Multi-phenomenology Object Detection via Centroid Comparison
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Solution Overview
Problem
Current techniques face limitations in resolving closely spaced objects due to diffraction limitations, signal to noise ratio, and pixel sample size, especially when one object is larger or brighter than others, making it difficult to accurately detect and characterize multiple objects in space situational awareness and astronomy applications.
Innovation Solution
The method involves imaging a target area with an array to detect different image characteristics of electromagnetic radiation, computing centroids for these characteristics, and comparing their locations to resolve the number of objects, utilizing various phenomenologies such as polarimetry and spectroscopy to enhance detection capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging techniques are used to detect closely spaced objects, then the detection process is simple, but the resolution is limited by diffraction and cannot distinguish closely spaced objects
Solution Approach 1:
The patent segments the imaging process into multiple independent measurements of different image characteristics (intensity, polarization, spectrum, time-lapse) rather than relying on a single measurement. Each characteristic provides independent information about the objects, allowing the system to resolve closely spaced objects by comparing multiple segmented measurements rather than attempting to resolve them in a single complex image.
Solution Approach 2:
The patent transitions from two-dimensional spatial imaging to multi-dimensional measurement by incorporating additional image characteristics (polarization state, spectral content, temporal variations). This dimensional expansion allows resolution of objects that are indistinguishable in the traditional two-dimensional image plane, as the additional dimensions provide independent information about object locations and characteristics.
2Measurement precision
If hypothesis testing techniques like Pixon method are used to resolve images beyond Rayleigh limit, then resolution improves, but computational complexity increases significantly requiring extensive computation capabilities
Solution Approach 1:
The patent extracts and measures multiple independent image characteristics (intensity, polarization, spectrum, time-lapse variations) separately rather than attempting to solve the full inverse problem in one complex computation. By taking out and measuring each characteristic independently, the system avoids the computationally intensive hypothesis testing required by methods like Pixon, while still achieving super-resolution through comparison of these extracted characteristics.
Solution Approach 2:
The patent allows the natural variations in different image characteristics to provide the resolving information automatically. By measuring how different characteristics (polarization, spectrum, time-lapse) naturally vary across the field of view, the system self-determines object locations and characteristics without requiring extensive computational hypothesis testing to reconstruct the image.
3Measurement precision
If single image characteristic detection is used, then the detection process is simple and fast, but the ability to resolve closely spaced objects is limited
Solution Approach 1:
The patent makes the imaging array multi-functional by enabling it to detect multiple image characteristics (intensity, polarization, spectrum, time-lapse) using the same physical array. This universality allows the system to resolve closely spaced objects by comparing measurements from the same array across different characteristics, avoiding the need for multiple separate imaging systems while still achieving enhanced resolution.
Data Source
AI summary
Method and system for utilizing multiple phenomenological techniques to resolve closely spaced objects during imaging includes detecting a plurality of closely spaced objects through the imaging of a target area by an array, and spreading electromagnetic radiation received from the target area across several pixels. During the imaging, different phenomenological techniques may be applied to capture discriminating features that may affect a centroid of the electromagnetic radiation received on the array. Comparing the locations of the centroids over multiple images may be used to resolve a number of objects imaged by the array. Examples of such phenomenological discriminating techniques may include imaging the target area in multiple polarities of light or in multiple spectral bands of light. Another embodiment includes time-lapse imaging of the target area, to compare time lapse centroids for multiple movement signal characteristics over pluralities of pixels on the array.