Robust Sequential Tracking via Bounded Information Geometry and Non-Parametric Field Actions
This addresses the issue of robust sequential tracking for applications such as autonomous systems and financial modeling, though it appears incremental as it builds on existing inference architectures.
The paper tackles the problem of sequential inference being compromised by extreme outliers, which cause unbounded covariance inflation and mean divergence in state-of-the-art estimators. It resolves this by using bounded information geometry and non-parametric field actions to truncate outliers, achieving robust estimation across domains like LiDAR tracking, cryptocurrency order flow, and quantum state tomography.
Standard sequential inference architectures are compromised by a normalizability crisis when confronted with extreme, structured outliers. By operating on unbounded parameter spaces, state-of-the-art estimators lack the intrinsic geometry required to appropriately sever anomalies, resulting in unbounded covariance inflation and mean divergence. This paper resolves this structural failure by analyzing the abstraction sequence of inference at the meta-prior level (S_2). We demonstrate that extremizing the action over an infinite-dimensional space requires a non-parametric field anchored by a pre-prior, as a uniform volume element mathematically does not exist. By utilizing strictly invariant Delta (or ν) Information Separations on the statistical manifold, we physically truncate the infinite tails of the spatial distribution. When evaluated as a Radon-Nikodym derivative against the base measure, the active parameter space compresses into a strictly finite, normalizable probability droplet. Empirical benchmarks across three domains--LiDAR maneuvering target tracking, high-frequency cryptocurrency order flow, and quantum state tomography--demonstrate that this bounded information geometry analytically truncates outliers, ensuring robust estimation without relying on infinite-tailed distributional assumptions.