.317	0.240
180	78.411	0.257
200	78.600	0.290

图2 搜索范围变化

Fig.2 Change of search range

图3 最大进化代数变化

Fig.3 Change of the maximum evolutionary algebra

图4 种群进化分布

Fig.4 Population evolutionary distribution map

图5 定位误差RMSE

Fig.5 RMSE of positioning error

图6 5种算法定位误差RMSE对比

Fig.6 Comparison of RMSEs of positioning errors for 5 kinds of algorithms

表2 3种算法的定位坐标

Table 2 Positioning coordinates of 3 kinds of algorithms m

定位误差/m	Chan-Taylor算法	SGA	EAGL算法
4	(50.05,99.72)	(49.98,99.98)	(49.98,100.00)
8	(50.43,98.79)	(50.47,99.29)	(50.01,99.93)
12	(49.85,98.66)	(49.99,99.84)	(50.03,99.90)
16	(49.00,97.55)	(50.13,99.39)	(50.04,99.87)
20	(51.65,94.45)	(51.55,98.97)	(49.77,99.87)
24	(48.50,93.27)	(48.74,99.52)	(50.17,99.54)
28	(48.50,93.06)	(49.58,100.50)	(49.81,99.88)
32	(47.22,88.78)	(47.97,99.05)	(50.15,99.78)
36	(54.08,87.20)	(54.29,96.32)	(50.17,99.44)
40	(46.01,89.66)	(46.50,101.25)	(49.13,99.77)

表3 不同目标位置3种算法的定位坐标

Table 3 Positioning coordinates of 3 kinds of algorithms for different targets

序号	源坐标/ m	定位坐标/m
序号	源坐标/ m	Chan-Taylor算法	SGA	EAGL算法
1	(40,90)	(39.55,74.48)	(39.38,86.84)	(39.38,89.55)
2	(30,100)	(29.54,90.63)	(29.29,98.56)	(29.76,99.84)
3	(20,110)	(21.00,104.93)	(19.63,109.10)	(20.01,109.760)

图7 不同待测目标源定位误差RMSE

Fig.7 RMSEs of of positioning errors of different target sources to be measured

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