Y casi dos años en el proyecto

El tiempo vuela. Hay que esforzarse para que los días simplemente no se amontonen unos sobre otros.

Todavía falta mucho por recorrer de este camino, pero he llegado a comprender bastante bien el problema. También comprendo las cosas que no entiendo, lo cual es bastante importante.

Y por eso me gustaría tener ayuda con muchas preguntas que todavía tengo. Estudiantes motivados, disciplinados e interesados en acústica submarina, señales y machine learning, que puedan sacar provecho de investigar en estos temas para poder graduarse y ganar experiencia y conocimiento, y al mismo tiempo ayudar a avanzar la investigación (claramente, recibiendo el crédito correspondiente).

Por eso publico acá temas de investigación que no sé si tenga de tiempo de analizar con el detalle necesario, y que por lo tanto vendría bien la participación de alguien más, de un estudiante.

Introduction

The Royal Military Academy (RMA) is the Belgian University that has a mission to provide education to superior officers within Belgian Defence. RMA also conducts scientific research at university level for projects funded by the Belgian Defence department or external sources and is fully recognized as a university, fulfilling the same criteria as civilian universities. At the RMA, researchers help push the boundaries of cutting-edge science and technology, working on complex and sometimes sensitive projects in land, sea, and air – and between cyber and space, to create innovations that will help ensure our continued peace and security.

The main research client of RMA is the Belgian Defence and as such, the research poles topics are centred around themes that are relevant for the Belgian Defence. As part of these research activities, RMA provides the Belgian contribution to the collaborative research in the NATO Science and Technology Organization and to the research conducted by the European Defence Agency. Innovative applied research is usually conducted in collaboration with other academic institutions, research centres and industrial partners in a triple-helix setup, typically aiming at satisfying one of the needs of the Belgian Defence while contributing to the development of an industrial capability in Belgium.

The Communication, Information, Systems and Sensors (CISS) department of the RMA has experience in processing data from wide area of sensors, as well as AI-based multi-sensory processing through different national, European and international projects. Applications of interest for this proposal include mine countermeasures, munition clearance, underwater surveillance and maritime situational awareness.

Summary

Summary by Categories

Exploration and synthesis: Topics 1, 2, 9, 10

Methodological comparison: Topics 3, 7, 14

Interpretability and analysis: Topics 4, 5, 13

Advanced classification: Topics 6, 8, 11, 12

Summary by Academic Level

Undergraduate/Internship: Topics 1, 2

Master’s: Topics 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14

Summary by Duration

3-6 months: Topics 1, 2 (2 topics)

6-9 months: Topics 4, 5, 7, 13 (4 topics)

9-12 months: Topics 3, 6, 8, 9, 10, 11, 14 (7 topics)

12 months: Topic 12 (1 topic)

Note: These are suggestions. An undergraduate student could work on a master’s-level topic after case-by-case analysis of their background and capabilities.*

Relationship with Papers IN PROGRESS

«Improving low-frequency sonar mine detection through near-seafloor echo removal»: Topics 1, 8, 13, 14 (papers in development, working titles)
«Evaluating low-frequency sonar mine detection under domain shift»: Topic 3 (paper in development, working title)
«Feature reduction and interpretability analysis for optimized low-frequency sonar mine detection»: Topic 4, 14 (paper in development, working title)
«Physics-informed classification of elastic vs. rigid underwater objects using synthetic low-frequency sonar returns»: Topics 5, 7 (papers in development, working titles)
«Bridging synthetic and real-world sonar data: Domain transfer and augmentation strategies»: Topics 6, 9, 10, 11 (paper in development, working title)
«One-class classification for underwater mine detection: Anomaly-based target identification using massive seafloor data»: Topics 2, 12, 13 (paper in development, working title)

General Notes

All projects use the curated LF-SAS dataset as base, complemented with synthetic data as needed
Also available an extensive additional dataset with many more seafloor return signals, specifically designed for one-class classifier training
Topics are designed to complement and not duplicate the main research work
All topic descriptions and project steps can be adjusted depending on the student’s needs, knowledge, and requirements
Results can provide valuable insights for future work and validation of alternative approaches in limited data scenarios

RESEARCH TOPICS (Ordered by Complexity)

TOPIC 1: Exploration of Underwater Acoustics Libraries for Sonar Data Synthesis

Level: Undergraduate (3-6 months, ideal for internship)
Category: Exploration and synthesis
Related to: «Improving low-frequency sonar mine detection through near-seafloor echo removal» (paper in development, working title)

Description:

This exploratory project focuses on understanding and documenting specialized underwater acoustics tools (such as BELLHOP, RAM, MATLAB acoustics toolboxes, or Python libraries like pyram) to assess their potential utility for the sonar research group. The student will investigate the capabilities and limitations of these simulation tools, understand what types of acoustic data can be generated, and evaluate their potential applications for underwater object detection and classification research. The primary goal is to provide comprehensive documentation and assessment of available tools rather than immediate implementation for data augmentation.

