LITRE

Layer and Interface Trackers for Radar Echograms

Sponsor: Remote Sensing Center (RSC), The University of Alabama
Role: Graduate research contributor (LAGER)

SAR CFAR JGLMB TST-GM-PHD RFS GUI

Overview

LITRE automated ice-layer tracking on airborne Synthetic Aperture Radar (SAR) echograms, replacing an error-prone manual workflow with detection-plus-tracking over long radar images.

Radar Echogram — Radar echogram to be tracked.

Technical Approach

Applied CFAR-based detection to identify candidate layer reflections across the radar image.
Implemented the JGLMB filter to track layer / interface tracks through the echogram.
Developed and tested TST-GM-PHD as a lower-cost alternative for high-throughput runs.
Built a GUI to streamline manual layer labeling and validate automated tracker output against human-labeled truth.

CFAR Illustration — CFAR detection method illustrated. Black curve is the threshold, ascope is at index 100.

Outcomes

Reduced the hands-on time required per image relative to the prior manual workflow.
Delivered a validation GUI usable by collaborators at RSC.

Layer Estimates — Layer estimates from radar detections using Trajectory Gaussian Probability Hypothesis Density filter.

Related Work

BREW was later wrapped in MATLAB to support this project.