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水下多普勒轴承机动目标运动分析基于联合估计的自适应无气味卡尔曼波器
Dajun Sun1, Yiao Zhang1, Tingting Teng1
1National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.
这项研究引入了一种新的算法,用于使用被动声纳追踪水下目标的机动. 联合估计的自适应无气味卡尔曼波器 (JE-AUKF) 改善了针对复杂机动的目标运动分析.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 目标追踪器 目标追踪器
背景情况:
- 被动声纳系统在跟踪不合作的水下机动目标时面临挑战.
- 传统的算法与未知的目标转速和中心频率作斗争.
研究的目的:
- 开发一种先进的算法,用于水下强大的目标运动分析.
- 加强对表现出复杂机动的不合作目标的追踪.
主要方法:
- 提出了联合估计的适应性无气味卡尔曼波器 (JE-AUKF).
- 将中心频率和转速集成到适应式建模的状态向量中.
- 采用时间变化的色因子来增强工艺噪声共变性.
主要成果:
- 与现有方法相比,JE-AUKF表现出优越的跟踪性能.
- 该算法在模拟和海上试验中有效处理不合作的机动目标.
结论:
- JE-AUKF算法在被动声纳目标跟踪方面取得了重大进展.
- 这种方法提供了更准确,更可靠的定位和跟踪潜水目标的机动.
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