Project Plan:

Core Requirements (Essential):

Review and select available underwater acoustics tools/libraries (BELLHOP, RAM, Kraken, pyram, etc.)
Document installation procedures, system requirements, and basic functionality for each tool
Implement basic underwater acoustic propagation simulations to understand tool capabilities
Investigate whether object inclusion in simulations is feasible and document findings
Create comprehensive documentation on tool usage, limitations, and potential applications for sonar research

Additional Tasks (Optional, if time permits): 6. Generate synthetic acoustic signals and investigate their characteristics using analysis tools (e.g., TSFresh-type feature extraction) 7. Assess potential for synthetic data to complement real datasets in classification tasks 8. Evaluate computational requirements and feasibility for integration into existing research workflows

TOPIC 2: COMSOL-Based Generation and Validation of Synthetic Acoustic Return Signals

Level: Master’s or Advanced Undergraduate (6-9 months, suitable for internship)
Category: Exploration and synthesis
Related to: «One-class classification for underwater mine detection: Anomaly-based target identification using massive seafloor data» (paper in development, working title)

Description:

This project focuses on learning and applying COMSOL Multiphysics to generate synthetic acoustic return signals (time series) from underwater objects under different conditions. The primary goal is to understand COMSOL’s capabilities for acoustic simulation, generate return signals for various object geometries and scenarios, and validate these results by comparing them with synthetic return signals generated using existing Python code. This validation step is crucial before advancing to more complex shapes or scenarios, ensuring that COMSOL can produce reliable acoustic simulations for underwater object detection research.

Project Plan:

Core Requirements (Essential):

Learn COMSOL Multiphysics setup for underwater acoustic modeling in the 0.5-10 kHz frequency range
Implement basic acoustic simulations for simple geometries (spheres, cylinders) under controlled conditions
Generate synthetic return signals (time series) from COMSOL simulations for different object types and configurations
Compare COMSOL-generated return signals with corresponding synthetic signals from existing Python code
Analyze differences and similarities between both synthetic approaches to validate COMSOL results
Document COMSOL procedures, capabilities, and limitations for future research use

Additional Tasks (Optional, if time permits): 7. Extend simulations to more complex object geometries and burial scenarios 8. Develop similarity metrics to quantify agreement between COMSOL and Python-generated signals 9. Implement detection or classification algorithms to evaluate synthetic signal quality using both approaches 10. Investigate parameter sensitivity in COMSOL simulations and their impact on return signal characteristics 11. Create automated pipeline for batch generation of validation scenarios with controlled parameters 12. Compare computational efficiency between COMSOL and Python-based synthetic generation methods

TOPIC 3: Comparative Analysis of Classification Architectures for Underwater Mine Detection

Level: Master’s (9-12 months)
Category: Methodological comparison
Related to: «Evaluating low-frequency sonar mine detection under domain shift» (paper in development, working title)

Description:

This project investigates the comparative performance between different classification architectures for underwater mine detection using LF-SAS return signals. The student will first review available classification methods suitable for acoustic signal processing, then select two approaches for detailed comparison. One suggested option is Random Forest (RF), as it serves as the established baseline in current research. The analysis includes evaluation under multiple validation schemes: RSK (Repeated Stratified K-Fold, optimistic baseline), LOMO (Leave-One-Mine-Out, realistic individual object generalization), and LOGO (Leave-One-Group-Out, domain-shift scenarios testing generalization to unseen mine types or burial conditions) to determine which architecture offers better robustness and operational generalization.

