On robust estimation of the Gauss–Markov model with a singular covariance matrix

Fang, Xing; Hu, Yu; Wang, Bin; Kutterer, Hansjörg; Zeng, Wenxian; Li, Dawei

doi:10.1016/j.measurement.2023.113834

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…where v is the normalized distance; d ik denotes the distance between the i-th sample point and the best Gaussian component µ k ; σ is the arithmetic square root of the variance of the k-th Gaussian component σ 2 k ; c 0 and c 1 are experience constants related to the scenarios, with ranges of 1-1.5 and 2.5-8, respectively [33][34][35].…”

Section: Posterior Probability Penaltymentioning

confidence: 99%

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

Zhang,

Zhao,

2024

Applied Sciences

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The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments.

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Section: Posterior Probability Penaltymentioning

confidence: 99%

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

Zhang,

Zhao,

2024

Applied Sciences

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“…It maps linear and indivisible data into high-dimensional feature space through nuclear technology, so as to classify them in high-dimensional space. The following is a brief introduction to its principle [21].…”

Section: Svm Multi-classification Algorithmmentioning

confidence: 99%

Chord Recognition in Music Using a Robust Pitch Class Profile (PCP) Feature and Support Vector Machines (SVM)

Kamonsantiroj

2024

IJIIS Int. J. Informatics Inf. Syst.

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Music serves as a powerful and immediate avenue for the expression of emotions, and a nuanced understanding of musical compositions is crucial for accurately interpreting and appreciating them. This research centers on the examination of robust Pitch Class Profile (PCP) features and Support Vector Machine (SVM) in the realm of music analysis. The initial phase of the study delves into the exploration of pertinent concepts and a myriad of resilient Constant-Q Transform (CQT) methods used in describing chord spectra for audio analysis. Subsequently, the paper elucidates the intrinsic correlation between SVM and speech tonality, outlining the design of a comprehensive system for music chord recognition. Rigorous testing of the system's performance follows, with a particular emphasis on evaluating the recognition rate. The results of these tests underscore the significant enhancement in music chord recognition achieved by the system, highlighting the pivotal role played by robust feature optimization and SVM pattern in bolstering its efficacy. This research not only contributes to the theoretical understanding of music analysis but also provides practical insights into improving the accuracy of music chord recognition systems through innovative feature selection and machine learning techniques.

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Toward a unified approach to the total least-squares adjustment

Hu,

Fang,

Zeng

2024

J Geod

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On robust estimation of the Gauss–Markov model with a singular covariance matrix

Cited by 4 publications

References 24 publications

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

Chord Recognition in Music Using a Robust Pitch Class Profile (PCP) Feature and Support Vector Machines (SVM)

Toward a unified approach to the total least-squares adjustment

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