Networked Radar Intrinsic Parameter Retrieval
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Solution Overview
Problem
Conventional meteorological radar systems face challenges in using radars with shorter wavelengths due to increased attenuation at higher frequencies, which requires a mechanism to account for the increased attenuation and often results in larger radar dishes and limited sampling of the atmosphere closer to the Earth's surface.
Innovation Solution
A networked radar system that operates by generating radar beams from multiple locations, measuring reflectivity along different paths, and using a cost function to retrieve intrinsic reflectivity values by accounting for attenuation, allowing for the determination of intrinsic parameters like reflectivity and specific attenuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If radars with shorter wavelengths (higher frequencies) are used, then the size of radar dishes can be reduced, but attenuation increases
Solution Approach 1:
The atmosphere is divided into multiple discrete volume elements along the radar beam path. By segmenting the propagation path into N volume elements and measuring attenuation through multiple radars at different locations, the system can isolate and quantify attenuation effects in each segment, enabling correction of the measured reflectivity to retrieve intrinsic values despite energy loss.
Solution Approach 2:
A computational retrieval algorithm acts as an intermediary between the measured attenuated reflectivity and the intrinsic reflectivity. The algorithm uses the relationship between measured values from multiple radars and the known attenuation model to solve for the intrinsic parameter values, effectively mediating the information loss due to attenuation.
2Device complexity
If conventional single-radar systems are used, then the system complexity is low, but the ability to retrieve intrinsic parameters in attenuating environments is limited
Solution Approach 1:
Multiple radars located at different positions are merged into a coordinated network system. Each radar contributes measurements along different paths through the same atmospheric volumes, and by combining these measurements with the attenuation model, the system retrieves intrinsic parameters with higher accuracy than any single radar could achieve alone.
Solution Approach 2:
The retrieval algorithm uses feedback from multiple independent measurements to iteratively solve for intrinsic parameters. The measured reflectivity values from different radars provide feedback constraints that allow the system to distinguish between intrinsic atmospheric properties and attenuation effects, improving measurement precision through this feedback mechanism.
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
Enables the retrieval of intrinsic parameters in environments with attenuation, allowing for more accurate and efficient sampling of the atmosphere using smaller radar dishes and varying frequencies, improving the accuracy and resolution of radar measurements.
Implementation Method 1
The radar generates an electromagnetic beam 108 that disperses approximately linearly with distance
Implementation Method 2
The effects of attenuation are manifested by the different radars measuring different values of a parameter
Implementation Method 3
A respective measured reflectivity of the environment is determined along a respective path of each of the respective radar beams
Data Source
AI summary
Radar beams are generated with radars disposed at different positions within an environment that attenuates at least a portion of one of the radar beams. A measured reflectivity of the environment is determined along a path of each of the radar beams. An intrinsic reflectivity is determined from different volume elements within the environment from the measured reflectivity.