Project Plan:

Core Requirements (Essential):

Conduct literature review of classification methods suitable for acoustic signal processing (Random Forest, CNNs, SVMs, XGBoost, etc.)
Select two classification approaches for comparison, with Random Forest recommended as one option due to established baseline
Implement and adapt both selected methods for sonar return signal classification
Prepare datasets and preprocessing pipelines appropriate for both classification methods
Evaluate both methods under RSK validation scheme (standard cross-validation baseline)
Document comparative performance analysis including computational requirements and deployment feasibility

Additional Tasks (Optional, if time permits): 7. Implement LOMO validation (Leave-One-Mine-Out) to test generalization to individual unseen objects 8. Implement LOGO validation (Leave-One-Group-Out) to test robustness under domain-shift conditions 9. Investigate cases where one method outperforms the other, identifying discriminative signal characteristics 10. Analyze feature importance or model interpretability for both approaches 11. Provide detailed recommendations on architecture selection according to operational context and constraints

TOPIC 4: Interpretability Analysis in Machine Learning Classifiers for Underwater Mine Detection

Level: Master’s (6-9 months)
Category: Interpretability and analysis
Related to: «Feature reduction and interpretability analysis for optimized low-frequency sonar mine detection» (paper in development, working title)

Description:

This project focuses on developing and applying interpretability techniques to understand how machine learning classifiers make decisions in underwater mine detection. The student will work with various ML methods (such as Random Forest, SVM, XGBoost, or neural networks) to investigate which features or signal characteristics are most important for classification decisions. Using techniques such as SHAP, LIME, permutation importance, and feature importance analysis, the student will investigate how these patterns relate to known acoustic scattering phenomena and provide insights into model decision-making processes.

Project Plan:

Core Requirements (Essential):

Select and train a machine learning classifier (e.g., Random Forest, SVM, XGBoost) for underwater mine detection
Implement basic interpretability techniques: feature importance analysis and permutation importance
Apply SHAP (SHapley Additive exPlanations) to understand individual prediction contributions
Analyze which acoustic features are most critical for target vs. seafloor discrimination
Create visualizations to effectively communicate interpretability findings
Document relationships between important features and known acoustic scattering principles

Additional Tasks (Optional, if time permits): 7. Implement LIME (Local Interpretable Model-agnostic Explanations) for local interpretation analysis 8. Compare interpretability results between different classifier types (e.g., tree-based vs. linear methods) 9. Investigate differences in feature importance between mine types and burial conditions 10. Correlate important features with physical phenomena (structural resonances, creeping waves, scattering mechanisms) 11. Develop interpretability analysis for time-series segments to identify critical temporal regions 12. Create operator-friendly dashboards for real-time interpretation of classification decisions

TOPIC 5: Domain Gap Analysis between Synthetic and Real Data in Underwater Object Classification

Level: Master’s (6-9 months)
Category: Interpretability and analysis
Related to: «Physics-informed classification of elastic vs. rigid underwater objects using synthetic low-frequency sonar returns» (paper in development, working title)

Description:

This project focuses on analyzing and understanding the differences between synthetic and real sonar data, specifically for cylindrical objects where synthetic data generation is feasible. The student will work with existing synthetic signal generation code to produce cylindrical object returns and compare them with corresponding real cylindrical target data from the LF-SAS dataset. The primary goal is to characterize the domain gap between synthetic and real signals, understand what causes these differences, and investigate potential approaches to reduce the gap for the specific case of cylindrical targets.

Project Plan:

Core Requirements (Essential):

Analyze real return signals from cylindrical targets in the LF-SAS dataset to understand their characteristics and variability
Use existing synthetic signal generation code to produce synthetic return signals for cylindrical objects with matching parameters
Compare synthetic and real cylindrical signals using appropriate signal analysis techniques to identify key differences
Characterize the domain gap between synthetic and real data through statistical and visual analysis methods
Investigate potential factors contributing to the observed differences (environmental effects, modeling limitations, etc.)
Document findings and provide recommendations for reducing the domain gap in cylindrical object simulations

Additional Tasks (Optional, if time permits): 7. Implement quantitative domain gap metrics (e.g., Maximum Mean Discrepancy, Wasserstein distance) for feature spaces 8. Develop visualization techniques (t-SNE, UMAP, PCA) to illustrate domain differences 9. Investigate the impact of different simulation parameters on domain gap reduction 10. Evaluate whether domain gap reduction improves classification performance in transfer learning scenarios 11. Extend analysis to other simple geometries if additional synthetic generation capabilities become available 12. Develop guidelines for synthetic data generation parameter selection to minimize domain gap

TOPIC 6: Optimization of Synthetic:Real Data Ratios for Few-Shot Learning in Cylindrical Target Detection

