ADS-B Station Gain Pattern Anomaly Detection With VAE Compression
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Solution Overview
Problem
The challenge of validating and certifying Machine Learning (ML) applications in safety-critical domains like airborne systems is exacerbated by the scarcity and sensitivity of data, making it difficult to generate comprehensive scenarios for validation and verification, especially in environments where large datasets are not readily available.
Innovation Solution
A system utilizing a variational autoencoder (VAE) to compress and decompress antenna gain pattern data, combined with a probabilistic model and game theoretic optimization, generates synthetic data that maintains the statistical properties of the original dataset, enabling effective anomaly detection and predictive maintenance in ADS-B systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ML validation methods are used with limited real data, then model accuracy may be maintained, but the ability to perform comprehensive safety verification and validation is severely limited
Solution Approach 1:
The patent creates synthetic copies of real antenna gain pattern data through VAE-based generative modeling. The model learns the underlying distribution of real data and generates artificial samples that replicate its statistical properties, enabling comprehensive validation without requiring additional physical measurements or real-world data collection.
Solution Approach 2:
The system performs preliminary data generation and model training using synthetic data before actual deployment or formal verification. By pre-generating diverse training scenarios including edge cases and failure modes, the system prepares robust ML models in advance, enabling thorough safety verification prior to real-world operation.
2Adaptability or versatility
If synthetic data is generated to overcome data scarcity, then comprehensive validation scenarios can be created, but data sensitivity and security concerns may arise
Solution Approach 1:
Instead of sharing or transmitting sensitive real data, the system creates synthetic copies that preserve the statistical characteristics and distribution properties of the original data without containing any actual sensitive information. This allows comprehensive validation while maintaining data security and privacy.
Solution Approach 2:
The VAE model acts as an intermediary that transforms sensitive real data into non-sensitive synthetic representations. The generative model serves as a mediator that captures essential data characteristics while removing identifying or sensitive information, enabling validation without direct exposure to sensitive data.
3Reliability
If more real data is collected for ML training, then model robustness improves, but data collection time and system downtime increase
Solution Approach 1:
The system performs preliminary generation of diverse training data synthetically before actual model deployment or retraining cycles. By pre-generating comprehensive datasets including rare events and failure scenarios, the system eliminates the need for time-consuming real-world data collection and system downtime for data gathering.
Solution Approach 2:
The system creates artificial data copies that replicate real-world scenarios without requiring physical data collection activities. This synthetic data generation approach maintains model robustness while eliminating the time loss associated with collecting, transmitting, and processing real measurement data.
Data Source
AI summary
An Automatic Dependent Surveillance Broadcast (ADS-B) system may include a plurality of ADS-B terrestrial stations, with each ADS-B terrestrial station comprising an antenna and wireless circuitry associated therewith defining a station gain pattern. The system may further include a controller including a variational autoencoder (VAE) configured to compress station pattern data from the plurality of ADS-B terrestrial stations, create a normal distribution of the compressed data in a latent space of the VAE, and decompress the compressed station pattern data from the latent space. The controller may also include a processor coupled to the VAE and configured to process the decompressed station pattern data using a probabilistic model selected from among different probabilistic models based upon a game theoretic reward matrix, determine an anomaly from the processed decompressed station pattern data, and generate an alert (e.g., a station specific alert) based upon the determined anomaly.