Improving Predictive Navigation Through the Optimization of Counterfactual Track Evaluation

Alexander Stringer, Geoffrey Dolinger, Timothy Sharp, Debra Hogue, Joseph Karch, Lesya Borowska, Justin G. Metcalf

Abstract:	Abstract—Modern navigation applications are confronting the challenge of path identification and prediction. Because of the inherent noisiness of measurement techniques, positional data often contains errors. Over time, when attempting to predict future positions, these errors can compound, causing drift in the target’s path. Filtering or regression techniques can use periodic high-fidelity measurements, like those from GPS sensors, to correct positional data and reduce historic errors. These techniques have been much less effective in specialized environments where GPS is intermittent or denied. At the same time, the development of semi- and fully-autonomous systems has increased the need for accurate predictive navigation. To address this problem, previous methods were proposed for repairing tracking data using causality-aware machine learning (ML). It combined a long short-term memory (LSTM) network that predicted target paths with the non-dominated sorting genetic algorithm II (NSGAII) which identified and evaluated counterfactual paths. Here the authors expand on that work by improving the simulated data used to train and test the system, expanding the GA by adding additional first principles-based objective functions, improving upon the LSTM implementation, and utilizing Extended Kalman Filter (EKF) pre-processing. System testing is conducted on Matlab-generated navigation scenarios, and results are compared to both EKF correction and spline interpolation. The proposed counterfactual track repair (CTR) tool produces paths with less repeatability than traditional approaches. However, it is shown to consistently generate more realistic path corrections, lower error in predictions based on its corrected paths, and is highly configurable, demonstrating the value and utility of this approach. Index Terms—Machine Learning, Causal Learning, Genetic Algorithms, Navigation
Published in:	2023 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 24 - 27, 2023 Hyatt Regency Hotel Monterey, CA
Pages:	93 - 104
Cite this article:	Stringer, Alexander, Dolinger, Geoffrey, Sharp, Timothy, Hogue, Debra, Karch, Joseph, Borowska, Lesya, Metcalf, Justin G., "Improving Predictive Navigation Through the Optimization of Counterfactual Track Evaluation," 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2023, pp. 93-104. https://doi.org/10.1109/PLANS53410.2023.10140127
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In