James Brodovsky

James Brodovsky

Navigation and estimation engineer. GNSS-denied PNT, inertial navigation, and sensor fusion.

Publications

You can also find my articles on my Google Scholar profile.

Conference Proceedings

Navigation in GNSS-Denied Environments Using MEMS-Grade Sensors and Geophysical Anomalies: A Particle Filter Approach

Published in Proceedings of the ION 2026 Pacific PNT Meeting, 2026

A particle filter for GNSS-denied navigation on MEMS-grade inertial sensors, aided by gravity, magnetic, and bathymetric anomaly map matching.

Recommended citation: Brodovsky, J. and Dames, P. (2026). "Navigation in GNSS-denied Environments using MEMS-grade Sensors and Geophysical Anomalies: A Particle Filter Approach." Proceedings of the ION 2026 Pacific PNT Meeting, Honolulu, HI, pp. 399-410. https://doi.org/10.33012/2026.20618

Navigation in GNSS-Denied Environments Using MEMS-Grade Sensors and Geophysical Anomalies: A UKF Approach

Published in Proceedings of the 2026 International Technical Meeting of the Institute of Navigation, 2026

An unscented Kalman filter formulation for bounding inertial drift on MEMS-grade sensors by map matching against gravity, magnetic, and bathymetric anomaly fields.

Recommended citation: Brodovsky, J. and Dames, P. (2026). "Navigation in GNSS-denied environments using MEMS-grade sensors and geophysical anomalies: A UKF approach." Proceedings of the 2026 International Technical Meeting of the Institute of Navigation, Anaheim, CA, pp. 155-164. https://doi.org/10.33012/2026.20508

In Review

A Priori Prediction of Geophysical Anomaly Navigation Fixability via Posterior Cramér-Rao Bounds and Machine Learning

Under review in IEEE Transactions on Aerospace and Electronic Systems

Predicting achievable navigation accuracy from trajectory and map characteristics before the mission, using posterior Cramér-Rao bounds and a learned regression model.

Recommended citation: Brodovsky, J. "A Priori Prediction of Geophysical Anomaly Navigation Fixability via Posterior Cramér-Rao Bounds and Machine Learning." IEEE Transactions on Aerospace and Electronic Systems (under review).

Theses

A Comparison of the Probability Hypothesis Density Filter and the Multiple Hypothesis Tracker for Tracking Targets of Multiple Types

Published in M.S. Thesis, Temple University, 2019

M.S. thesis comparing PHD filter and MHT performance on multi-target tracking problems where targets are of heterogeneous types.

Recommended citation: Brodovsky, J. A. (2019). "A Comparison of the Probability Hypothesis Density Filter and the Multiple Hypothesis Tracker for Tracking Targets of Multiple Types." M.S. Thesis, Temple University.