Iterative Sensor Matrix Layout for Non-Uniform Illumination
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Solution Overview
Problem
Existing methods for creating matrices of sensors or antennas with specific properties and orientations are susceptible to errors due to uneven illumination, particularly under non-uniform lighting conditions, which cannot be easily distinguished from polarization information, leading to incorrect signal detection.
Innovation Solution
A method involving iterative application of modification and expansion operators to form larger matrices from smaller ones, combined with virtual experiments to evaluate and select matrices with the smallest error signals, ensuring uniform illumination and minimizing sensitivity to local disturbances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sensors are arranged in a simple configuration, then device complexity is reduced, but measurement precision deteriorates under non-uniform illumination
Solution Approach 1:
The sensor array is divided into multiple groups or blocks, where each group contains sensors with specific orientations. This segmentation allows independent optimization of each group's response to local illumination variations while maintaining overall system simplicity.
Solution Approach 2:
The patent employs asymmetric sensor distributions within groups, where sensors are not uniformly spaced but positioned to compensate for expected illumination gradients. This asymmetric arrangement cancels out first-order gradient effects without requiring complex overall system design.
2Quantity of substance
If the sensor array covers a larger area, then signal strength is improved, but sensitivity to illumination gradients increases
Solution Approach 1:
Different regions of the sensor array are assigned different sensor orientations and densities tailored to local illumination conditions. Areas with higher gradient exposure receive sensors oriented to compensate for those specific gradients, while uniform areas use standard configurations.
Solution Approach 2:
The patent introduces a spatial frequency dimension by arranging sensors at multiple scales - both individual sensor positions and group patterns are optimized to respond to different spatial frequencies of illumination variations, effectively filtering out gradient effects.
3Measurement precision
If surrounding brightness sensors are added to compensate for gradients, then measurement precision improves, but device complexity increases
Solution Approach 1:
Polarization-sensitive sensors and brightness sensors are merged into unified sensor groups where both types of measurements are taken simultaneously by the same physical structures, eliminating the need for separate compensation hardware.
Solution Approach 2:
The sensor elements are designed to perform multiple functions - detecting both polarization state and local illumination intensity - allowing a single sensor configuration to serve both measurement and gradient compensation purposes without additional components.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively reduces the impact of local intensity fluctuations, enhancing the accuracy of sensor arrays by iteratively optimizing their arrangement to minimize errors under varying illumination conditions.
Implementation Method 1
The basic measuring principle, based on the Malus law, can be demonstrated with just one polarization-sensitive single sensor.
Data Source
AI summary
A method for iteratively generating a matrix of base elements that includes forming at least one base matrix, applying modification operators iteratively, which are able to transform the base matrix or any matrix arising therefrom into a modified matrix, applying expansion operators iteratively, which form a larger matrix from a plurality of optionally modified smaller matrices from the preceding iteration by copying, rotation or reflection by virtue of parts of the larger matrix being filled with the optionally modified smaller matrices, performing virtual experiments within the scope of which the properties of a created matrix are examined by systematic creation of values deliberately containing errors from which an error signal is derivable, and in order to create a complex matrix, forming all permutations of next larger matrices by applying expansion operators and then evaluating by means of virtual experiments, the next larger matrices selected with the smallest error signals, and then forming the next larger matrices successively therefrom by applying expansion operators and evaluating by means of virtual experiments until the error signal drops below a given limit.