Marine Radar Autofocus With Machine-Learning Gain Feedback
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Solution Overview
Problem
Conventional marine radar systems rely on manual gain adjustments, which are prone to human error and limit the ability to process radar data effectively for navigation and collision avoidance, especially in varying conditions.
Innovation Solution
A radar system controller utilizing a gain prediction model trained via machine learning, particularly a convolutional neural network, to automatically adjust the gain settings based on real-time feedback, optimizing radar data quality for display and further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual gain control is used, then the radar system is simple to operate, but the gain setting accuracy and radar data quality deteriorate due to human error
Solution Approach 1:
The radar system performs self-adjustment of gain settings through an automated controller that analyzes radar data and modifies gain parameters without user intervention. The controller converts raw radar data into processed data, applies it to a gain prediction model, and automatically adjusts the gain setting value based on the determined gain error, enabling the system to service itself
Solution Approach 2:
The manual mechanical adjustment of gain (via knobs or buttons) is replaced with an automated electronic control system. The radar system controller uses a gain prediction model and feedback mechanisms to electronically adjust gain settings, substituting the mechanical user interaction system with an automated computational system
2Productivity
If manual gain adjustment is used, then the device complexity is low, but the radar data processing capability deteriorates due to limited analysis
Solution Approach 1:
The radar system implements a feedback loop where the controller continuously monitors radar data quality, determines gain errors by applying processed data to a gain prediction model, and adjusts gain settings accordingly. This closed-loop feedback mechanism enables continuous optimization of radar data processing capability
Solution Approach 2:
The system performs preliminary conversion of raw radar data into processed radar data and applies it to a gain prediction model before final gain adjustment. This preliminary processing action prepares the data for optimal gain setting determination, enhancing overall processing capability
3Reliability
If automated gain control is implemented, then the gain setting accuracy improves, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The radar system controller performs multiple functions: it converts raw radar data to processed data, applies the processed data to a gain prediction model, determines gain errors, and adjusts gain settings. This multi-functional controller consolidates several operations into a single device, managing complexity through functional integration
Data Source
AI summary
A method implemented by a radar system controller configured to be disposed on a marine vessel and interface with a marine radar system to predict a gain error is provided. The method may include receiving raw radar data, converting the raw radar data into processed radar data, applying the processed radar data to a radar gain prediction model to determine a gain error, and adjusting a gain setting value of the radar system as a feedback input to repeatedly adjust the operation of the radar system based on the gain error.