GAN-Based Sea Clutter Data Generation for Radar Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating simulated sea clutter data rely on statistical models that only capture average behavior, failing to replicate irregularities and resulting in limited variability, which is insufficient for accurately simulating real-world radar system operations.
Innovation Solution
A deep learning algorithm, specifically a Generative Adversarial Network (GAN) comprising a generation module and a discrimination module, is trained on real radar data to produce simulated sea clutter data that mimics the real structure of sea clutter, using a convolutional neural network architecture and data augmentation techniques to enhance variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical models are used to generate simulated sea clutter data, then the average behavior of sea clutter can be reproduced, but the variability and irregularities of real-world sea clutter are not captured
Solution Approach 1:
The patent applies preliminary action by training the deep learning model offline on a large dataset of real radar signals before deployment. The model learns the complex statistical properties and irregularities of sea clutter in advance, enabling it to generate highly variable and realistic simulated data without requiring real-time adjustments or additional data collection.
2Reliability
If detection threshold is set to a high value to avoid spurious detections, then false alarms are reduced, but small targets are not detected
Solution Approach 1:
The patent uses copying by generating multiple synthetic sea clutter datasets through the trained deep learning model. These copied datasets replicate the statistical properties and variability of real sea clutter, allowing radar systems to be tested and optimized against realistic scenarios without requiring actual field data, thereby improving both reliability and target detection capability.
3Measurement precision
If detection threshold is set to a low value to detect small targets, then target detection sensitivity is improved, but spurious pulses are misidentified as detections
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning model on diverse real-world sea clutter data encompassing various conditions (different sea states, radar parameters, geographic locations). This preliminary training enables the model to generate synthetic datasets that capture the full range of variability, allowing radar systems to be robustly tested and optimized for both sensitivity and reliability before deployment.
4Loss of time
If deep learning models are trained on limited real data, then training time is reduced, but the model fails to generalize to diverse sea clutter conditions
Solution Approach 1:
The patent applies preliminary action by performing extensive offline training of the deep learning model on comprehensive real radar data before deployment. This preliminary training phase allows the model to learn robust features and statistical properties that generalize well to diverse sea clutter conditions, eliminating the need for extensive online training or data collection during actual radar operations.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
This method for generating a simulated sea-clutter data set is based on a generation module (16) integrating a first neural network and on a discrimination module (26) integrating a second neural network and implements, in a phase of simultaneous training of the neural networks, a common optimisation algorithm using training data (De) corresponding to real sea-clutter data, in order to make the neural networks iteratively and simultaneously converge in order for the generation module to supply simulated data that is increasingly similar to the training data, while the discrimination module is increasingly restrictive as to what can constitute data similar to training data. The method further comprises an operating phase that consists in producing simulated sea-clutter data from the final generation module.