Because of the high maneuverability of targets, track breakages are common in the tracking process. With the purpose of stitching the track segments and achieving better tracking results, we adopt an algorithm to stit...
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ISBN:
(纸本)9781538676042
Because of the high maneuverability of targets, track breakages are common in the tracking process. With the purpose of stitching the track segments and achieving better tracking results, we adopt an algorithm to stitch the track segments based on expectationmaximization (EM). The EM algorithm can be used to estimate and identify the maneuvering targets' state and angular velocities simultaneously. It consists of two steps. The expectation (E) step is implemented by an extended Kalman filter (EKF) and extended Rauch-Tung-Striebel smoother (ERTSS). The maximization (M) step is implemented by genetic algorithm, which can achieve the Maximum likelihood sequence estimation for unknown parameters. Experiments show that this algorithm can achieve better tracking results. Moreover, it also exhibits good capability when estimating the unknown parameter.
This paper proposes a direct learning controller for wireless sensor and robot network to coverage an environment by utilizing expectation maximization algorithm. In addition to sensors, including high-level and low-l...
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ISBN:
(纸本)9781509021826
This paper proposes a direct learning controller for wireless sensor and robot network to coverage an environment by utilizing expectation maximization algorithm. In addition to sensors, including high-level and low-level sensors, mounted on mobile robots, low-level stationary sensors are considered to provide information for enhance the performance of coverage control. The main objective is to maximize the information quantity of high-level sensors from the sensing density generated by a group of low-level sensors. This direct method uses the parameter of basis function to design controller based on expectationmaximization (EM) algorithm. Moreover, the proposed estimation and learning law are also used in indirect method for other coverage controller. Subsequently, this paper proposes a transformation method based on EM algorithm for coverage problem with complicated sensing function such as Gaussian function. Numerical examples are introduced to demonstrate the performance of the proposed direct coverage control in wireless sensor and robot network.
This paper discusses channel estimation during uplink transmission in Cell-Free (CF) Massive MIMO (MaMIMO) systems. We model the problem as a semi-blind estimation problem with independent and identically distributed ...
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ISBN:
(纸本)9789464593617;9798331519773
This paper discusses channel estimation during uplink transmission in Cell-Free (CF) Massive MIMO (MaMIMO) systems. We model the problem as a semi-blind estimation problem with independent and identically distributed (i.i.d.) Gaussian input. Two hybrid expectationmaximization (EM) and expectation Propagation (EP) algorithms are proposed to improve convergence behavior. The first algorithm, EM-EP, adopts a vector-level EP approach by treating the per-user channel coefficients and data sequence as EP variables. To make the algorithm tractable, we use the central limit theorem (CLT) to approximate the interference terms and employ EM to construct a majorizer function for the likelihood of the received data, leading to majorization minimization. To further enhance convergence behavior, we propose a matrix-level loop-free EM-EP algorithm. In this algorithm, we treat the channel coefficients and data sequences corresponding to users using the same pilot as EP variables. This method is an alternating minimization algorithm, ensuring convergence. Our simulations verify the effectiveness of the two proposed algorithms.
Selective labels occur when label observations are subject to a decision-making process;e.g., diagnoses that depend on the administration of laboratory tests. We study a clinically-inspired selective label problem cal...
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Selective labels occur when label observations are subject to a decision-making process;e.g., diagnoses that depend on the administration of laboratory tests. We study a clinically-inspired selective label problem called disparate censorship, where labeling biases vary across subgroups and unlabeled individuals are imputed as "negative" (i.e., no diagnostic test = no illness). Machine learning models naively trained on such labels could amplify labeling bias. Inspired by causal models of selective labels, we propose Disparate Censorship expectation-maximization (DCEM), an algorithm for learning in the presence of disparate censorship. We theoretically analyze how DCEM mitigates the effects of disparate censorship on model performance. We validate DCEM on synthetic data, showing that it improves bias mitigation (area between ROC curves) without sacrificing discriminative performance (AUC) compared to baselines. We achieve similar results in a sepsis classification task using clinical data.
We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the ex...
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Large Language Models (LLMs) have revolutionized the landscape of natural language processing, demonstrating remarkable abilities across various complex tasks. However, their stateless nature limits the capability to ...
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In this paper proposed an effective fuzzy expectation-maximization phoneme prediction method in diffusion model-based dysarthria voice conversion (FEMPPDM-DVC) which is accessible to (i) training without parallel data...
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In this paper, we study the system identification problem for linear time-invariant dynamics with bilinear observation models. Accordingly, we consider a suitable parametric description for the system model and formul...
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Pre-trained language models (PLMs) that rely solely on textual corpus may present limitations in multimodal semantics comprehension. Existing studies attempt to alleviate this issue by incorporating additional modal i...
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This paper develops a general framework for dynamic models in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show tha...
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