Radar CDI Architecture for ABT and Ballistic Threat Identification
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Solution Overview
Problem
Existing weapon systems struggle with high error probabilities in threat classification, particularly in stand-alone modes, failing to accurately classify, sub-classify, and identify Air Breathing Targets (ABTs) and ballistic missiles, and distinguish threatening from non-threatening ballistic objects.
Innovation Solution
A system and method using radar tracks for classification, sub-classification, and identification of threats by employing off-line algorithms to extract kinematic features, training neural networks, and utilizing Bayesian networks, with threshold comparisons and probabilistic calculations to ensure high accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If threshold-based classification is used for threat evaluation, then the system operates in stand-alone mode with simple architecture, but the probability of correct classification is low
Solution Approach 1:
The patent transforms the classification approach from simple threshold comparisons to a probabilistic framework using Bayesian networks. This involves changing the parameters from deterministic thresholds to probability distributions and conditional probabilities, enabling the system to handle uncertainty and improve classification reliability while maintaining stand-alone operation capability
Solution Approach 2:
The patent introduces Bayesian networks as an intermediary layer between radar track data and threat classification. This intermediary probabilistic model integrates multiple features (kinematic, geometric, physical) and computes posterior probabilities, serving as a mediator that reconciles simple input data with reliable classification output without requiring complex external systems
2Adaptability or versatility
If only basic threat classification is performed, then the system architecture remains simple, but sub-classification and identification of ABT threats cannot be achieved
Solution Approach 1:
The patent segments the threat identification process into three distinct hierarchical levels: (1) classification into ABT or ballistic threat categories, (2) sub-classification of ABT threats into specific types (cruise missiles, helicopters, airplanes, UAVs), and (3) identification through probability computation. This segmentation enables comprehensive threat analysis while organizing the complexity into manageable modular components
Solution Approach 2:
The Bayesian network framework serves as a universal platform that handles multiple functions: initial classification, sub-classification of ABT threats, and probabilistic identification. This multi-functional approach eliminates the need for separate specialized systems for each threat analysis task, achieving versatility without proportionally increasing overall system complexity
3Ease of operation
If stand-alone mode operation is used, then the system is self-sufficient without external tactical links, but the classification error probability increases
Solution Approach 1:
The patent performs preliminary action by pre-defining the Bayesian network structure, conditional probability tables, and feature extraction algorithms during system initialization. This preliminary configuration enables the system to operate autonomously in stand-alone mode while maintaining high classification accuracy, as the probabilistic model is prepared in advance to handle various threat scenarios without requiring real-time external assistance
Data Source
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AI summary
The present invention relates to an innovative and flexible Classification, Discrimination, Identification (CDI) architecture which, from the track data (and, when available, from the high range resolution profiles) of a surveillance or tracking radar, allows the classification, sub-classification, discrimination and typing of the threat tracked by the sensor.