Receding Horizon Recursive Location Estimation

This paper presents a recursive solution to the receding or moving horizon estimation (MHE) problem for nonlinear time-variant systems. We provide the conditions under which the recursive MHE is equivalent to the extended Kalman filter (EKF), regardless of the horizon size. Theoretical and empirical evidence is also provided. Moreover, we clarify the connection between MHE and factor graph optimization (FGO). We apply the recursive MHE to GNSS localization and evaluate its performance using publicly available datasets. The paper is based on the deterministic least squares framework.

Publication Details

Journal
ArXiv.org
Published
2025-06-23
Primary Topic
Time Series Analysis and Forecasting
Type
article
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article

Receding Horizon Recursive Location Estimation

ArXiv.org
Time Series Analysis and Forecasting
article

Receding Horizon Recursive Location Estimation

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Abstract

This paper presents a recursive solution to the receding or moving horizon estimation (MHE) problem for nonlinear time-variant systems. We provide the conditions under which the recursive MHE is equivalent to the extended Kalman filter (EKF), regardless of the horizon size. Theoretical and empirical evidence is also provided. Moreover, we clarify the connection between MHE and factor graph optimization (FGO). We apply the recursive MHE to GNSS localization and evaluate its performance using publicly available datasets. The paper is based on the deterministic least squares framework.

ArXiv.org
Openalex Percentile: Top 45%
Time Series Analysis and Forecasting
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Receding Horizon Recursive Location Estimation · ArXiv.org (2025) | TGRS Research Map | TGRS