Radar Clutter Mitigation UI for Dynamic Sensitivity Control
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Solution Overview
Problem
Radar systems struggle to differentiate between clutter and true targets, especially in environments with low and slow-flying targets, leading to increased false alarms and inefficient target detection due to high sensitivity, which requires manual tuning and is time-consuming.
Innovation Solution
A machine learned user interface for dynamic control of intelligent radar clutter mitigation, allowing operators to adjust sensitivity settings and model preferences through a user interface, using machine learning to adapt to dynamic environments and reduce false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning clutter mitigation models are used to differentiate between clutter and true targets, then measurement precision is improved, but device complexity increases due to manual tuning and redesign requirements
Solution Approach 1:
The system automatically collects operator feedback and uses it to retrain machine learning models without requiring manual intervention. The operator's selections of true targets versus clutter automatically adjust model parameters, making the system self-adjusting and eliminating the need for expert engineers to perform tedious manual tuning and redesign
Solution Approach 2:
The system implements a feedback loop where operator selections and interactions with the radar interface are continuously collected and used to retrain the machine learning models. This feedback mechanism allows the system to adapt to changing environmental conditions and operator preferences automatically, resolving the contradiction by making the complex system self-regulating
2Measurement precision
If receiver sensitivity is increased to detect low and slow-flying targets, then measurement precision is improved, but false alarm rate increases
Solution Approach 1:
The system replaces traditional mechanical threshold-based clutter rejection with machine learning-based intelligent differentiation. The ML models analyze multiple features and patterns to distinguish true targets from clutter, maintaining high sensitivity while reducing false alarms through intelligent pattern recognition rather than simple threshold comparisons
Solution Approach 2:
The system uses an ensemble of multiple machine learning models (e.g., random forest, neural networks, support vector machines) that process different feature sets and combine their outputs. This composite approach leverages the strengths of multiple algorithms to achieve both high sensitivity and low false alarm rates by cross-validating detections across multiple independent models
3Adaptability or versatility
If manual tuning of machine learning models is performed to meet operator preferences, then adaptability is improved, but loss of time increases due to tedious tuning and retuning
Solution Approach 1:
The system automatically adapts to operator preferences by monitoring and analyzing operator feedback and selections. The machine learning models are automatically retrained using this feedback data, eliminating the need for manual tuning sessions and allowing the system to adapt continuously as operator preferences evolve
Solution Approach 2:
The system performs preliminary adaptation by pre-processing operator feedback and preparing retraining datasets in real-time. When operator preferences change, the system has already collected and organized the necessary training data, enabling rapid model updates without requiring time-consuming manual tuning sessions
4Adaptability or versatility
If continuous updates to machine learning models are enabled to respond to dynamic environments, then adaptability is improved, but device complexity increases due to retraining infrastructure requirements
Solution Approach 1:
The system performs self-updates by automatically collecting operational data, preparing training datasets, and retraining models in production environments. This self-service capability allows continuous adaptation to dynamic environments without requiring complex external retraining infrastructure or expert engineering intervention for each update cycle
Data Source
AI summary
Systems and methods for sensing targets within an ambient environment. One example system includes a human machine interface and an electronic processor. The electronic processor is configured to receive sensor data including a plurality of targets. The electronic processor is configured to select a clutter mitigation model and generate, with the clutter mitigation model, a subset of the plurality of targets based on the sensor data. The electronic processor is configured to receive, from the human machine interface, an operator sensitivity setting. The electronic processor is configured to generate, with a machine learning mapping function, a post-processing parameter based on the operator sensitivity setting. The electronic processor is configured to process the subset of the plurality of targets based on the post-processing parameter to select, from the subset, a display target. The electronic processor is configured to display, via the human machine interface, the display target.