舰载机多雷达传感器任务分配与采样间隔融合优化算法  

Optimization algorithm of task allocation and sampling interval fusion for multi radar sensors of carrier based aircraft

作　　者：李宝鹏 高伟亮 王守权 李大龙 王永坤 LI Bao-peng;GAO Wei-liang;WANG Shou-quan;LI Da-long;WANG Yong-kun(NAU Qingdao Branch,Qingdao 266041,China;Leihua Electronic Technology Institute of AVIC,Wuxi 214063,China)

机构地区：[1]海军航空大学青岛校区,山东青岛266041 [2]中航工业雷华电子技术研究所,江苏无锡214063

出　　处：《控制与决策》2022年第3期565-573,共9页Control and Decision

基　　金：航空基金项目(2016JC07001);武器装备科研基金项目(海装计[2018]19号)。

摘　　要：针对舰载机协同探测中多雷达传感器资源配置问题,提出一种多目标跟踪场景下的多传感器数据率管理与任务分配融合优化算法.在基于协方差控制的多传感器分配模型基础上,加以目标优先级和传感器效能条件约束,建立一种多传感器数据率管理与任务分配融合优化模型.将驻留时间改进因子引入序贯卡尔曼滤波算法,计算不同采样间隔下传感器组合状态估计融合协方差,求解最优采样间隔与传感器组合.仿真结果表明,所提出的融合优化算法能自适应优化数据率和雷达分配组合,提高多传感器的多目标跟踪能力,可有效节省雷达资源,与其他方法相比具有较快的收敛速度和更好的稳态精度.A multi-sensor data rate management and task assignment fusion optimization algorithm under multi-target tracking scenario is proposed to solve the problem of multi-radar sensor resource allocation in co-detection of shipborne aircrafts.Based on the multi-sensor distribution model controlled by covariance,a fusion optimization model of multi-sensor data rate management and task assignment is established by constriction of target priority and sensor performance conditions.By introducing dwell time improvement factors into the sequential Kalman filtering algorithm,the fusion covariance of sensor combination state estimation under different sampling intervals is calculated,and the optimal sampling interval and the sensor combination are solved.Simulation results show that the proposed fusion optimization algorithm can adaptively optimize the data rate and radar allocation combination,improve the multi-sensor tracking capability,effectively save radar resources,and has faster convergence speed and steady-state accuracy compared with other methods.

关 键 词：多目标跟踪 多雷达传感器 数据率管理 任务分配 协同探测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]