Radar Track Classification for False Angel Echo Filtering
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Solution Overview
Problem
Existing radar systems struggle to automatically distinguish false radar echoes (false angels) from targets of interest, such as aircraft or missiles, due to atmospheric phenomena, leading to disrupted air surveillance missions, and existing solutions either eliminate slow-moving targets or require manual adjustments that are impractical and ineffective.
Innovation Solution
A radar track classification system using a three-stage process with source classification, angel phenomenon detection, and classification by two pre-trained random forest algorithms to automatically identify and filter false radar echoes, adapting to current atmospheric conditions without eliminating genuine targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If minimum speed threshold is increased to eliminate false angel tracks, then false track elimination is improved, but slow-moving drone detection is worsened
Solution Approach 1:
The system segments the track classification problem into multiple independent classification stages, each handling specific aspects of track analysis. This allows different speed thresholds and criteria to be applied in different stages without compromising either false track elimination or slow-moving target detection.
Solution Approach 2:
The system dynamically changes classification parameters including speed thresholds, angular sector definitions, and track confirmation criteria based on detected atmospheric conditions. When angel phenomena are detected, the system adjusts parameters to favor false track elimination; when conditions are normal, parameters are adjusted to preserve slow-moving target detection.
2Reliability
If minimum speed threshold is increased locally around radar, then false track elimination is improved in that area, but the affected area becomes too large eliminating drone detection
Solution Approach 1:
The system applies different speed thresholds and classification criteria to different angular sectors and altitude ranges based on local atmospheric conditions. Each sector can have customized parameters optimized for its specific false track patterns without affecting the entire surveillance volume.
Solution Approach 2:
The system dynamically adjusts the spatial scope and parameters of speed threshold application based on real-time detection of angel phenomena. The affected area and threshold values are continuously adapted to match the actual distribution and characteristics of false tracks, minimizing impact on legitimate target detection.
3Reliability
If minimum radial speed threshold is defined per angular sector, then false track elimination is improved, but system complexity and adjustment difficulty increase
Solution Approach 1:
The system automatically detects atmospheric conditions and self-adjusts sector-specific speed thresholds without requiring manual operator intervention. The classification algorithms autonomously adapt parameters based on real-time analysis of track patterns and environmental data.
Solution Approach 2:
The system continuously monitors track classification results and uses this feedback to automatically adjust speed thresholds and other parameters. The feedback loop enables the system to learn from past performance and optimize parameters dynamically, reducing the need for manual adjustment while improving effectiveness.
4Adaptability or versatility
If manual adjustment of speed thresholds is performed frequently, then adaptation to atmospheric conditions is improved, but operational time and human intervention requirements increase
Solution Approach 1:
The system automatically detects changes in atmospheric conditions and self-adjusts classification parameters without requiring manual operator intervention. The system continuously monitors track patterns and environmental data to autonomously optimize performance.
Solution Approach 2:
The system pre-establishes multiple sets of classification parameters optimized for different atmospheric conditions and weather scenarios. When conditions change, the system quickly switches between pre-configured parameter sets, avoiding the need for time-consuming manual adjustments while maintaining optimal performance.
Data Source
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AI summary
Radar track classification system, into false angel tracks, corresponding to radar angels, and other tracks, corresponding to targets of interest, comprising three stages: - a first stage (1) of source classification, receiving as input a track update, configured to determine if a track has a secondary radar source and output a classification as other track if an updated track has a secondary radar source, or transmit the track update to a second stage (2); - the second stage (2) of detection and characterization of an angel phenomenon, configured to detect an angel phenomenon and output a status of detection or not of an angel phenomenon, to a first status test module (3), and, in case of detection of an angel phenomenon, output a characterization of the detected angel phenomenon, to a third stage (4);- the first status test module (3), receiving as input the track update from the first stage and the detection status of the second stage (2) of an angel phenomenon, configured to test the value of the detection status of an angel phenomenon by the second stage (2), and, in the case of a non-detection status of an angel phenomenon, to output a classification as another track of the updated track, and in the case of a detection status of an angel phenomenon, to transmit the track update to the third stage (4); and - the third stage (4), configured to perform a classification of the track update as a false angel track or another track, by two pre-trained classifiers, arranged in series, each using a random forest type algorithm.