Quantitative Assessment of LiDAR Technology for Detecting, Localizing, and Characterizing Underwater Munitions in Shallow Waters

Objective

The detection and classification of underwater munitions in shallow waters is a critical challenge for military operations. Traditional methods lack the resolution and adaptability needed for reliable identification. This project addressed the gap by developing and validating new methodologies for assessing Uncrewed Aerial System (UAS)-based bathymetric Light Detection and Ranging (LiDAR) systems, with a focus on the Orion Space Solutions (OSS) EDGE LiDAR platform. The objective of this project was to create a comprehensive framework for evaluating system performance, resolution capabilities, and environmental limitations, ultimately guiding operational decisions and system improvements.

Technical Approach

The research introduced three foundational components for LiDAR system assessment:

Uncertainty Quantification Modeling – to evaluate system precision and error propagation.
Modulation Transfer Function (MTF) Analysis – both theoretical and empirical, to quantify resolution under varying conditions.
End-to-End System Performance Modeling – integrating platform behavior, environmental factors, and system specifications to simulate operational outcomes.

Underwater calibration targets, particularly line targets, were developed to empirically assess resolution. These were deployed in field campaigns and used to correlate environmental metrics with MTF-derived resolution values. Synthetic data generation supports machine learning validation and performance benchmarking.

Results

Field data from Panama City, FL revealed critical limitations in the base OSS EDGE LiDAR system, particularly in mirror pointing accuracy. MTF and UQ analysis led to specific system upgrades, including reduced pointing error (from 0.25° to 0.01°), increased point density, and optimized flight parameters. Empirical MTF assessments validated these improvements and demonstrated enhanced resolution sufficient for detecting and classifying medium-to-large munitions (≥100 mm). End-to-end modeling identified key environmental constraints—such as water surface slope, turbidity, and sea state—that significantly impact resolution and informed Go/No-Go operational criteria.

Benefits

This research provides a robust framework for evaluating and improving UAS-based bathymetric LiDAR systems. Key benefits include:

Operational Guidance: Clear Go/No-Go criteria based on environmental and system parameters.
System Optimization: Quantitative tools for refining LiDAR specifications to meet resolution requirements.
Standardization: MTF-based evaluation enables cross-validation with other instruments and related environmental conditions to make informed actionable decisions.
Machine Learning Support: Synthetic prelabeled datasets enhance unsupervised and semi-supervised classification capabilities.

These advancements enable more reliable detection and classification of underwater munitions, improving safety and mission effectiveness in shallow water environments. (Project Completion - 2025)