Interpretable Augmented Physics-Based Model for Estimation and Tracking

TL;DR

This paper introduces a constrained estimation method for augmented physics-based models that combines neural networks with known physics to improve tracking accuracy while maintaining interpretability.

Contribution

It proposes a novel constrained estimation strategy that keeps the augmented model close to the physics-based model, enhancing flexibility and interpretability.

Findings

Improved tracking accuracy in radar scenarios.

Enhanced interpretability of estimated states.

Trade-offs between model flexibility and constraint enforcement.

Abstract

State-space estimation and tracking rely on accurate dynamical models to perform well. However, obtaining an vaccurate dynamical model for complex scenarios or adapting to changes in the system poses challenges to the estimation process. Recently, augmented physics-based models (APBMs) appear as an appealing strategy to cope with these challenges where the composition of a small and adaptive neural network with known physics-based models (PBM) is learned on the fly following an augmented state-space estimation approach. A major issue when introducing data-driven components in such a scenario is the danger of compromising the meaning (or interpretability) of estimated states. In this work, we propose a novel constrained estimation strategy that constrains the APBM dynamics close to the PBM. The novel state-space constrained approach leads to more flexible ways to impose constraints than the traditional APBM approach. Our experiments with a radar-tracking scenario demonstrate different aspects of the proposed approach and the trade-offs inherent in the imposed constraints.