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A Bayesian expectation maximization algorithm for state estimation of intelligent vehicles considering data loss and noise uncertainty

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Science China(Technological Sciences) 2025年第2期68卷 257-268页

作者： Yan WANG Feng TIAN Jianqiang WANG Keqiang LI Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China

Sideslip angle,yaw rate,and vehicle velocity are essential for intelligent vehicle *** these vehicle states are not measured directly,some Kalman-based approaches have been developed to estimate these states using in-vehicle ***,the existing studies seldom account for the influence of sensor data loss on estimation *** addition,the process and measurement noise change during the estimation process because of the various driving *** address these problems,an expectation-maximization robust extended Kalman filter(EMREKF)is ***,a robust extended Kalman filter(REKF)is developed to deal with the impact of missing ***,an improved expectation maximization(EM)algorithm that considers data loss is presented to update the noise parameter of the REKF ***,the improved EM is fused with the REKF to form the EMREKF to estimate vehicle *** experimental results demonstrate that the EMREKF outperforms EKF,REKF,and maximum correntropy criterion EKF for various degrees of data loss and the proposed algorithm has a strong adaptive ability to different driving conditions.

关键词： intelligent vehicles state estimation expectation maximization method robust extended Kalman filter

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Diversity combining for DS/SS systems with time-varying correlated fading branches

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IEEE TRANSACTIONS ON COMMUNICATIONS 2003年第2期51卷 284-295页

作者： Gao, W Tsai, S Lehnert, JS Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Diversity combining of multiple time varying and correlated fading branches is investigated for direct-sequence spread-spectrum systems. The correlated branches are modeled and estimated jointly as a vector autoregressive (VAR) process. The joint estimation is shown to provide performance gain over separate estimation of the fading branches. The parameter matrices of the VAR model are estimated via the method of expectation maximization (EM) with two algorithms. The first algorithm, using results from Kalman smoothing, provides a closed-form solution to the maximization problem in the. iterative EM procedure. However, the iterative EM-Kalman algorithm operates repeatedly on a batch of training data and results in large storage requirements and long processing delays. To overcome these disadvantages, a new algorithm with only forward-time recursions is proposed that approximates the iterative EM solution and efficiently adapts to slowly changing Doppler spreads. As a result, the new algorithm significantly reduces memory and training sequence requirements. Through computer simulations, a near ideal bit-error rate performance is found for both algorithms, and the efficacy of the new adaptive algorithm for channels with changing Doppler spreads is demonstrated.

关键词： autoregressive processes correlated fading channels direct-sequence spread-spectrum communication diversity methods expectation maximization method maximum-likelihood estimation

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Non-stationary sources separation based on maximum likelihood criterion using source temporal-spatial model

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NEUROCOMPUTING 2018年 275卷 341-349页

作者： Li, Jiong Zhang, Hang Fan, Menglan Zhang, Jiang PLA Univ Sci & Technol Coll Commun & Engn 1 Haifuxiang Nanjing Jiangsu Peoples R China Nanjing Inst Telecommun Engn 18 Houbiaoying Nanjing Jiangsu Peoples R China

In this paper, we propose a new non-stationary sources separation algorithm, which is referred to as autoregressive hidden Markov Gaussian process (AR-HMGP), in which the sources are non-stationary and temporally correlated. For the proposed algorithm, a generative model is employed to track the non-stationarity of the source where the temporal dependencies of sources are represented by autoregressive model (AR) and the distribution of the associated innovation process is described using non-stationary Gaussian process with hidden Markov model (HMM). We further explore the maximum likelihood (ML) method to estimate the parameters of the source model by using the expectation maximum (EM) algorithm. Our important findings reveal that (a) AR-HMGP algorithm outperforms the other three BSS algorithms for non-stationary sources separation, the instantaneous mixture system is also well corroborated with the effectiveness of our algorithm;(b) both independent and dependent non-stationary sources have been successfully separated;(c) the proposed algorithm is robust with respect to noise, while the other three algorithms are not. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Blind source separation Maximum likelihood expectation maximization method Non-stationary signal processing Autoregressive model Hidden Markov model

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Hyperspectral fluorescence tomography of quantum dots

Hyperspectral fluorescence tomography of quantum dots

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Conference on Optical Tomography and Spectroscopy of Tissue IX

作者： Klose, Alexander D. Columbia Univ New York NY 10032 USA

ISBN: (纸本)9780819484338

Hyperspectral excitation-resolved fluorescence tomography (HEFT) exploits the spectrally-dependent absorption properties of biological tissue for recovering the unknown three-dimensional (3D) fluorescent reporter distribution inside tissue. Only a single light source with macro-illumination and wavelength-discrimination is required for the purpose of light emission stimulation and 3D image reconstruction. HEFT is built on fluorescent sources with a relatively broad spectral absorption profile (quantum dots) and a light propagation model for strongly absorbing tissue between wavelengths 560 nm and 660 nm (simplified spherical harmonics - SPN, - equations). The measured partial current of fluorescence light is cast into an algebraic system of equations, which is solved for the unknown quantum dot distribution with an expectation-maximization (EM) method. HEFT requires no source-detector multiplexing for 3D image reconstruction and, hence, offers a technologically simple design.

