Target Tracking Radar Classifier Glint Detection
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Solution Overview
Problem
Target-tracking radars face challenges in distinguishing between targets of interest and non-interest due to noise in signal-to-noise ratio (SNR) fluctuations, leading to increased variance in measured off-boresight angles, making it difficult to accurately classify and track intended targets.
Innovation Solution
A target-tracking radar system equipped with a target classifier that compares total epsilon measurements with theoretical noise values, adjusts for range and pivot ranges, and uses noise count thresholds to determine whether to continue tracking or break off from a target, utilizing a combination of monopulse errors and glint characteristics to differentiate between intended and non-intended targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If the radar bandwidth is increased to improve target detection capability, then the target detection capability is improved, but the radar inadvertently responds to noise in target epsilons, making discrimination between targets of interest and non-interest difficult
Solution Approach 1:
The patent segments the target detection and classification process into distinct stages: initial target detection using high bandwidth, followed by separate glint detection and epsilon analysis stages. This segmentation allows the system to benefit from high bandwidth detection while subsequently filtering out noise through dedicated classification algorithms that analyze epsilon statistics and glint characteristics independently.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between the high-bandwidth detection stage and the final target tracking decision. This intermediary analyzes target epsilons and glint information to determine whether a detected target is genuine or noise, thereby resolving the contradiction between detection sensitivity and classification accuracy.
2Reliability
If the radar responds to all detected targets with high bandwidth, then no targets are missed, but noise fluctuations cause increased variance in measured epsilons, leading to false tracking of non-intended targets
Solution Approach 1:
The patent applies preliminary action by performing glint detection and epsilon analysis before committing to track a target. The system pre-processes detected targets through classification algorithms that evaluate epsilon statistics and glint characteristics, filtering out noisy false targets before they can contaminate the tracking system. This preliminary filtering action prevents noise from affecting tracking reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the classification system continuously monitors target epsilons and glint information, and uses this feedback to adjust tracking decisions. When noise causes epsilon variance to exceed thresholds, the feedback loop triggers re-evaluation or abandonment of the track, thereby maintaining tracking reliability despite high bandwidth operation.
3Productivity
If the radar tracks all detected targets, then comprehensive monitoring is achieved, but discrimination between targets of interest and targets of non-interest becomes difficult due to epsilon noise
Solution Approach 1:
The patent applies dynamics by making the tracking allocation decision adaptive rather than static. The system dynamically evaluates each detected target using glint detection algorithms and epsilon analysis, adjusting tracking resources based on the classified target type. This dynamic approach allows comprehensive monitoring of all targets while maintaining high discrimination accuracy by allocating tracking resources preferentially to targets of interest identified through the classification process.
Data Source
AI summary
Embodiments of a target classifier and method for target classification using measured target epsilons and target glint information are generally described herein. The target classifier is configured to compare a total epsilon measurement with target glint information to determine whether to the target being tracked corresponds to an intended target type. Based on the comparison, the target classifier may cause target tracking circuitry of a target-tracking radar to either continue tracking the target or break-off from tracking the target. Glint of different target types may be characterized at different ranges and the target's glint characteristics may be used to distinguish intended from non-intended targets. Accordingly, intended targets such as incoming artillery may be distinguished from non-intended targets such as aircraft to help prevent countermeasures from being launched against non-intended targets.