Marine Physical Laboratory, Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA.
J Acoust Soc Am. 2010 Jul;128(1):75-87. doi: 10.1121/1.3438475.
A particle filtering (PF) approach is presented for performing sequential geoacoustic inversion of a complex ocean acoustic environment using a moving acoustic source. This approach treats both the environmental parameters [e.g., water column sound speed profile (SSP), water depth, sediment and bottom parameters] at the source location and the source parameters (e.g., source depth, range and speed) as unknown random variables that evolve as the source moves. This allows real-time updating of the environment and accurate tracking of the moving source. As a sequential Monte Carlo technique that operates on nonlinear systems with non-Gaussian probability densities, the PF is an ideal algorithm to perform tracking of environmental and source parameters, and their uncertainties via the evolving posterior probability densities. The approach is demonstrated on both simulated data in a shallow water environment with a sloping bottom and experimental data collected during the SWellEx-96 experiment.
提出了一种粒子滤波（PF）方法，用于使用移动声源对复杂海洋声环境进行序贯水声反演。该方法将声源位置处的环境参数（例如水柱声速剖面（SSP）、水深、沉积物和底部参数）和声源参数（例如声源深度、范围和速度）视为随声源移动而演变的未知随机变量。这允许实时更新环境并准确跟踪移动声源。作为一种在具有非高斯概率密度的非线性系统上运行的顺序蒙特卡罗技术，PF 是通过随时间演变的后验概率密度来跟踪环境和声源参数及其不确定性的理想算法。该方法在具有倾斜底部的浅水环境中的模拟数据以及在 SWellEx-96 实验中收集的实验数据上进行了演示。
