舰载机多雷达传感器任务分配与采样间隔融合优化算法
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海军航空大学青岛校区

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TP391

基金项目:

航空基金(2016ZC07001,2014ZC07003);武器装备军内科研基金(海装计〔2018〕19号)


Optimization Algorithm of Task Allocation and Sampling Interval Fusion for Multi Radar Sensors of Carrier Based Aircraft
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Avionics Engineering and Command Department, Naval Aviation University Qingdao Campus

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    摘要:

    针对舰载机协同探测中多雷达传感器资源配置问题,提出了一种多目标跟踪场景下的多传感器数据率管理和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,建立了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。仿真表明:本文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,可有效的节省雷达资源,与其他方法相比具有较快的收敛速度和稳态精度。

    Abstract:

    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 ship-borne aircraft. 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 factor into sequential Kalman filtering algorithm, the fusion covariance of sensor combination state estimation under different sampling intervals is calculated, and the optimal sampling interval and sensor combination is solved. Simulation results show that the fusion optimization algorithm proposed in this paper 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.

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历史
  • 收稿日期:2020-09-14
  • 最后修改日期:2020-12-09
  • 录用日期:2021-01-08
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