高分辨率的亚齐木斯估计方法基于一个压力梯度MEMS矢量声
Xiao Chen1, Ying Zhang2, Yujie Chen3
1Information Science and Technology College, Dalian Maritime University, Dalian 116026, China.
Micromachines
|February 27, 2026
概括
一种新方法使用了压力梯度微电机系统 (MEMS) 矢量水电机和改进的粒子群优化 (IPSO) 算法,用于精确的水下目标定位. 这种方法显著提高了近视角估计的准确性和在海洋环境中的系统稳定性.
科学领域:
- 声学和信号处理
- 海洋技术 海洋技术
- 传感器系统 传感器系统
背景情况:
- 采用单压梯度微电机系统 (MEMS) 矢量水声器的常规方位估计方法在实现高分辨率定位方面存在局限性.
- 现有的算法,如多重信号分类 (MUSIC) 和复杂声学强度 (CAI),在实际的海洋环境中遭受性能退化和高分辨率值.
研究的目的:
- 为压力梯度MEMS矢量水声器开发一个高分辨率的 азимут估计方法.
- 解决复杂海洋环境中传统方法的局限性,提高水下探测能力.
主要方法:
- 建立了MEMS矢量水力电话的声压和粒子速度之间的交叉光谱模型.
- 集成了一种改进的粒子群优化 (IPSO) 算法,用于方程估计.
- 为性能分析推导了克拉梅尔-拉奥边界.
主要成果:
- 拟议的算法准确地分辨了两个以5°相隔的目标,其SNR为5dB,根平均平方误差为~0.35°,成功率为100%.
- 与CAI和MUSIC算法相比,显示了较低的分辨率值和更高的估计准确性.
- 通过模拟和实地测试在实际海水环境中验证了高分辨率性能.
结论:
- 拟议的IPSO集成方法提供了一个强大的,准确的解决方案,用于使用MEMS矢量水声机进行高分辨率的近视距离估计.
- 该算法的低计算复杂性使得水下检测的高效实时处理成为可能.
- 这种进步为复杂的海洋环境中的多目标场景带来了巨大的潜力,提高了系统的稳定性.
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