Multi-Parameter Spectrometer Regulation for Deep-Space Signal Detection
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Solution Overview
Problem
Current deep space in-situ spectrometers face challenges in adjusting multiple detection parameters due to complex and harsh environments, leading to suboptimal detection sensitivity and dynamic range, and there is a lack of effective multi-parameter coupling regulation methods to improve spectral detection capabilities.
Innovation Solution
A multi-level and multi-parameter coupling regulation method using swarm intelligence optimization algorithms to automatically adjust parameters like spectral detection observation angle, RF power, and integration time, based on scene analysis and weight calculations, to optimize signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple detection parameters are manually adjusted based on engineering experience, then detection capability can be improved in certain scenarios, but the system cannot simultaneously optimize detection sensitivity and dynamic range across different scenarios due to fixed parameter constraints
Solution Approach 1:
The patent transforms fixed detection parameters into dynamically adjustable ones through a multi-level coupling regulation system. The system automatically adjusts parameters such as integration time, gain, and modulation frequency based on real-time scene analysis, enabling the spectrometer to adapt to different detection scenarios and simultaneously optimize both detection sensitivity and dynamic range.
Solution Approach 2:
The patent implements a feedback mechanism where the spectrometer analyzes scene elements (target characteristics, illumination angle, thermal radiation level, etc.) and automatically adjusts detection parameters based on this analysis. This closed-loop control enables the system to maintain optimal detection performance across varying scenarios without manual intervention.
2Ease of operation
If detection parameters are fixed to simplify system operation, then device complexity is reduced, but the system cannot flexibly adjust to different detection scenarios, limiting spectral detection capabilities
Solution Approach 1:
The patent enables the spectrometer to automatically analyze scene elements and adjust its own detection parameters without external intervention. The system performs self-diagnosis and self-regulation by evaluating target characteristics, illumination conditions, and thermal radiation levels, then autonomously optimizes parameters such as integration time and gain to suit different detection scenarios.
Solution Approach 2:
The patent transforms static, fixed parameters into dynamic, automatically adjustable parameters through an embedded control system. This enables the spectrometer to adapt its operational characteristics in real-time based on scene conditions, maintaining ease of operation while achieving high adaptability across diverse detection scenarios.
3Measurement precision
If multi-parameter coupling regulation is implemented to improve detection capability, then spectral detection performance is enhanced, but computational complexity and resource consumption increase
Solution Approach 1:
The patent divides the parameter regulation system into multiple hierarchical levels, with each level responsible for specific parameter adjustments. This segmented approach breaks down the complex multi-parameter optimization problem into manageable sub-tasks, reducing computational complexity while maintaining overall detection performance.
Solution Approach 2:
The patent applies different regulation strategies to different parameter groups based on their specific characteristics and influence on detection performance. By tailoring the control approach to each parameter's local requirements rather than applying a uniform complex algorithm to all parameters, the system achieves effective multi-parameter regulation with reduced computational burden.
Data Source
AI summary
A multi-level and multi-parameter coupling regulation method includes the following steps: acquiring multi-source scene data, analyzing the multi-source scene data, and generating scene analysis information; calculating adjustment weights of preset adjustable parameters according to the multi-source scene data; calculating adjustment values of the adjustable parameters through a swarm intelligence optimization algorithm according to the scene analysis information and the adjustment weights; adjusting the adjustable parameters according to the adjustment values.