Coded Optical Localization Arrays Beyond Pixel-Density Limits
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Solution Overview
Problem
Existing 3D object localization and orientation systems face limitations in precision, size, weight, and power due to the reliance on spatial density of image sensing pixels and baseline distances between sensors, which restricts their performance and efficiency.
Innovation Solution
The implementation of coded localization systems, where an optical array of elements is arranged similarly to radar systems, with overlapping field patterns and applied mathematical weights, allowing for improved localization precision independent of pixel density and sampling, reducing system size, weight, and power while maintaining high performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the spatial density of image sensing pixels is increased to improve localization precision, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent replaces traditional mechanical/optical imaging systems that rely on dense pixel arrays with a coded aperture system combined with computational algorithms. Instead of using many physical sensors to achieve high resolution, the system uses a coded mask that modulates the light field, followed by computational reconstruction algorithms that decode the spatial information. This substitution of physical sensing density with computational processing resolves the contradiction by achieving high localization precision without increasing pixel density.
Solution Approach 2:
The patent changes the fundamental parameter from spatial sampling density to coded aperture pattern design. By using specific coded patterns (such as random masks, binary masks, or phase masks) in the aperture plane, the system encodes spatial information in a way that allows precise localization through computational decoding. This parameter change enables high measurement precision while maintaining low device complexity, as the coded aperture can be implemented with simple binary patterns rather than requiring dense sensor arrays.
2Measurement precision
If the baseline distance between sensors is increased to improve 3D localization precision, then measurement precision improves, but device complexity and size increase
Solution Approach 1:
The patent replaces traditional stereo vision systems that require large baseline distances between multiple cameras with a single-camera coded aperture system. By using a coded mask in front of a single sensor array, the system captures multiple coded projections of the 3D scene simultaneously. Computational algorithms then reconstruct the 3D localization information from these coded projections, eliminating the need for large physical baselines while achieving comparable or superior precision.
Solution Approach 2:
The patent transitions from a 2D sensor plane to a 3D coded aperture space. By placing the coded mask in the aperture plane (before the sensor) rather than processing only 2D image data, the system encodes depth and 3D spatial information directly into the light field modulation. This dimensional change allows a single sensor to capture 3D localization data that would traditionally require multiple sensors separated by large baselines.
3Measurement precision
If more antenna elements are used in radar systems to improve angular estimation accuracy, then measurement precision improves, but size, weight and power increase
Solution Approach 1:
The patent applies coded aperture principles to optical imaging systems, creating an analogy with radar's signal processing approaches. Instead of using many physical antenna elements to achieve high angular resolution, the system uses a coded aperture mask that modulates the optical field, combined with computational algorithms that decode the angular information. This substitution reduces the physical size and weight of the system while maintaining high measurement precision through computational processing rather than physical array expansion.
Data Source
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AI summary
A coded localization system includes a plurality of optical channels arranged to cooperatively image at least one object onto a plurality of detectors. Each of the channels includes a localization code that is different from any other localization code in other channels, to modify electromagnetic energy passing therethrough. Output digital images from the detectors are processable to determine sub-pixel localization of the object onto the detectors, such that a location of the object is determined more accurately than by detector geometry alone. Another coded localization system includes a plurality of optical channels arranged to cooperatively image partially polarized data onto a plurality of pixels. Each of the channels includes a polarization code that is different from any other polarization code in other channels to uniquely polarize electromagnetic energy passing therethrough. Output digital images from the detectors are processable, to determine a polarization pattern for a user of the system.