Custom ML Algorithms and Physics-Based Navigation Models
Enhancing AI-Driven Navigation with Sensor Fusion and Time-Series Intelligence
Case Study Summary
Client: NeoMatrix
Website: neomatrix.com.au
Industry: Positioning and Tracking Systems
Impact Metrics:
- 20% increased tracking accuracy with new sensor data
- Successfully integrated new sensor types into the navigation stack
- Route keypoints and features are correctly detected 90% of the time
Challenge
NeoMatrix is developing low-power, AI-enhanced tracking systems that function where traditional satellite navigation fails—such as inside tunnels, shipping containers, or remote environments. The challenge was to extend their proprietary tracking technology by integrating new sensor types and improving signal interpretation in noisy, real-world conditions.
My Approach
I developed a custom algorithms to better align and interpret disparate time-series data from multiple sensors. I designed mathematical and physical models to simulate sensor behavior under varying environmental conditions and built pipelines to correlate multi-modal data, enhancing system robustness. I also implemented lightweight machine learning (ML) models for forecasting and pattern recognition, enabling predictive tracking even with intermittent signals.
Results
The upgraded tracking system demonstrated improved accuracy and reliability in GPS-denied environments when incorporating the new sensor inputs. Power efficiency was preserved while enabling real-time inference and long-term device deployment with minimal maintenance.
Technical Expertise
This project leveraged time-series analysis, signal processing, and sensor fusion. Key techniques included Dynamic Time Warping, Kalman filtering and Madgwick fusion implemented in python. On the machine learning side, custom convolutional neural networks were constructed and trained using PyTorch.
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