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在单个观察者被动定位中用于速度向量估计的次优化优化策略
Shuyi Gu1,2, Zhenghua Luo1,2,3, Yingjun Chu2,3
1School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究提出了一种新的亚最佳估计方法,用于在被动定位系统中独立确定目标速度. 该方法通过动态加权优化标准来提高准确性和稳定性,以实现更快的融合.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 机器人技术 机器人技术 机器人技术
背景情况:
- 单个观察者被动定位系统同时估计目标位置和速度.
- 同时估计可能会导致相关错误,扭曲结果并需要独立估计.
- 准确的速度估计对于有效的被动定位至关重要.
研究的目的:
- 在单个观察者被动本地化中引入一种新的亚最佳估计策略,用于独立的速度向量的估计.
- 为了解决传统的同时估计方法中相关错误的局限性.
- 为了提高被动定位系统的准确性和稳定性.
主要方法:
- 开发了一个次优估计策略,将速度估计重新定义为全球优化搜索.
- 从初始解决方案空间实现了多个优化标准的动态权重.
- 使用模拟模型验证了对均运动和恒定加速度目标轨迹的方法.
主要成果:
- 与传统方法相比,拟议的次优估计方法显示了较快的趋同.
- 在速度向量的估计中取得了更高的准确性.
- 在不同的运动模型中表现出强大的稳定性.
结论:
- 非最佳估计策略为被动本地化中的独立速度估计提供了显著的进步.
- 该方法具有动态权重标准的能力,确保了更好的性能和可靠性.
- 这种方法提高了单个观察者被动定位系统的整体有效性.
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