A New Statistical Model for Waveguide Invariant-Based Range Estimation in Shallow Water

In the undersea environment, positioning is often challenging, and thus, ranging based on passive acoustic data can provide valuable information for subsurface navigation and source localization. We present a range estimation method based on the waveguide invariant (WI) theory that uses ship noise recorded underwater as an acoustic source. The WI is a scalar parameter that describes the interference patterns in spectrograms caused by pairs of modes of acoustic waves propagating in a waveguide, e.g., in shallow water. The WI theory enables ranging using a single receiver without a detailed knowledge of the environment. The machinery of a large ship induces broadband background noise characterized by sets of prominent narrowband tonal signals. We develop a likelihood function for a WI-based range estimation by introducing a statistical model for the broadband component when it dominates over the background noise. The capability of the resulting range estimation method is demonstrated on real acoustic measurements of a moving container ship recorded during the Seabed Characterization Experiment 2017 (SBCEX17).

Toward Terrain-based Navigation Using Side-scan Sonar

This paper introduces a statistical model and corresponding sequential Bayesian estimation method for terrain-based navigation using side-scan sonar (SSS) data. The presented approach relies on slant range measurements extracted from the received ping of a SSS. In particular, incorporating slant range measurements to landmarks for navigation constrains the location and altitude error of an autonomous platform in GPS-denied environments. The proposed navigation filter consists of a prediction step based on the unscented transform and an update step that relies on particle filtering. The SSS measurement model aims to capture the highly nonlinear nature of SSS data while maintaining reasonable computational requirements in the particle-based update step. For our numerical results, we assume a scenario with a surface vehicle that performs SSS and compass measurements. The simulated scenario is consistent with our current hardware platform. We also discuss how the proposed method can be extended to autonomous underwater vehicles (AUVs) in a straightforward way and why the combination of SSS sensor and compass is particularly suitable for small autonomous platforms.

Navigation in shallow water using passive acoustic ranging

Passive acoustics can provide a variety of capabilities with applications in oceanographic research and maritime situational awareness. In this paper, we develop a method for the navigation of autonomous underwater vehicles (AUVs) in shallow water. Our approach relies on passively recorded signals from acoustic sources of opportunities (SOOs). By making use of the waveguide invariant, a measurement of the range to the SOO is extracted from the spectrogram of a single hydrophone. Range extraction requires knowledge of the range rate, i.e., the radial velocity between the SOO and AUV, computed from the pressure fields at different time intervals. A particle-based navigation filter fuses the range measurements with the AUV's internal velocity and heading measurements. As a result, the position error, which would otherwise increase over time, can be bounded. The ability to compute the range rate and range measurements from the pressure field measured using a single hydrophone is demonstrated on real data from the SWellEx-96 experiment. The capability of the developed navigation filter is shown based on synthetic data generated by the normal mode program KRAKEN.