Marine Physical Laboratory, Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA.
J Acoust Soc Am. 2013 Aug;134(2):971-81. doi: 10.1121/1.4807819.
Sequential Bayesian methods such as particle filters have been used to track a moving source in an unknown and space/time-evolving ocean environment. These methods treat both the source and the ocean parameters as non-stationary unknown random variables and track them via the multivariate posterior probability density function. Particle filters are numerical methods that can operate on nonlinear systems with non-Gaussian probability density functions. Particle smoothers are a natural extension to these filters. A smoother is appropriate in applications where data before and after the time of interest are readily available. Both past and "future" measurements are exploited in smoothers, whereas filters just use past measurements. Geoacoustic and source tracking is performed here using two smoother algorithms, the forward-backward smoother and the two-filter smoother. Smoothing is demonstrated on experimental data from both the SWellEx-96 and SW06 experiments where the parameter uncertainty is reduced relative to just filtering alone.
序贯贝叶斯方法（如粒子滤波器）已被用于在未知的时变海洋环境中跟踪移动声源。这些方法将源和海洋参数都视为非平稳未知随机变量，并通过多元后验概率密度函数对其进行跟踪。粒子滤波器是一种可用于具有非高斯概率密度函数的非线性系统的数值方法。粒子平滑器是对这些滤波器的自然扩展。在感兴趣时间前后的数据易于获得的应用中，平滑器是合适的。平滑器既利用过去的测量值，也利用“未来”的测量值，而滤波器仅使用过去的测量值。本文使用两种平滑器算法（前向后向平滑器和双滤波器平滑器）进行了水声和源跟踪。在 SWellEx-96 和 SW06 实验的实验数据上进行了平滑处理，与仅滤波相比，参数不确定性有所降低。
