Passive Multispectral Scanner Using Recursive Adaptive Sampling
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Solution Overview
Problem
Passive surveillance systems face challenges in efficiently scanning across a wide spectrum of electromagnetic frequencies due to the time-consuming nature of capturing and analyzing massive data sets, leading to either active transmission detection or limited frequency range monitoring.
Innovation Solution
A passive multispectral scanner employing recursive adaptive sampling, which uses directional antennas, an aiming system, a frequency selection system, and a controller to intelligently sample signal strengths across a wide range of frequencies and directions, subdividing regions with high gradient magnitudes until a resolution limit is reached, and subtracting background noise for effective object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If passive surveillance systems scan across a wide spectrum of electromagnetic frequencies, then the detection capability is improved, but the data processing time increases significantly
Solution Approach 1:
The patent divides the wide frequency spectrum into multiple discrete frequency bands or channels. Instead of analyzing the entire spectrum continuously, the system segments it into manageable portions that can be processed independently and in parallel, significantly reducing the overall data processing time while maintaining comprehensive detection capability across all frequencies.
Solution Approach 2:
The system implements adaptive sampling that focuses computational resources on specific frequency regions where signals are detected or where gradients are high, rather than uniformly processing all frequency bands. This partial action approach concentrates processing power where needed most, reducing total processing time while maintaining detection effectiveness.
2Area of stationary object
If passive surveillance systems capture signals from a large number of directions, then the coverage area is improved, but the data volume increases significantly
Solution Approach 1:
The patent divides the 360-degree azimuth and elevation ranges into discrete angular bins or sectors, creating a grid structure for spatial sampling. This segmentation allows the system to process angular space in manageable discrete units rather than as a continuous infinite domain, reducing the effective data volume while preserving comprehensive directional coverage.
Solution Approach 2:
The system applies adaptive sampling in the angular domain by identifying regions with high signal gradients and concentrating sampling efforts in those specific directional sectors. Areas with low or no signals receive reduced sampling density, thereby reducing total data volume while maintaining detection capability in critical directions.
3Measurement precision
If passive surveillance systems analyze signals in a large number of frequency bands, then the spectral resolution is improved, but the computational complexity increases significantly
Solution Approach 1:
The patent divides the frequency spectrum into multiple discrete bands or channels, allowing parallel processing of each band independently. This segmentation transforms a single complex high-resolution spectral analysis problem into multiple simpler parallel tasks, reducing the computational complexity of each individual task while maintaining overall spectral resolution through the combination of all bands.
Solution Approach 2:
The system implements frequency-domain adaptive sampling by concentrating computational resources on frequency regions where signals are present or where spectral gradients are high. Frequency regions with no signals or flat spectra receive minimal processing, reducing total computational complexity while maintaining high spectral resolution where it matters most for detection.
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 significantly reduces data processing time and resources, making it feasible to scan large areas across multiple frequencies, enhancing the detection of objects by focusing on high gradient regions and minimizing background noise interference.
Implementation Method 1
The antennas may be configured to receive electromagnetic signals within a total frequency range
Implementation Method 2
The aiming system may be configured to aim the antennas to receive signals from sources emitting at an azimuth angle and an elevation angle
Implementation Method 3
The frequency selection system may be configured to obtain a signal strength in many frequency subranges, where each frequency subrange lies within the total frequency range
Implementation Method 4
For each polygon and for each frequency subrange, the controller may estimate the gradient magnitude of the signal strength in that frequency subrange within the polygon
Data Source
AI summary
A system that passively scans for electromagnetic radiation across a potentially wide area and wide spectrum to identify objects in the area based on their emission spectra. The system may use antennas that can be aimed to sweep across a large range of azimuth and elevation angles, and frequency selectors that measure signals in a large set of frequency bands. To make scanning this large space feasible, the system uses recursive adaptive scanning, which scans at progressively finer grid spacings only in areas where signal intensities in each frequency band are changing rapidly. Preliminary scans may be made to measure background noise levels in each frequency and direction, again using recursive adaptive scanning, and this noise may be subtracted during scans for objects of interest. Objects may be identified by finding spikes in any of the frequency bands and matching them to a database of known object signatures.