Underwater Geolocalization with Bioinspired Polarization Sensors and Algorithms
Abstract
Precise and continuous underwater geolocalization is an important tool for fast and reliable navigation across the ocean waters. Since satellite-based global positioning system signals do not penetrate water, alternative methods have been developed for underwater navigation. Today~s underwater geolocalization and navigation rely on data collected from various sensors, such as Doppler sonars, time-of-flight acoustic signals, inertial measurement units, optical gyrocompasses, pressure depth sensors, landmark identification navigation and others. Despite these technological advancements, there are many instances when submarines, UUVs and scuba divers do not have geolocalization capabilities, which are of critical importance for underwater navigation and geolocalization. Therefore, there is an urgent need for a robust and disruptive geolocalization technology for underwater geolocalization which can operate under a variety of conditions such as night or day, windy or calm, fast or slow current and shallow or deep (up to 200 m) waters. Recently, passive underwater geolocalization technology based on the in-water polarization information was developed by Prof. Gruev~s research group. Polarized light manifests prominently in shallow underwater environments. Light from the sun and sky is selectively refracted at the water~s surface and scatters within the water, creating complex patterns of polarization states in the underwater light field. Based on our preliminary data, we can infer the apparent heading and elevation angles of the sun with an average angular error of 0.38~, which translates to global position estimation error of 61 km when the sun is at least 40~ above the horizon. Sensitivity measurements indicate that our bio-inspired instrument can detect differences between polarization patterns of two sites that are 6 km apart with 99% confidence. The one order of magnitude difference between the sensitivity of our instrument and global position estimation accuracy warrants the development of new algorithms to improve geolocalization estimation. We propose to develop a low-noise, bio-inspired polarization imaging sensor and novel machine learning algorithms that will enable passive underwater geolocalization with at least 1 km accuracy. The proposed method will enable underwater geolocalization during sunny days at depths up to 200 m and during partial moonlight conditions during the night at depths up to 2 m, without the need of satellite-based GPS. We will focus on the following three specific aims to realize this disruptive technology. The first aim is to develop a working prototype of compact, very sensitive polarization image sensor having the following specifications: readout noise of ~ 1 electron, quantum efficiency of >60% at 450 nm, spatial resolution of at least 1 megapixel, pixel pitch of ~6 ~m, 40 frames per second readout rate, and extinction ratios of at least 1000. The second aim is to develop new machine learning algorithms that will consider the temporal aspects of the underwater polarization information to improve its geolocalization. The third aim is to develop machine learning algorithms that will take into account the noise of the imaging devices to estimate the polarization information and use it for geolocalization purposes. The proposed system will be deployed at various locations around the world and geolocalization accuracy will be evaluated during the day at depths up to 200 m and night at depths up to 2 m during different phases of the moon. The proposed technology can mitigate current problems with underwater navigation and provide mission-critical geolocation information to divers and UUVs in real-time. The abstract is approved for public release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 13, 2019
- Source ID
- N000141912400
Entities
People
- Viktor Gruev
Organizations
- Office of Naval Research
- United States Navy
- University of Illinois Urbana–Champaign