天基异步异质传感器网络多目标协同跟踪节点优化调度方法

杨庆伟; 蒋李兵; 郑舒予; 任笑圆; 王壮

doi:10.12263/DZXB.20250748

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天基异步异质传感器网络多目标协同跟踪节点优化调度方法

更新时间：2026-05-01

- 天基异步异质传感器网络多目标协同跟踪节点优化调度方法
- Multi-Target Cooperative Tracking Node Optimization Scheduling Method for Space-Based Asynchronous Heterogeneous Sensor Networks
- 电子学报 2026年页码：1-18
- 作者机构：
  
  1.国防科技大学自动目标识别全国重点实验室，湖南长沙 410073
  2.火箭军工程大学智控实验室，陕西西安 710025
- 作者简介：
  
  [ "杨庆伟男，1994年9月出生于山西省晋中市。现为国防科技大学电子科学学院博士研究生。主要研究方向为雷达信号处理、雷达资源调度分配。 E-mail: yangqingwei_nudt@163.com" ]
  [ "蒋李兵男，1982年12月出生于江苏省盐城市。博士，现为国防科技大学电子科学学院副教授、硕士生导师。主要研究方向为空间目标监视、雷达目标探测与识别。 E-mail: jianglibing@nudt.edu.cn" ]
  [ "郑舒予男，1996年3月出生于吉林省吉林市。博士，现为火箭军工程大学智控实验室讲师。主要研究方向为空间目标监视、目标检测与跟踪、自动目标识别。 E-mail: zhengshuyu21@sina.com" ]
  [ "任笑圆男，1991年9月出生于山西省太原市。博士，现为国防科技大学电子科学学院讲师。主要研究方向为空间目标监视、图像处理、计算机视觉。 E-mail: renxiaoyuan12@nudt.edu.cn" ]
  [ "王壮男，1973年9月出生于陕西省西安市。博士，现为国防科技大学电子科学学院教授、博士生导师。在国内外发表学术论文40余篇，主要研究方向为雷达信息处理、空间目标监视、自动目标识别。 E-mail: zhuang_wang@sina.com" ]
- 基金信息：
- DOI：10.12263/DZXB.20250748
  中图分类号： TN957.51;
- 收稿：2025-08-25，
  
  录用：2026-02-26，
  
  网络首发：2026-04-30，
- 稿件说明：
移动端阅览
杨庆伟, 蒋李兵, 郑舒予, 等. 天基异步异质传感器网络多目标协同跟踪节点优化调度方法[J/OL]. 电子学报, 2026,1-18.

YANG Qingwei, JIANG Libing, ZHENG Shuyu, et al. Multi-Target Cooperative Tracking Node Optimization Scheduling Method for Space-Based Asynchronous Heterogeneous Sensor Networks[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-18.
杨庆伟, 蒋李兵, 郑舒予, 等. 天基异步异质传感器网络多目标协同跟踪节点优化调度方法[J/OL]. 电子学报, 2026,1-18. DOI： 10.12263/DZXB.20250748.

YANG Qingwei, JIANG Libing, ZHENG Shuyu, et al. Multi-Target Cooperative Tracking Node Optimization Scheduling Method for Space-Based Asynchronous Heterogeneous Sensor Networks[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-18. DOI： 10.12263/DZXB.20250748.

摘要

有效的资源调度是天基异步异质传感器网络（Asynchronous Heterogeneous Sensor Networks，AHSN）多目标协同跟踪效能得以充分发挥的关键。监视系统中的天基雷达（Space-Based Radar，SBR）与天基光学（Space-Based Optical，SBO）两类典型传感器在遂行跟踪任务中的资源约束相对独立，但存在多重变量耦合共同影响多目标跟踪（Multi-Target Tracking，MTT）性能的问题。针对这一问题，本文提出一种面向低地球轨道（Low Earth Orbit，LEO）MTT的天基异质传感器网络节点调度方法。考虑到不同传感器的量测维度与采样率差异，本文提出一种分布式异步异质传感器并行局部滤波融合方法。该方法首先将目标的过程噪声同时建模在雷达笛卡尔坐标系与光学修正球坐标系（Modified Spherical Coordinate，MSC）下的运动方程中，并进行局部滤波；然后利用时戳偏置信息预测近似后验分布，通过容积点映射进行状态转换后在快速协方差交叉（Covariance Intersection，CI）融合准则下实现融合反馈跟踪。为发挥系统最优全局效能，论文综合考虑SBR回波信噪比（Signal-to-Noise Ratio，SNR）与SBO太阳相位角对量测质量的影响，推导了AHSN并行局部滤波融合后验克拉美-罗下界（AHSN Parallel Local Filtering Fusion Posterior Cramer-Rao Lower Bound，AHSN-PLFF-PCRLB）的跟踪性能测度。在此基础上建立了传感器网络节点调度优化模型，引入节点效能因子，开发了一种多起点贪婪迭代异质节点选取策略（Multi-Start Greedy Iterative Heterogeneous Node Scheduling Strategy，MSGI-HNSS），能够实现传感器-目标最优匹配方案的高效快速求解。仿真结果表明：与开环调度算法、枚举算法等标准方法相比，所提方法的跟踪精度优于开环调度算法，且在降低优化求解复杂度的基础上，能够获得可比拟枚举算法的优化跟踪精度。

Abstract

Effective resource scheduling is the key to maximizing multi-target collaborative surveillance for space-based asynchronous heterogeneous sensor networks (AHSNs). The resource constraints of space-based radar (SBR) and space-based optical (SBO) in tracking tasks are relatively independent

but there are multiple variables that are coupled and jointly affect the performance of multi-target tracking (MTT). To address this issue

this paper proposes a space-based heterogeneous sensor network node scheduling method for low Earth orbit (LEO) MTT. Considering the differences in measurement dimensions and sampling rates of different sensors

a distributed AHSNs parallel local filtering fusion method is proposed. This method simultaneously models the target process noise in the motion equations under the radar Cartesian coordinate and the optical modified spherical coordinate (MSC)

and performs local filtering. Then

it predicts the approximate posterior distribution utilizing the timestamp offset information

performs state transformation using the cubature point mapping

and achieves fusion feedback tracking under the fast covariance intersection (CI) criterion. To maximize the global performance

this paper comprehensively considers the influence of the echo signal-to-noise ratio (SNR) of SBR and the bistatic solar angle of SBO on the measurement quality

and derives the quantifiable tracking performance metric AHSN parallel local filtering fusion posterior Cramer-Rao lower bound (AHSN-PLFF-PCRLB). On this basis

an optimization model for sensor network node scheduling was established. By introducing the node efficiency factor

a multi-start greedy iterative heterogeneous node selection strategy (MSGI-HNSS) was developed

which can achieve fast solution for the optimal matching between sensors and targets. Simulation results demonstrate that compared to standard methods such as open-loop scheduling algorithms and exhaustive search algorithms

the proposed method achieves comparable tracking accuracy to exhaustive methods while significantly reducing optimization complexity.

关键词

Keywords

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