Level: Master’s (9-12 months)
Category: Advanced classification
Related to: «Bridging synthetic and real-world sonar data: Domain transfer and augmentation strategies» (paper in development, working title)

Description:

This project investigates how to optimally combine synthetic and real data for training classifiers when real data is limited, focusing specifically on cylindrical targets where synthetic data generation is feasible. In many operational scenarios, real target data is scarce (few-shot learning), but synthetic data can be generated abundantly. The student will work with existing synthetic signal generation code to produce cylindrical target returns and systematically investigate what ratio of synthetic to real data produces the best classification performance when real data is limited. The goal is to determine practical guidelines for mixing synthetic and real cylindrical data to maximize detection performance.

Project Plan:

Core Requirements (Essential):

Generate synthetic return signals for cylindrical targets using existing code with various parameter configurations
Create few-shot learning scenarios using limited real cylindrical target data from the LF-SAS dataset (e.g., 5, 10, 20 samples)
Systematically test different synthetic:real data ratios (e.g., 10:90, 30:70, 50:50, 70:30, 90:10) for training classifiers
Evaluate classification performance for each ratio using appropriate metrics and validation schemes
Identify optimal ratios for different levels of real data scarcity
Document practical recommendations for synthetic:real data mixing in cylindrical target detection

Additional Tasks (Optional, if time permits): 7. Investigate how optimal ratios depend on the quality/diversity of synthetic data generation parameters 8. Compare performance across different classifier types (Random Forest, CNN, etc.) 9. Analyze which synthetic data characteristics most benefit few-shot learning scenarios 10. Develop adaptive strategies that adjust ratios based on available real data quantity and quality 11. Extend analysis to different burial conditions or environmental parameters for cylindrical targets 12. Create guidelines for practitioners on when synthetic data augmentation is most beneficial

TOPIC 7: Underwater Object Detection using Time-Frequency Representations with Real Sonar Data

Level: Master’s (6-9 months)
Category: Methodological comparison
Related to: «Physics-informed classification of elastic vs. rigid underwater objects using synthetic low-frequency sonar returns» (paper in development, working title)

Description:

This project investigates the effectiveness of different time-frequency representations for underwater object detection (target vs. seafloor) using the real LF-SAS dataset. The student will select two time-frequency representations from available options (such as spectrograms, scalograms/wavelets, Wigner-Ville distributions, Hilbert-Huang Transform, etc.) and compare their performance for the detection task. The student will also choose an appropriate detection algorithm (such as CNNs, Random Forest adapted for 2D data, or other suitable methods) to evaluate how well each representation captures discriminative characteristics present in real return signals.

Project Plan:

Core Requirements (Essential):

Review available time-frequency representation techniques and select two for comparison
Select an appropriate detection algorithm suitable for the chosen representations (e.g., 2D CNN, adapted Random Forest, SVM)
Implement the selected time-frequency transforms on the real LF-SAS dataset
Develop preprocessing pipelines specific to each chosen representation (normalization, windowing, resolution considerations)
Train and evaluate the detection algorithm using both representations for target vs. seafloor classification
Compare performance between the two representations using standard detection metrics

Additional Tasks (Optional, if time permits): 7. Apply multiple validation schemes (RSK, LOMO, LOGO) to evaluate robustness of each representation 8. Analyze computational efficiency and memory requirements of each approach 9. Investigate which time-frequency characteristics are most discriminative for target detection 10. Compare results against time-series based approaches if baseline data is available 11. Provide recommendations on representation selection according to operational context and computational resources 12. Analyze failure cases to understand limitations of each representation

TOPIC 8: Modeling and Incorporation of Seafloor Reverberation in Synthetic Sonar Signals

Level: Master’s (9-12 months)
Category: Exploration and synthesis
Related to: «Improving low-frequency sonar mine detection through near-seafloor echo removal» (paper in development, working title)

Description:

This project investigates the incorporation of marine environment reverberation effects in synthetic sonar return signals to improve their realism and similarity to real data. Underwater reverberation, caused by multiple reflections from the seafloor and water boundaries, generates a diffuse acoustic background that affects real sonar signals but may be absent in synthetic simulations. The student will work with existing synthetic signal generation code to add reverberation effects and compare the resulting signals with real data, particularly for cases where synthetic and real data correspond (such as cylindrical objects). The goal is to assess whether adding reverberation improves the match between synthetic and real return signals.

