Abstract
Both source localization and environmental inversions are practical problems for long-standing applications in underwater acoustics. This paper presents an approach of the moving source localization and sound speed field (SSF) inversion in shallow water. The approach is formulated in a state-space model with a state equation for both the source parameters (e.g., source depth, range, and speed) and SSF parameters (first three empirical orthogonal function coefficients, EOFs) and a measurement equation that incorporates underwater acoustic information via a vertical line array (VLA). As a sequential processing algorithm that operates on nonlinear systems with non-Gaussian probability densities, an improved sequential importance resampling type particle filtering (SIR PF) is proposed to counter degeneracy. The improved PF performs tracking of source and SSF parameters simultaneously, and evaluates their uncertainties in the form of time-evolving posterior probability densities (PPDs). The performance of improved PF is illustrated with well-tracked simulations of real-time source localization and time-varying SSF inversion. Moreover, the influence of different particle numbers on PF tracking accuracy and computational cost is also demonstrated. Simulation results show that the high-particle-number PF has an outperform performance. For a given hardware system, the reasonable compromise between accuracy and computational cost is a matter of tradeoff.
Highlights
In underwater acoustic applications, the passive source localization/tracking and ocean parameters inversion are always hot issues of serious concerned [1]–[5]
AND DISCUSSION simulation studies are carried out to demonstrate the performance of the proposed approach, consisting of the following contents: tracking source and sound speed field (SSF) parameters, evaluating how the PF tracks their uncertainties as posterior probability densities (PPDs), and the results of real-time source localization and time-varying SSF inversion
TRACKING PARAMETERS OF THE MOVING SOURCE AND SSF To synthesize the acoustic pressure data used in the measurement equation, the environment model used is shown in Figure 1, the simulation setup involving a fixed vertical line array (VLA) and moving source in a range-dependent shallow water environment
Summary
The passive source localization/tracking and ocean parameters inversion are always hot issues of serious concerned [1]–[5]. M. Dai et al.: Improved Particle Filtering Technique for Source Localization and SSF Inversion in Shallow Water physical variability and dynamic processes cause the environmental parameters (including the SSP) in the propagation change in time and space [12]. Dai et al.: Improved Particle Filtering Technique for Source Localization and SSF Inversion in Shallow Water physical variability and dynamic processes cause the environmental parameters (including the SSP) in the propagation change in time and space [12] These characteristics of stepwise variability can be considered as a tracking problem, which is suitable for the sequential Bayesian algorithm to deal with [13]. THEORY the theory of the acoustic propagation model as well as the SSF parameterization is summarized, and the state-space model for tracking is given, along with the algorithm of improved SIR PF
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