通过增强的PSO优化在异步声学网络中的轨迹规则化定位
Jingyi Zhou1, Qiushi Zhao1, Zihan Feng1
1Sichuan University Pittsburgh Institute, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种新的声学定位框架,用于快速移动的目标,提高了在具有挑战性的室内环境中的精度. 该方法通过分析整个轨迹,而不仅仅是单个点来增强实时跟踪.
科学领域:
- 声学信号处理 声学信号处理
- 室内定位系统的内部定位系统
- 运动追踪器是什么?
背景情况:
- 由于非视线 (NLOS) 传播和有限的传感器 () 放置,很难在室内定位快速移动的目标.
- 现有的方法在异步数据和稀疏部署方面扎.
研究的目的:
- 开发一个强大的轨迹重建框架,用于室内声学定位.
- 为了克服NLOS传播的局限性和对快速移动的目标的稀疏部署.
主要方法:
- 一个整合到达时间 (ToA) 和到达频率 (FoA) 测量的新框架.
- 在整个轨迹上进行受约束的非线性最小平方优化,结合时间连续性和运动动态.
- 一个增强的粒子群集优化 (PSO) 算法,具有自适应相位切换和抗噪音更新.
主要成果:
- 与传统最小平方估计器相比,其精度和稳定性更高,特别适用于高速轨迹.
- 现实世界的实验表明,在3米以内,超过90%的定位错误.
- 该方法在异步和散环境中被证明是有效的.
结论:
- 拟议的轨迹重建框架显著提高了快速移动目标的室内声学定位.
- 基于模型,无培训的方法为具有挑战性的环境提供了可扩展和有效的解决方案.
- 整合ToA/FoA和先进的PSO提供了一个强大而准确的本地化系统.
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