Project Plan:

Core Requirements (Essential):

Study the fundamentals of underwater reverberation and its characteristics in the real LF-SAS dataset
Use existing synthetic signal generation code to produce baseline synthetic return signals
Develop and implement methods to add controlled reverberation effects to synthetic signals
Compare synthetic signals (with and without reverberation) against corresponding real data, focusing on cases with known geometry correspondence (e.g., cylinders)
Analyze differences and similarities between synthetic and real signals using appropriate signal analysis techniques
Document the impact of reverberation addition on synthetic signal realism

Additional Tasks (Optional, if time permits): 7. Investigate different levels and types of reverberation to optimize the match with real data 8. Extend comparison analysis to other object types beyond cylinders 9. Evaluate whether reverberation-enhanced synthetic signals could improve training dataset quality 10. Develop guidelines for reverberation parameter selection based on environmental conditions 11. Create comprehensive documentation for reverberation modeling procedures for future research use

TOPIC 9: Advanced Data Augmentation Techniques for Acoustic Sonar Signals

Level: Master’s (9-12 months)
Category: Exploration and synthesis
Related to: «Bridging synthetic and real-world sonar data: Domain transfer and augmentation strategies» (paper in development, working title)

Description:

This project expands beyond traditional data augmentation techniques (noise injection, temporal shifting, amplitude scaling) toward more sophisticated methods specifically designed for underwater acoustic signals. The student will select one or two advanced augmentation techniques from available options (such as adversarial augmentation, mixup adapted for sonar signals, generative models like VAE/GAN, or other advanced methods) and investigate their effectiveness for creating realistic variations of return signals that preserve relevant physical characteristics while increasing classifier robustness.

Project Plan:

Core Requirements (Essential):

Review available advanced data augmentation techniques suitable for acoustic signals
Select one or two specific augmentation methods for detailed implementation and evaluation
Implement the selected augmentation technique(s) for sonar return signals
Apply the augmentation methods to the LF-SAS dataset and generate augmented signal variations
Assess the quality and realism of augmented signals through comparison with original data
Document findings and provide analysis of the augmentation method effectiveness

Additional Tasks (Optional, if time permits): 7. Evaluate the impact of augmentation on classifier performance using appropriate validation schemes 8. Develop physics-aware augmentation methods that preserve acoustic scattering characteristics 9. Compare augmentation in raw signal domain vs. feature domain approaches 10. Implement hybrid augmentation combining multiple techniques intelligently 11. Analyze computational efficiency and feasibility for real-time implementation 12. Create comprehensive documentation and code library for the implemented augmentation techniques for future research use

TOPIC 10: Classification of Elastic vs. Rigid Objects using Time-Frequency Representations and Synthetic Data

Level: Master’s (9-12 months)
Category: Advanced classification
Related to: «Physics-informed classification of elastic vs. rigid underwater objects using synthetic low-frequency sonar returns» (paper in development, working title)

Description:

This project addresses the problem of underwater material classification (elastic vs. rigid) using time-frequency representations applied to synthetic sonar return signals. Based on acoustic scattering simulations that model fundamental physical differences between elastic objects (aluminum, steel shells) and rigid objects (rock-like materials), the student will select one or two time-frequency representations and investigate how well they capture the characteristic signatures of each material type. The student will also choose an appropriate classifier to perform the elastic vs. rigid discrimination and validate results against known scattering theory.

Project Plan:

Core Requirements (Essential):

Review available time-frequency representation techniques and select one or two for the study (e.g., spectrograms, wavelets, Wigner-Ville, etc.)
Generate or access synthetic dataset of elastic vs. rigid objects with controlled material properties and geometries
Apply the selected time-frequency representation(s) to the synthetic return signals
Select and implement an appropriate classifier for the elastic vs. rigid discrimination task
Train and evaluate the classifier using the time-frequency representations
Validate results by comparing classification decisions with known physical scattering theory (structural resonances, material properties)

Additional Tasks (Optional, if time permits): 7. Develop data augmentation methods specific to time-frequency representations of acoustic signals 8. Perform interpretability analysis to identify critical time-frequency regions for material classification 9. Compare performance between different time-frequency representations (if two were selected) 10. Investigate classification robustness across different object geometries (spheres, cylinders, complex shapes) 11. Evaluate transferability of models trained on simple objects toward more complex geometries 12. Develop framework for physical validation of classification decisions in time-frequency domain

TOPIC 11: Development of One-Class Classification Algorithms Optimized for Underwater Acoustic Environments

Description:

This project focuses on developing and optimizing One-Class Classification (OCC) algorithms specifically designed for the unique characteristics of underwater acoustic signals. The student will select one or two existing OCC methods and adapt them for the underwater acoustic domain, considering physical properties of scattering, angular dependency, and temporal characteristics of return signals. The goal is to develop OCC approaches that leverage domain knowledge to improve anomaly detection in underwater environments where seafloor data is abundant but mine samples are scarce.

