Accurate navigation of autonomous underwater vehicles (AUVs) is a key task for data collection at sea with high resolution in time and space. Sidescan sonar (SSS), originally developed for imaging the seafloor, has high potential for establishing landmark-aided navigation or simultaneous localization and mapping (SLAM) capabilities on small-scale AUVs. A key task to establish these capabilities is to determine the height of the AUV from the seafloor, also referred to as “flight height.” This paper combines image processing techniques with probabilistic data association to detect and filter AUV flight height from SSS data. The proposed method first aims to detect the edge between the water column and the seabed using image processing techniques. (The pixel index of this edge is proportional to the flight height in meters.) Subsequently, a multisensor probabilistic data association filter (PDAF) fuses the resulting flight height detections computed from images provided by port and star-board SSS transducers to effectively mitigate missed detections and false positives. To facilitate deployments, we evaluate the performance of the proposed approach using real data collected by surface vehicles with SSS and demonstrate accurate flight height estimation from noisy SSS images.

This paper introduces a statistical model and corresponding sequential Bayesian estimation method for terrain-based navigation using sidescan 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.