Optical Compressive Sensing With Spatial Filtering for Wide-Area Coverage
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Solution Overview
Problem
Traditional active remote sensing systems require high optical energy and have large Size, Weight, and Power (SWaP) due to linear scaling with covered area, leading to high costs and inefficiencies in wide area coverage.
Innovation Solution
The implementation of active optical compressive sensing techniques using spatial filtering patterns to generate sparse scattered data, allowing for logarithmic resource scaling and significant reduction in transmitter requirements, enabling direct measurement of sparse data with orders-of-magnitude less energy for wide area coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional active remote sensing systems use linear scaling with covered area, then measurement precision and coverage area are achieved, but use of energy and device complexity increase significantly
Solution Approach 1:
The patent segments the illumination process by using multiple distinct illumination patterns (e.g., different spatial distributions, wavelengths, or temporal sequences) to probe different aspects of the scene. Each pattern provides partial information that, when combined, reconstructs the full scene with fewer total measurements than traditional pixel-by-pixel scanning, thereby reducing optical energy consumption while maintaining coverage area.
Solution Approach 2:
The patent transitions from direct spatial measurement to measurement in a transformed domain by applying compressive sensing theory. Instead of measuring each spatial location independently (linear scaling), the system measures compressed projections of the entire scene using random or structured illumination patterns, then reconstructs the image computationally. This dimensional transformation enables sub-linear scaling of energy consumption with coverage area.
2Area of stationary object
If traditional active remote sensing systems increase transmitter power for wide area coverage, then coverage area improves, but size, weight and power (SWaP) increase
Solution Approach 1:
The patent divides the wide area coverage task into multiple sequential measurements using different illumination patterns rather than attempting to illuminate and detect the entire area simultaneously with high power. This temporal and spatial segmentation allows the use of lower power transmitters while achieving the same total coverage through multiple low-power measurement passes.
Solution Approach 2:
The patent measures the scene in a compressed sensing domain rather than direct spatial domain, allowing wide area coverage to be achieved with fewer measurements. This reduces the total energy required from the transmitter while maintaining coverage area, directly addressing the SWaP constraint by reducing power requirements.
3Measurement precision
If traditional active remote sensing systems use flying-spot or multipixel receiver architectures, then measurement precision is maintained, but use of energy remains high with only power versus time tradeoff
Solution Approach 1:
The patent merges multiple measurement functions into a single measurement by using compressive sensing to capture compressed representations of the scene that contain sufficient information for reconstruction. Instead of using separate measurements for each spatial location (as in flying-spot or multipixel systems), the system combines information from multiple illumination patterns into a single compressed measurement set, reducing total energy while maintaining measurement precision through computational reconstruction.
Solution Approach 2:
The patent transforms the measurement problem from direct spatial sampling to compressed domain sampling, allowing the system to achieve the same measurement precision with fewer total photons. This dimensional transformation enables energy reduction while maintaining signal-to-noise ratio by measuring in a domain where the scene sparsity can be exploited.
4Measurement precision
If traditional active remote sensing systems collect full data for image generation, then measurement precision is achieved, but data processing requirements and energy consumption increase
Solution Approach 1:
The patent segments the data acquisition process to collect only the essential information needed for image reconstruction by using compressive sensing measurements. Instead of collecting redundant data from every pixel location, the system collects a smaller set of compressed measurements that, when processed through reconstruction algorithms, preserve the essential scene information with high fidelity, thereby reducing data volume while maintaining image quality.
Solution Approach 2:
The patent measures the scene in a compressed sensing domain rather than direct spatial domain, inherently acquiring data in a compressed form that requires less storage and processing. This dimensional transformation allows the system to maintain measurement precision and image quality while significantly reducing the volume of data that must be processed and transmitted, effectively managing information loss through intelligent sampling.
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
This approach reduces the total energy and data requirements for active optical sensing systems, achieving efficient wide area coverage with minimal system changes, providing orders-of-magnitude improvements in energy and data processing efficiency.
Implementation Method 1
an optical source to generate light for illuminating a target
Implementation Method 2
A pattern generator to generation of a pattern. A pattern controller controls an operation of the pattern generator to cause generating a desired pattern. The pattern is a spatial filtering pattern that enables data compression by generating sparse scattered data.
Implementation Method 3
an optical mixer to receive the source light and the modulated light in two different paths and to generate a phase-error signal
Implementation Method 4
Relay optics projects the modulated light onto a target and relays resulting scattered light from the target to an optical heterodyne detector to generate a detector signal
Data Source
AI summary
An active optical compressive sensing system includes an optical source to generate light for illuminating a target and a pattern generator to generate a pattern. A pattern controller controls an operation of the pattern generator to cause generation of a desired pattern. The pattern is a spatial filtering pattern that enables data compression by generating sparse scattered data. Applying the pattern allows a logarithmic resource scaling.