arXiv subjects

Chenxin Tu

Publications and source records attributed to Chenxin Tu.

3 recordsLinked to original sources

arXiv2026

Hessian-matching Based Weighting for Attitude Determination Using Short-Range DoA Measurements with IMU Assistance

Accurate and reliable attitude determination (AD) is essential for unmanned vehicles operating in Global Navigation Satellite System (GNSS)-denied environments. Short-range wireless arrays can provide direction-of-arrival (DoA) measurements from multiple anchors, enabling AD by aligning corresponding direction vectors (DVs) expressed in the body and navigation frames. In short-range scenarios, navigation-frame DVs inherit non-negligible uncertainty induced by anchor/vehicle position errors in addition to DoA-induced errors in body-frame DVs. Moreover, due to projection and unit-norm normalization, the DV errors are generally anisotropic, which motivates a total least squares (TLS) viewpoint. This paper identifies the key modeling distinction in short-range AD, develops a TLS-consistent formulation based on the total DV error and solves the resulting covariance-weighted orthogonal Procrustes problem via a manifold Gauss--Newton method. To retain the efficiency and numerical robustness of the closed-form weighted Wahba solution, we further propose Hessian-matching based scalar weighting strategies that approximate the Hessian of Wahba formulation to the TLS formulation, including a full-attitude strategy for overall accuracy and a direction-of-interest (DOI) strategy for prioritizing a selected attitude component. Finally, we incorporate IMU-derived gravity as an additional DV pair for static initialization, leading to extended Wahba and extended TLS formulations. Simulation results demonstrate that the proposed Hessian-matching weighting improves accuracy and robustness compared with existing baselines, and that gravity-DV augmentation further reduces attitude errors and improves solution availability under limited anchor availability.

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arXiv2025

Weighted Covariance Intersection for Range-based Distributed Cooperative Localization of Multi-Vehicle Systems

Cooperative localization enables autonomous navigation for multi-vehicle systems (MVS) in GNSS-denied environments. Among the available architectures, distributed cooperative localization (DCL) is attractive for its robustness and scalability in large-scale MVS. To address the challenge of untrackable state correlations between vehicles in a distributed framework, covariance intersection (CI) has been introduced as a means to fuse inter-vehicle measurements. However, existing studies typically treat CI as a plug-in technique, directly applying traditional optimization criteria and focusing mainly on simple two-dimensional (2D) scenarios. When extended to three-dimensional (3D) cooperative localization with higher-dimensional states and pronounced disparities in scale and observability among state components, traditional CI criteria fail to maintain balanced estimation performance across the full state, and some state components suffer substantial accuracy degradation. This limitation calls for task-oriented improvements to the CI fusion process. In this paper, we introduce a weighting mechanism, called weighted covariance intersection (WCI), to regulate the CI fusion process in 3D DCL. We further develop a concurrent fusion strategy for multiple distance measurements and design a dedicated weighting matrix based on inertial navigation system (INS) error propagation. The method supports diverse MVS platforms, including unmanned aerial vehicle (UAV) swarms and autonomous ground vehicle (AGV) fleets, in complex 3D environments. Simulation results show that the proposed WCI significantly improves cooperative localization performance over traditional CI, while the distributed framework offers clear advantages over the centralized counterpart in robustness and scalability.

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arXiv2024

Parameterized TDOA: TDOA estimation for mobile target localization in a time-division broadcast positioning system

In a time-division broadcast positioning system (TDBPS), localizing mobile targets using classical time difference of arrival (TDOA) methods poses significant challenges. Concurrent TDOA measurements are infeasible because targets receive signals from different anchors and extract their transmission times at different reception times, as well as at varying positions. Traditional TDOA estimation schemes implicitly assume that the target remains stationary during the measurement period, which is impractical for mobile targets exhibiting high dynamics. Existing methods for mobile target localization are mostly specialized and rely on motion modeling and do not rely on the concurrent TDOA measurements. This issue limits their direct use of the well-established classical TDOA-based localization methods and complicating the entire localization process. In this paper, to obtain concurrent TDOA estimates at any instant out of the sequential measurements for direct use of existing TDOA-based localization methods, we propose a novel TDOA estimation method, termed parameterized TDOA (P-TDOA). By approximating the time-varying TDOA as a polynomial function over a short period, we transform the TDOA estimation problem into a model parameter estimation problem and derive the desired TDOA estimates thereafter. Theoretical analysis shows that, under certain conditions, the proposed P-TDOA method closely approaches the Cramer-Rao Lower Bound (CRLB) for TDOA estimation in concurrent measurement scenarios, despite measurements being obtained sequentially. Extensive numerical simulations validate our theoretical analysis and demonstrate the effectiveness of the proposed method, highlighting substantial improvements over existing approaches across various scenarios.

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