基于贝叶斯变量最大电流量标准 立方体 卡尔曼波器 适用于目标跟踪
Yu Ma1, Guanghua Zhang2, Songtao Ye2
1School of Electronics and Control Engineering, Chang'an University, Xi'an 710018, China.
Entropy (Basel, Switzerland)
|October 28, 2025
概括
这项研究引入了一种新的自适应过器,用于在具有挑战性的雷达环境中强大的目标跟踪. 基于贝叶斯的最大电流度标准的卡尔曼波器 (VBMCC-CKF) 在非高斯噪声下提高了准确性和效率.
科学领域:
- 信号处理 信号处理
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 雷达目标跟踪面临来自非线性动态,非高斯噪声和传感器异常值的挑战.
- 现有的强大的方法在经验调和计算负载方面扎,限制了性能.
- 对于复杂的跟踪场景,需要适应性,高效性和强大的过.
研究的目的:
- 为非线性系统提出一个完全适应和强大的过框架.
- 解决当前压制噪声和实时效率的方法的局限性.
- 开发一种可以消除手动实证调整的过器.
主要方法:
- 引入了基于贝叶斯的最大电流度标准的变量立方卡尔曼波器 (VBMCC-CKF).
- 综合变量贝叶斯推理与立方体卡尔曼波器 (CKF).
- 模拟内核大小作为反向马分布式随机变量,用于联合状态和参数优化.
主要成果:
- 在非高斯噪音下,VBMCC-CKF在单个和多个目标跟踪中表现出强的性能.
- 在单个目标追踪中,实现了至少14.33%的平均根平均平方误差 (Avg-RMSE) 减少.
- 在混乱的环境中显示了40%较低的最佳子模式分配 (OSPA) 距离和更高的命中率.
结论:
- VBMCC-CKF框架为动态目标跟踪提供了精确和可适应的解决方案.
- 该方法实现了平衡的噪声抑制和实时计算效率.
- 它有效地克服了传统过器中经验调节的局限性.
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