# Robust Detection for Mills Cross Sonar

**Authors:** Olivier Lerda, Ammar Mian, Guillaume Ginolhac, Jean-Philippe Ovarlez, Didier Charlot  
**arXiv:** [2303.17979](https://arxiv.org/abs/2303.17979)  
**Date:** 2023-03-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.17979v1/

## Key points

- Use of multi-array systems in sonar and radar applications
- Enhances communication speeds, target discrimination, and imaging capabilities
- Focus on a multibeam sonar system with two receiving arrays
- New adaptive techniques proposed to improve detection capabilities compared to traditional approaches
- Consider correlated arrays and impulsive clutter
- Goal is to develop detectors that perform well in partially homogeneous environments and exhibit CFAR behavior
- Employ 2-step GLRT and Rao approach for a generalized ANMF test
- Equivalent detector is numerically simpler and robust to environmental unknowns
- Performance evaluated through theoretical examples, simulations, and experimental data insights
- Proposed detectors outperform competitors and exhibit superior robustness to environmental uncertainties
- Previous works discussed include subspace data models and complex processing techniques like STAP and MIMO systems

## Summary

The paper titled "Robust Detection for Mills Cross Sonar" discusses the use of multi-array systems in sonar and radar applications. These systems have been proven to enhance communication speeds, target discrimination, and imaging capabilities. The authors focus specifically on a multibeam sonar system that operates with two receiving arrays. In this study, the authors propose new adaptive techniques to improve detection capabilities compared to traditional sonar detection approaches. They consider correlated arrays whose covariance matrices are estimated up to scale factors as well as impulsive clutter. The goal is to develop detectors that can perform well in partially homogeneous environments and exhibit a well-established texture Constant False Alarm Rate (CFAR) behavior. To achieve this, the authors employ the 2-step Generalized Likelihood ratio Test (GLRT) and Rao approach which lead to a generalization of the Adaptive Normalized Matched Filter (ANMF) test. This results in an equivalent detector that is numerically simpler and exhibits strong robustness to environmental unknowns. The performance of these detectors is evaluated through theoretical examples, numerous simulations, and insights into experimental data. The results demonstrate that the proposed detectors outperform their competitors and exhibit superior robustness in dealing with environmental uncertainties. The paper also provides additional context by discussing previous works in the field of remote sensing and radar systems. It highlights advancements such as subspace data models for mismatched signals and complex processing techniques like Space-Time Adaptive Processing (STAP) for airborne radars and Multiple-Input Multiple-Output (MIMO) systems. Overall, this paper presents novel adaptive techniques for improving detection capabilities in multibeam sonar systems. The proposed detectors show promising performance in various scenarios and offer increased robustness to environmental uncertainties.