Project Plan:

Core Requirements (Essential):

Review existing One-Class Classification algorithms and select one or two for detailed study and implementation
Implement selected OCC algorithm(s) using the extensive seafloor dataset for training the «normal» class model
Evaluate basic OCC performance using real mine data as anomalies and seafloor data as normal class
Incorporate angle-stratified training to account for grazing angle dependencies in seafloor characteristics
Compare performance against standard binary classification approaches using standard anomaly detection metrics
Document the effectiveness of selected OCC approaches for the underwater mine detection problem

Additional Tasks (Optional, if time permits): 7. Develop domain-specific variants incorporating kernels or features designed specifically for acoustic signals 8. Create hybrid OCC methods combining multiple algorithms to leverage complementary strengths 9. Implement ensemble OCC techniques that improve robustness across different acoustic conditions 10. Analyze computational efficiency and feasibility for deployment in real-time sonar systems 11. Validate algorithms using synthetic anomalies with known ground truth characteristics 12. Investigate the impact of different seafloor data preprocessing strategies on OCC performance

TOPIC 12: Few-Shot Learning and Meta-Learning for Detection of New Underwater Object Types

Level: Master’s (12 months)
Category: Advanced classification
Related to: «Bridging synthetic and real-world sonar data: Domain transfer and augmentation strategies» (paper in development, working title)

Description:

This project addresses operational scenarios where new types of underwater objects appear with very few training samples available. The student will investigate how existing classifiers can be adapted to recognize new object classes when only a small number of examples are available (few-shot learning). The work focuses on practical approaches to leverage knowledge from known objects to improve classification of new, previously unseen object types using the LF-SAS dataset and synthetic data.

Project Plan:

Core Requirements (Essential):

Create few-shot learning scenarios by holding out specific object types from the LF-SAS dataset as «new» classes
Implement basic few-shot learning approach using transfer learning from pre-trained classifiers
Evaluate performance in 5-shot and 10-shot scenarios (5 or 10 examples of new object types)
Compare few-shot learning results with baseline performance when no adaptation is used (direct application of pre-trained classifier to new object types)
Investigate which features or characteristics transfer best between known and new object types
Document practical guidelines for adapting classifiers to new object types with limited data

Additional Tasks (Optional, if time permits): 7. Implement advanced few-shot learning architectures (Prototypical Networks, Matching Networks) 8. Develop model-agnostic meta-learning (MAML) approach for underwater object classification 9. Create meta-learning training episodes using dataset subsets and synthetic data 10. Investigate 1-shot learning scenarios (only 1 example of new object type) 11. Develop similarity metrics between objects for informed adaptation strategies 12. Implement continual learning to incorporate new objects without forgetting previous ones 13. Create operational framework for rapid deployment when new object types are encountered

TOPIC 13: Grazing Angle Dependency Analysis for Seafloor Acoustic Return Characterization

Level: Master’s (6-9 months)

Category: Interpretability and analysis

Related to: «Improving low-frequency sonar mine detection through near-seafloor echo removal», «Evaluating low-frequency sonar mine detection under domain shift», «One-class classification for underwater mine detection: Anomaly-based target identification using massive seafloor data» (papers in development, working titles)

Description:

This project focuses on characterizing and understanding the variability of seafloor acoustic return signals as a function of grazing angle to establish optimal angular ranges for balanced detector training. Using the extensive seafloor dataset, the student will analyze how acoustic signatures change across different beam angles, determine threshold angles where signal characteristics become significantly different, and develop guidelines for creating grazing angle-balanced training datasets. The primary goal is to understand the acoustic dependency on grazing angle for the specific seafloor type in the available data and establish whether signals at similar angles (e.g., 30° vs 35°) are acoustically equivalent, while clearly differentiating ranges where significant changes occur (e.g., 30° vs 90°).

