Intelligent Sensor Placement Modeling via Signal Propagation Simulation
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Solution Overview
Problem
Current systems lack the ability to intelligently select and optimize sensor placement and signal detection in varying environmental conditions, requiring time-consuming trial and error, and fail to simulate geographical and environmental impacts on signal transmission effectively for both military and civilian applications.
Innovation Solution
A computer modeling system that processes user-defined signal and directional attributes through a series of processors to calculate signal propagation probabilities, incorporating environmental data and sensor characteristics, creating statistical models for optimized sensor network design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional trial and error methods are used for sensor selection and placement, then sensor network design can be achieved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary computational analysis by creating a digital twin of the environment and pre-calculating signal propagation paths, sensor coverage areas, and detection probabilities before actual sensor deployment. This allows designers to evaluate multiple configurations virtually and select optimal placements without time-consuming physical trials.
Solution Approach 2:
The patent replaces physical trial-and-error sensor deployment with a computer-based modeling system that uses signal propagation models, environmental data processing, and automated optimization algorithms to determine optimal sensor placement, eliminating the need for repeated physical testing and adjustment.
2Measurement precision
If comprehensive environmental data and signal propagation modeling are implemented, then accurate signal detection probability calculation is achieved, but system complexity increases
Solution Approach 1:
The system segments the complex modeling task into distinct functional modules: environmental data processing module, signal propagation modeling module, sensor performance characterization module, and optimization module. Each module handles specific computations independently, making the overall complex system manageable and allowing parallel processing of different environmental factors and signal types.
Solution Approach 2:
The patent introduces a digital twin as an intermediary virtual representation of the physical environment. This digital twin mediates between complex environmental data and sensor performance predictions, allowing accurate modeling of signal propagation through terrain, weather, and obstacles without requiring direct complex calculations for every possible sensor placement scenario.
3Reliability
If intelligent sensor selection and placement optimization is implemented, then sensor network performance is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs comprehensive computational analysis in advance by creating detailed signal propagation models and evaluating multiple sensor placement scenarios during the design phase. This preliminary optimization ensures high network performance without requiring intensive real-time computations during actual sensor operation, reducing ongoing energy consumption.
Solution Approach 2:
The patent creates a virtual copy (digital twin) of the environment and sensor network to perform computational optimizations. All intensive calculations for signal propagation, coverage analysis, and performance prediction are performed on this virtual copy rather than requiring real-time computational resources during actual sensor deployment and operation.
Data Source
AI summary
The present system for modeling intelligent sensor selection and placement takes signal and sensor information and calculates a statistical inference. As signal data passes through a series of processors, it is transformed by functions to account for signal emission, sensor reception, environmental factors, and noise. This produces a simulation of what the emitted signal would appear to be at a given sensor. The system may be used to select the most effective sensors for a given area or to determine the best sensor coverage for a given area.