# Robust Sequential Tracking via Bounded Information Geometry and Non-Parametric Field Actions

Source: [Robust Sequential Tracking via Bounded Information Geometry and Non-Parametric Field Actions](<https://www.sota2.com/research/paper/69b8dfe20636e5bf4afbe7b7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-18T05:00:04.467Z

Authors: Carlos C. Rodriguez

Published: 2026-03-13T21:40:44.000Z

## Abstract

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 \\nu) 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [6D Kinematic Tracking on LiDAR Kinematic Tracking Monte Carlo simulation 1,000 trials](<https://www.sota2.com/research/sota/6d-kinematic-tracking-on-lidar-kinematic-tracking-monte-carlo-simulation-1-000-trials.md>): 6D Kinematic Tracking; LiDAR Kinematic Tracking Monte Carlo simulation 1,000 trials; Mean RMSE (m): 0.67.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.13613>)

[PDF](<https://arxiv.org/pdf/2603.13613.pdf>)