基于改进CCGA的岸基无源传感器协同运用优化方法

doi:10.12305/j.issn.1001-506X.2026.10.12

摘要/Abstract

摘要：

针对岸基无源传感器在复杂电磁环境下协同探测效能优化问题，提出一种基于改进合作型进化遗传算法（improved cooperative co-evolutionary genetic algorithm, ICCGA）的协同运用优化方法。该方法通过构建多传感器协同探测模型，引入自适应交叉和自适应变异操作，实现动态传感器资源分配，可有效解决传统优化算法在应对高维、多目标优化时存在的早熟收敛和计算效率低下的问题。仿真结果表明，所提算法与其他主流优化算法相比在适应度值上平均提升5.54%，系统运行时间上平均提升40.31%，为岸基无源传感器体系协同运用提供了有效的技术支撑。

关键词: 改进合作型协同进化遗传算法, 传感器调度, 协同运用, 目标跟踪, 区域搜索

Abstract:

Addressing the issue of optimizing the collaborative detection efficiency of the shore-based passive sensor in complex electromagnetic environments, an improved cooperative co-evolutionary genetic algorithm （ICCGA） is proposed for collaborative application optimization. This method constructs a multi-sensor collaborative detection model and introduces adaptive crossover and adaptive mutation operations to achieve dynamic sensor resource allocation. It effectively solves the problems of premature convergence and low computational efficiency that traditional optimization algorithms face when dealing with high-dimensional and multi-objective optimization. Simulation results show that, compared with other mainstream optimization algorithms, the proposed algorithm has an average increase of 5.54% in the fitness value and an average increase of 40.31% in the system running time, which provides an effective technical support for the cooperative application of shore-based passive sensor system.

Key words: improved cooperative co-evolutionary genetic algorithm (ICCGA), sensor scheduling, collaborative application, target tracking, area search

中图分类号:

TN 971

王德友, 安永旺, 段永胜. 基于改进CCGA的岸基无源传感器协同运用优化方法[J]. 系统工程与电子技术, 2026, 48(10): 3393-3403.

Deyou Wang, Yongwang An, Yongsheng Duan. Optimization method of cooperative application of shore-based passive sensor based on improved CCGA[J]. Systems Engineering and Electronics, 2026, 48(10): 3393-3403.

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符号	含义
S	传感器的数量
T	时刻
$ {\theta }_{{i}} $	传感器i的朝向角度
$ {\alpha }_{i} $	传感器i的探测角度
$ {c}_{j} $	第j个目标被传感器监视跟踪的个数
$ {\varpi }_{1} $，$ {\varpi }_{2} $	权重系数
$ B $	传感器的负载均衡度
$ {\varDelta }_{\text{th}} $	差异阈值
$ \beta $	0~1均匀随机数
$ \eta (0,\sigma ) $	均值为0、标准差为$ \sigma $的高斯随机数
$ \sigma $	种群的标准差
$ {\sigma }_{\max } $	最大标准差阈值
$ {P}_\text{min} $	最小变异概率
$ {P}_\text{max} $	最大变异概率
$ {P}_\text{mut} $	自适应变异概率

对比类型	ICCGA	CCGA	GA
使用场景	动态、高维度、多组件的复杂问题	高维度、多组件的复杂问题	低维度、中小规模的优化问题
问题处理	问题分解+动态协同机制	将问题分解，分别优化	对整体直接优化
种群结构	分层子种群	多个子种群并行优化	单一混合种群
交叉操作	自适应交叉操作	相同个体交叉	单点交叉
变异操作	结合负载状态的自适应变异操作	同GA	固定步长高斯变异
选择策略	同GA	同GA	轮盘赌选择
适应度评估	多目标加权评估	子种群之间的最优解组合评估	计算整体的适应度
局部优化	周期性精英扰动	无	无
协同机制	动态协作	通过适应度进行固定周期协作	无
求解效率	高	中	低

装备型号	坐标/km	探测距离/km	多目标处理个数	波束覆盖范围/（°）
传感器1	（257.9, 38.9）	198	3	22
传感器2	（260.1, 55）	202	5	24
传感器3	（266.5, 70.8）	217	6	21
传感器4	（279.4, 89.3）	185	8	15
传感器5	（289.7, 106.7）	176	5	18
传感器6	（300.3, 120.6）	165	9	26
传感器7	（302.6, 140.5）	189	3	25
传感器8	（303.7, 160.5）	159	9	31
传感器9	（308.9, 177.5）	189	7	28
传感器10	（306.9, 190.6）	163	3	17
传感器11	（301.4, 207.5）	178	2	18
传感器12	（290.8, 221.5）	226	9	22
传感器13	（283.7, 239.5）	213	4	23
传感器14	（275.5, 257.5）	197	3	25
传感器15	（267.8, 277.6）	202	2	19

平台型号	工作频率/MHz	位置/km
目标1	8 400~9 000	（386.5, 46.5）
目标2	9 000~10 000	（389.5, 71.5）
目标3	8 000~12 000	（391.3, 90.5）
目标4	9 300~10 100	（404.5, 113.5）
目标5	8 000~12 500	（398.5, 175.5）
目标6	8 000~12 500	（389.5, 212.5）
目标7	8 000~12 500	（405.5, 217.5）
目标8	8 000~12 000	（381.5, 255.5）
目标X	8 500~9 600	（377.5, 17.5）
目标Y	9 300~10 100	（436.5, 277.5）

平台型号	初始速度向量/ （km/h）	合速度/ （km/h）	初始方向/ （°）	转弯角速度/ （°/h）
目标1	[120,−90]	150	−36.87	180
目标2	[90,120]	150	53.13	0
目标3	[120,−90]	150	−36.87	200
目标4	[150,0]	150	0	180
目标5	[30,−135]	138.3	−77.47	120
目标6	[30,−135]	138.3	−77.47	120
目标7	[30,−135]	138.3	−77.47	120
目标8	[141,−60]	153.4	−23.07	0
目标X	[90,120]	150	53.13	0
目标Y	[−120,90]	150	143.13	180