# Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target Recognition

arXiv: https://arxiv.org/abs/2210.10267 · HTML: https://mlsanity.com/paper/2210.10267/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Overhead Image Factors for Underwater Sonar-based SLAM](https://mlsanity.com/paper/2202.05811/) (2022)

## Built on by (most cited first, 5 of 5)

- [STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagery](https://mlsanity.com/paper/2310.01667/) (2023)
- [S3Simulator: A benchmarking Side Scan Sonar Simulator dataset for Underwater Image Analysis](https://mlsanity.com/paper/2408.12833/) (2024)
- [Physics-informed simulation framework for realistic sonar image generation and statistical validation](https://mlsanity.com/paper/2605.19712/) (2026)
- [Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism via Dual Diffusion Models and GPT Prompting](https://mlsanity.com/paper/2410.08612/) (2024)
- [Calibration and Comparative Analysis of Forward-Looking Sonar and 3D Sonar for Enhanced Underwater Object Recognition](https://mlsanity.com/paper/2608.29433/) (2026)

## Abstract (arXiv)

We propose a novel method to generate underwater object imagery that is acoustically compliant with that generated by side-scan sonar using the Unreal Engine. We describe the process to develop, tune, and generate imagery to provide representative images for use in training automated target recognition (ATR) and machine learning algorithms. The methods provide visual approximations for acoustic effects such as back-scatter noise and acoustic shadow, while allowing fast rendering with C++ actor in UE for maximizing the size of potential ATR training datasets. Additionally, we provide analysis of its utility as a replacement for actual sonar imagery or physics-based sonar data.