arXiv subjects

Yuanhao Cheng

Publications and source records attributed to Yuanhao Cheng.

3 recordsLinked to original sources

arXiv2025

The PHD/CPHD filter for Multiple Extended Target Tracking with Trajectory Set Theory and Explicit Shape Estimation

In this paper, we propose two methods for tracking multiple extended targets or unresolved group targets with elliptical extent shape. These two methods are deduced from the famous Probability Hypothesis Density (PHD) filter and the Cardinality-PHD (CPHD) filter, respectively. In these two methods, Trajectory Set Theory (TST) is combined to establish the target trajectory estimates. Moreover, by employing a decoupled shape estimation model, the proposed methods can explicitly provide the shape estimation of the target, such as the orientation of the ellipse extension and the length of its two axes. We derived the closed Bayesian recursive of these two methods with stable trajectory generation and accurate extent estimation, resulting in the TPHD-E filter and the TCPHD-E filter. In addition, Gaussian mixture implementations of our methods are provided, which are further referred to as the GM-TPHD-E filter and the GM-TCPHD-E filters. We illustrate the ability of these methods through simulations and experiments with real data. These experiments demonstrate that the two proposed algorithms have advantages over existing algorithms in target shape estimation, as well as in the completeness and accuracy of target trajectory generation.

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arXiv2024

Data-Driven Quantification of Battery Degradation Modes via Critical Features from Charging

Battery degradation modes influence the aging behavior of Li-ion batteries, leading to accelerated capacity loss and potential safety issues. Quantifying these aging mechanisms poses challenges for both online and offline diagnostics in charging station applications. Data-driven algorithms have emerged as effective tools for addressing state-of-health issues by learning hard-to-model electrochemical properties from data. This paper presents a data-driven method for quantifying battery degradation modes. Ninety-one statistical features are extracted from the incremental capacity curve derived from 1/3C charging data. These features are then screened based on dispersion, contribution, and correlation. Subsequently, machine learning models, including four baseline algorithms and a feedforward neural network, are used to estimate the degradation modes. Experimental validation indicates that the feedforward neural network outperforms the others, achieving a root mean square error of around 10\% across all three degradation modes (i.e., loss of lithium inventory, loss of active material on the positive electrode, and loss of active material on the negative electrode). The findings in this paper demonstrate the potential of machine learning for diagnosing battery degradation modes in charging station scenarios.

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arXiv2024

Variational Bayesian Inference for Multiple Extended Targets or Unresolved Group Targets Tracking

In this work, we propose a method for tracking multiple extended targets or unresolvable group targets in a clutter environment. Firstly, based on the Random Matrix Model (RMM), the joint state of the target is modeled as the Gamma Gaussian Inverse Wishart (GGIW) distribution. Considering the uncertainty of measurement origin caused by the clutters, we adopt the idea of probabilistic data association and describe the joint association event as an unknown parameter in the joint prior distribution. Then the Variational Bayesian Inference (VBI) is employed to approximately solve the non-analytical posterior distribution. Furthermore, to ensure the practicability of the proposed method, we further provide two potential lightweight schemes to reduce its computational complexity. One of them is based on clustering, which effectively prunes the joint association events. The other is a simplification of the variational posterior through marginal association probabilities. Finally, the effectiveness of the proposed method is demonstrated by simulation and real data experiments, and we show that the proposed method outperforms current state-of-the-art methods in terms of accuracy and adaptability.

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