Project Plan:

Core Requirements (Essential):

1. Statistical characterization of seafloor signals across grazing angles: Extract and analyze acoustic features from seafloor return signals as a function of grazing angle using the extensive seafloor dataset

2. Similarity analysis between adjacent angles: Develop metrics to quantify acoustic similarity between signals at neighboring grazing angles (e.g., 30° vs 32° vs 35°) using signal processing and statistical techniques

3. Threshold identification for significant differences: Determine critical grazing angle thresholds where acoustic characteristics change significantly, identifying natural breakpoints in the angular dependency

4. Angle range segmentation: Propose optimal grazing angle ranges (e.g., 30°-45°, 45°-60°, etc.) based on acoustic similarity analysis that can be used for balanced detector training

5. Validation of proposed ranges: Verify that signals within proposed ranges are acoustically similar while signals across different ranges show significant differences

6. Documentation of findings: Create comprehensive guidelines for grazing angle-based seafloor data stratification for use in detector training pipelines

Additional Tasks (Optional, if time permits):

7. Physical interpretation of angular dependencies: Correlate observed acoustic variations with known scattering phenomena and seafloor interaction physics

8. Feature-specific angular analysis: Investigate how different acoustic features (time-domain, frequency-domain, statistical) vary with grazing angle to identify the most angle-sensitive characteristics

9. Optimal sampling strategies: Develop recommendations for balanced seafloor data sampling across the identified grazing angle ranges to maximize detector robustness

10. Environmental factor analysis: Investigate whether grazing angle dependencies vary with other environmental parameters available in the dataset (depth, seafloor type indicators, etc.)

11. Comparative analysis with synthetic models: Compare observed angular dependencies with theoretical scattering models to validate findings against established acoustic theory

12. Automated range selection algorithms: Develop algorithmic approaches to automatically determine optimal grazing angle ranges from new seafloor datasets

TOPIC 14: Temporal Windowing Optimization for Target-Specific Acoustic Feature Extraction

Level: Master’s (9-12 months)

Category: Methodological comparison

Related to: «Improving low-frequency sonar mine detection through near-seafloor echo removal», «Feature reduction and interpretability analysis for optimized low-frequency sonar mine detection» (papers in development, working titles)

Description:

This project investigates the optimization of temporal windowing strategies to isolate target-specific acoustic information from sonar return signals. Currently, return signals contain comprehensive acoustic information from extended time windows, including contributions from nearby objects and environmental reflections. The student will develop and evaluate methods to identify and extract the most discriminative temporal segments, focusing specifically on post-specular echo regions that contain target-specific scattering characteristics. The primary goal is to determine whether temporal segmentation strategies (such as isolating information after the specular/geometric echo) can improve detection and classification performance by reducing environmental noise and focusing on target-relevant acoustic signatures.

Project Plan:

Core Requirements (Essential):

1. Specular echo identification algorithms: Develop robust methods to automatically identify the timing of specular/geometric echoes in both target and seafloor return signals using signal processing techniques

2. Temporal segmentation strategies: Implement and compare different windowing approaches, including pre-specular, post-specular, and hybrid windowing schemes for both target and seafloor signals

3. Comparative performance evaluation: Train and evaluate classifiers using different temporal segmentation strategies to assess the impact on detection and classification performance

4. Feature analysis across temporal windows: Investigate how acoustic features change across different temporal segments and identify which time regions contain the most discriminative information

5. Signal-to-noise ratio analysis: Quantify how temporal windowing affects signal quality and target-to-background discrimination across different segmentation strategies

6. Validation across target types: Evaluate the effectiveness of temporal windowing optimization across different target geometries and burial conditions in the LF-SAS dataset

Additional Tasks (Optional, if time permits):

7. Adaptive windowing algorithms: Develop dynamic windowing strategies that automatically adjust temporal segments based on signal characteristics and environmental conditions

8. Physics-informed segmentation: Incorporate acoustic scattering theory to guide optimal temporal window selection based on expected scattering mechanisms for different target types

9. Multi-window ensemble approaches: Investigate combining information from multiple temporal segments to create robust detection algorithms that leverage complementary acoustic information

10. Computational efficiency analysis: Evaluate the computational benefits of processing reduced temporal segments versus full-length signals for real-time detection applications

11. Cross-validation with synthetic data: Validate temporal windowing findings using synthetic return signals where ground truth temporal structure is precisely known

12. Operational deployment guidelines: Create practical recommendations for implementing optimal temporal windowing strategies in operational sonar detection systems

Y casi dos años en el proyecto

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