Navigation is a challenging problem, particularly in the underwater marine environment. The maximum visibility in pure water is 80 m but such clarity is rarely present in nature, limiting the utility of vision-based navigation techniques to much shorter distances in clear waters. A further challenge is the lack of visual features in open waters. Using an underwater polarization video system inspired by the mantis shrimp eye, we have previously shown that it is possible to bypass this limitation and perform celestial navigation without direct observation of the sun. The heading and elevation of the sun can be inferred from polarization angle measurements of the scattered, in-water light field. Our proof-of-concept system achieved an accuracy of 61 km in global positioning, or 0.38° in heading. To reduce the material, computational, and energy costs of the system, we have applied the biological design principles of sparsity and matched filtering. That is, we sense and compute the minimum information necessary for specific levels of accuracy and our sensors are tuned to the most informative set of signals in the environment. This work brings us closer to a practical realization of a new method for long-distance navigation in underwater vehicles, without the need to surface, and shows how biological design principles can ease requirements for real-time and resource-constrained systems.
Polarization information is abundant in nature, including the underwater environment. Polarization of light in the underwater environment is due to light coming from both the sun and from the sky. Hence, the underwater polarization is primarily determined by light’s transmission from air to water and in-water scattering. In this talk, we will present a new framework to solve sun’s position (heading and elevation) using background underwater polarization information. Based on this data, the underwater geo location of an observer can be determined passively. Extensive experimental data will be presented in the talk to demonstrate the accuracy of this method.
Multiple parallel channels are ubiquitous in optical communications, with spatial division multiplexing (separate physical paths) and wavelength division multiplexing (separate optical wavelengths) being the most common forms. Here, we investigate the viability of polarization division multiplexing, the separation of distinct parallel optical communication channels through the polarization properties of light. Two or more linearly polarized optical signals (at different polarization angles) are transmitted through a common medium, filtered using aluminum nanowire optical filters fabricated on-chip, and received using individual silicon photodetectors (one per channel). The entire receiver (including optics) is compatible with standard CMOS fabrication processes.
The filter model is based upon an input optical signal formed as the sum of the Stokes vectors for each individual channel, transformed by the Mueller matrix that models the filter proper, resulting in an output optical signal that impinges on each photodiode. The results show that two- and three-channel systems can operate with a fixed-threshold comparator in the receiver circuit, but four-channel systems (and larger) will require channel coding of some form. For example, in the four-channel system, 10 of 16 distinct bit patterns are separable by the receiver. The model supports investigation of the range of variability tolerable in the fabrication of the on-chip polarization filters.
Polarization imaging sensors using the division-of-focal-plane paradigm have recently emerged on the market. These sensors, due to their compact design, are ideal for field work. One of the major drawbacks in these sensors is the spatial variation of the optical response of individual pixels across the imaging array. These spatial variations are due to variations in the nanowires of the pixelated polarization filters. In this paper, we describe and compare two methods for calibrating a division of focal plane sensors. We present theoretical and experimental data for these calibration methods.
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