关键词： Fluorescence tomography spectral imaging quantum dots hyperspectral excitation simplified spherical harmonics equations expectation maximization method small animal imaging

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Hyperspectral Fluorescence Tomography Of Quantum Dots Using The Simplified Spherical Harmonics Equations

Hyperspectral Fluorescence Tomography Of Quantum Dots Using ...

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Conference on Diffuse Optical Imaging III

作者： Klose, Alexander D. Tekabe, Yared Johnson, Lynne Columbia Univ Dept Radiol New York NY 10032 USA Columbia Univ Dept Med New York NY 10032 USA

ISBN: (纸本)9780819486851

The proposed image reconstruction method exploits the spectrally dependent absorption properties of biological tissue and quantum dots for recovering the three-dimensional reporter distribution. Only a single light source with macro-illumination needs to be used for the purpose of light emission stimulation and image reconstruction. The light propagation in strongly absorbing tissue is modeled with the simplified spherical harmonics (SPN) equations.

关键词： Fluorescence tomography spectral imaging quantum dots hyperspectral excitation simplified spherical harmonics equations expectation maximization method small animal imaging

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SAR Image Ship Target Detection under Complex Background 2020

SAR Image Ship Target Detection under Complex Background

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3rd International Conference on Image and Graphics Processing (ICIGP)

作者： Qiao, Yulong Men, Xiaoyong Zhang, Shu Harbin Engn Univ 145 Nantong St Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781450377201

As the first step of the automatic image interpretation system, the automatic detection of the target must be accurate and fast. For synthetic aperture radar (SAR) images, CFAR detection algorithm is the most commonly used target detection framework. In CFAR algorithm, the modeling of background clutter is very important, because the detection threshold is calculated based on this model. The Gumbel Mixed (GM) distribution model is proposed to model the background clutter, and the expected maximization method (EM) is used to estimate the parameters of the model, and Newton iteration method is applied to obtain the detection threshold. The model is effective in modeling complex statistics, including but not limited to statistics involving heavy tail distribution. At the same time, os-cfar detection algorithm of ordered statistics based on the model was applied to detect ship targets in SAR images. The experimental data set was derived from public SAR images, and the effect was significantly improved compared with traditional methods.

关键词： SAR Automatic detection Background clutter Parameter estimation Gumbel Mixed distribution expectation maximization method Newton iteration method OS CFAR

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A Comparative Analysis of Supervised Land Cover Classification using Multi-Polarized PALSAR EM Image Fusion 9

A Comparative Analysis of Supervised Land Cover Classificati...

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9th Int Conf Ind Information Syst (ICIIS2014)

作者： Mittal, Vikas Singh, D. Saini, L. M. NIT Kurukshetra ECE Dept Kurukshetra Haryana India IIT Roorkee Elect & Comm Engn Dept Uttarakhand India NIT Elect Engn Dept Kurukshetra Haryana India

ISBN: (纸本)9781479964994

Complementary information contained in each of the individual polarization mode of fully polarimetric PALSAR data is fused using expectation maximization (EM) algorithm to improve information contents in the fused image. A comparative analysis of supervised classifiers viz. parallelepiped and minimum distance is accomplished to assess the suitability of the particular classifier. It is also demonstrated that fusion indeed improves various figures of merit such as producer, user and overall accuracies.

关键词： PALSAR image data expectation maximization method multi-polarized image fusion land cover classification supervised classification algorithms

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Local regression modeling for accurate analysis of probability of detection data

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MATERIALS EVALUATION 2002年第12期60卷 1438-1443页

作者： McCulloch, CC Murphy, J Gen Elect Gobal Res Ctr Moscow 123098 Russia Gen Elect Aircraft Engines Cincinnati OH USA

The currently accepted method of modeling a-hat versus a eddy current data for probability of detection calculations involves time consuming and highly subjective operator intervention. We propose a new method which removes all dependence on the operator and therefore enjoys 100% repeatability. We model the a-hat versus a response using local regression and calculate confidence bounds on probability of detection and A90 threshold curves using the bootstrap method. Censored data are modeled via the expectation maximization algorithm and repeated measure correlations are fully estimated. We present results on four eddy current data sets to compare our new method with the current industry standard method.

关键词： a-hat versus a eddy current testing expectation maximization method bootstrap confidence intervals

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