Adaptive Dynamic Cluster Deinterleaving for Radar Pulse Separation
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Solution Overview
Problem
Existing radar deinterleaving systems struggle to accurately separate interleaved radar pulses from multiple sources, especially when faced with dynamically varying and randomized signal parameters from advanced radar emitters, leading to misidentification or failure in threat detection.
Innovation Solution
The method involves adaptive dynamic cluster deinterleaving, where weights are dynamically adjusted based on the variance of measured features within clusters, using a weighted distance calculation to identify pulse membership and iteratively refine the weight adjustments to optimize cluster formation and minimize processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static weights are used in deinterleaving, then device complexity is reduced, but measurement precision deteriorates when facing dynamically varying radar signals
Solution Approach 1:
The patent implements dynamic weight adjustment by calculating the variance of each pulse parameter within clusters and using this variance to adaptively modify the weights in the weighted distance calculation. This allows the system to respond to dynamically varying radar signals by adjusting the importance of each parameter based on its observed stability, thereby maintaining high identification accuracy without requiring overly complex fixed-structure systems
Solution Approach 2:
The system changes the weight parameters dynamically based on the observed variance of pulse characteristics. By monitoring the variance of each parameter (frequency, pulse width, amplitude, AOA) within clusters and adjusting weights accordingly, the system adapts to different radar signal patterns and environmental conditions, improving measurement precision while maintaining reasonable system complexity
2Measurement precision
If weights are adjusted to reduce measurement error, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent applies partial action by adjusting weights only for parameters that exhibit significant variance within clusters. Rather than continuously re-evaluating and adjusting all parameters, the system focuses computational effort on parameters that actually need adjustment, thereby reducing processing time while maintaining measurement precision for the critical parameters
3Measurement precision
If cluster window is made small to reduce misclassification, then measurement precision improves, but productivity deteriorates due to increased error rates
Solution Approach 1:
The system dynamically adjusts the effective cluster window size through weight modification. By changing the weights based on observed variance, the system can effectively expand or contract the acceptance criteria for cluster membership. This allows the system to maintain high assignment accuracy while adapting to different signal conditions, preventing excessive misclassification that would reduce processing efficiency
Data Source
AI summary
Described herein are methods and systems capable of dynamically adapting weights in response to a received stream of pulses by deinterleaving a stream of pulses according to an initial weighted distance, adjusting the weighted distance, and deinterleaving the stream of pulses according to the adjusted weighted distance.