基于朝向解耦的动态编队目标变分跟踪方法

Orientation-decoupling-based variational tracking method for dynamic formation targets

  • 摘要: 鸟群与无人机群等典型低空编队飞行目标是雷达探测领域的热点。准确对其空间位置与编队构型进行准确估计是有效获取目标态势信息的关键。然而此类编队目标通常呈现复杂的空间构型,且内部结构高度动态。传统的多椭球跟踪方法仅使用简单的随机矩阵对每个子结构的扩展外形建模,子结构朝向与大小相互耦合,导致子结构初始化模型无法及时响应群目标的动态变化。为了对动态编队群目标的位置构型进行准确估计,本文提出了一种基于朝向解耦的多椭球群目标变分跟踪方法。每个子结构的朝向被单独建模为高斯随机变量。子结构的扩展大小被建模为一个对角的对称正定矩阵。由于量测方程的非线性以及缺乏共轭性质,变分贝叶斯技术被用来近似求解编队目标状态的复杂后验分布。所提方法的有效性通过实验数据得到验证,结果表明编队结构估计精度优于现有方法。

     

    Abstract:
    Background Low-altitude formation targets, including bird flocks and unmanned aerial vehicle (UAV) swarms, exhibit complex spatial configurations and rapidly changing internal structures. Accurate estimation of sub-object positions, orientations, extensions, and target counts is essential for radar-based situational awareness. Conventional multi-ellipsoidal tracking methods couple orientation and size within a random-matrix extension, limiting their response to rapid formation maneuvers.
    Purpose This study aims to improve the estimation of dynamic formation structure and internal composition by developing an orientation-decoupled variational tracking method.
    Methods The proposed multi-ellipsoidal orientation-decoupled group target tracking method (MEO-GTT) models each sub-object orientation independently as a Gaussian random variable. Its extension size is represented by a diagonal symmetric positive-definite matrix, while sub-object target counts are explicitly included as composition variables. A variational Bayesian procedure approximates the nonconjugate posterior induced by the nonlinear measurement model. It jointly estimates kinematic states, orientations, extension sizes, target counts, and measurement-to-sub-object associations. Performance was assessed through 200 Monte Carlo trials for a maneuvering V-shaped formation of 24 targets and through field measurements of 18 UAVs. The field data were collected using a high-resolution phased-array radar and evaluated against real-time kinematic positioning records. MEO-GTT was compared with conventional multi-ellipsoidal group target tracking (ME-GTT) under matched initialization and dynamic-model settings.
    Results In simulations, MEO-GTT maintained lower orientation errors during coordinated turns, converged faster after maneuvers, and improved the stability of position and axis-length estimates. It also stably estimated the number of targets within each sub-object. In the field experiment, MEO-GTT reduced orientation root mean square error (RMSE) from 7.60 degrees to 4.77 degrees and axis-length RMSE from 9.23 m to 8.78 m. The reported execution time increased from 0.0461 s to 0.0923 s.
    Conclusions Orientation-decoupled modeling improves structural tracking of maneuvering formation targets and supports simultaneous estimation of their internal composition. The method provides higher accuracy than conventional multi-ellipsoidal tracking at increased computational cost.

     

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