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On the application of the expectation-maximisation algorithm to the relative sensor registration problem

IET RADAR SONAR AND NAVIGATION 2013年第2期7卷 191-203页

作者： Fortunati, Stefano Gini, Fulvio Farina, Alfonso Graziano, Antonio Greco, Maria S. Giompapa, Sofia Univ Pisa Dept Ingn Informaz I-56122 Pisa Italy SELEX ES I-00123 Rome Italy

An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this study, an algorithm based on the expectation-maximisation approach is proposed to estimate all the registration errors involved in the grid-locking problem, that is, attitude, measurement and position biases. Its statistical performance is investigated by Monte Carlo simulation and compared with that of a previously derived linear least squares estimator and to the hybrid Cramer-Rao lower bound.

关键词： Monte Carlo simulation multisensor integration registration bias errors expectation-maximisation algorithm relative sensor registration problem optimisation sensor fusion grid-locking process Cramér–Rao lower bound Optimisation techniques Monte Carlo methods Sensing devices and transducers

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Bilayered Anatomically-constrained Split-and-Merge expectation maximisation algorithm (BiASM) for Brain Segmentation

Bilayered Anatomically-constrained Split-and-Merge Expectati...

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Conference on Medical Imaging - Image Processing

作者： Sudre, Carole H. Cardoso, M. Jorge Ourselin, Sebastien UCL CMIC London England UCL DRC London England

ISBN: (纸本)9780819498274

Dealing with pathological tissues is a very challenging task in medical brain segmentation. The presence of pathology can indeed bias the ultimate results when the model chosen is not appropriate and lead to mis-segmentations and errors in the model parameters. Model fit and segmentation accuracy are impaired by the lack of flexibility of the model used to represent the data. In this work, based on a finite Gaussian mixture model, we dynamically introduce extra degrees of freedom so that each anatomical tissue considered is modelled as a mixture of Gaussian components. The choice of the appropriate number of components per tissue class relies on a model selection criterion. Its purpose is to balance the complexity of the model with the quality of the model fit in order to avoid overfitting while allowing flexibility. The parameters optimisation, constrained with the additional knowledge brought by probabilistic anatomical atlases, follows the expectation maximisation (EM) framework. Split-and-merge operations bring the new flexibility to the model along with a data-driven adaptation. The proposed methodology appears to improve the segmentation when pathological tissue are present as well as the model fit when compared to an atlas-based expectation maximisation algorithm with a unique component per tissue class. These improvements in the modelling might bring new insight in the characterisation of pathological tissues as well as in the modelling of partial volume effect.

关键词： expectation-maximisation algorithm segmentation Gaussian mixture model model selection split-and-merge operations pathologies

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Scalable spatiotemporal regression model based on Moran's eigenvectors

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2023年第1期37卷 162-188页

作者： Nishi, Hayato Asami, Yasushi Baba, Hiroki Shimizu, Chihiro Hitotsubashi Univ Ctr Promot Social Data Sci Educ & Res Tokyo Japan Univ Tokyo Dept Urban Engn Tokyo Japan Kyoto Univ Ctr Southeast Asian Studies Hakubi Ctr Adv Res Kyoto Japan

We propose a scalable regression model with spatially and temporally varying coefficients based on Moran's eigenvectors and efficient computation algorithms. Regression models that consider spatiotemporal non-stationarity are important because many real-world datasets, such as housing prices, are tied to geographical and temporal locations. Although geographically weighted regression (GWR) and its variants are widely used to model spatially varying coefficients, they cannot handle large datasets. We employ an alternative modelling method of spatially varying coefficients based on Moran's eigenvectors and extend it to handle large spatiotemporal datasets. Additionally, we introduce a scalable learning algorithm that exploits the model structures based on the Kalman filter and the expectation-maximisation algorithm. Our scalable algorithm is efficient even for large datasets that cannot be handled by GWR. To evaluate the performance of the proposed model, we applied it to a housing market dataset collected in Tokyo, Japan. The results show that the predictive performance of the proposed model is comparable to that of GWR while increasing the computational speed. Moreover, larger datasets can accelerate the algorithm convergence.

关键词： Eigenvector spatial filtering spatiotemporal modelling state-space model expectation-maximisation algorithm spatially varying coefficients

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Sparse channel estimation algorithms for OTFS system

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IET COMMUNICATIONS 2022年第18期16卷 2158-2170页

作者： Ouchikh, Rabah Aissa-El-Bey, Abdeldjalil Chonavel, Thierry Djeddou, Mustapha Ecole Mil Polytech Lab Telecommun Bordj El Bahri Algeria IMT Atlantique UMR CNRS 6285 Lab STICC F-29238 Brest France

Orthogonal time-frequency space (OTFS) modulation, which has recently been proposed in the literature, is one of the promising techniques designed in the 2D Delay-Doppler domain adapted to combat high Doppler fading channels. However, channel estimation in high Doppler scenarios in advanced mobile-communication systems is still a challenging task. In this paper, the problem of channel estimation in the Delay-Doppler domain of the OTFS is focused on. First, a simple adaptation of the generalized orthogonal matching pursuit procedure, which will serve as a baseline method in this work, is proposed. Then, iterative algorithms are derived beneficiating from the sparsity of the channel. The unknown channel vector is separated into an unknown sparse support vector corresponding to the delay and Doppler taps, and an unknown vector of channel gains. These algorithms involve l(1)-norm minimization and a two-stage iterative procedure to recover alternatively the channel support and its coefficients. The estimation problem is also addressed from a Bayesian point of view. The sparse representation is reformulated as a specific marginalization of the maximum a posteriori problem on the support of the channel. To deal with the intractability of this problem, two existing techniques are adapted to this context, namely: The Monte Carlo Markov chain with the Gibbs sampler and variational mean-field approximation with the variational Bayesian expectation-maximization procedure. Finally, to assess the performance of the proposed algorithms, their complexity and performance are compared against existing methods. Experimental tests, conducted in high-mobility scenarios and low-latency applications, show that the proposed schemes are slightly more expensive in terms of complexity load but perform significantly better in terms of normalized mean square error and bit error rate.

关键词： Monte Carlo methods high-mobility scenarios sparse representation unknown channel vector fading channels two-stage iterative procedure mean square error methods Bayes methods generalized orthogonal matching pursuit procedure estimation problem maximum likelihood estimation high Doppler scenarios time-frequency analysis sparse channel estimation algorithms channel estimation advanced mobile-communication systems channel support orthogonal time-frequency space modulation minimisation error statistics high Doppler fading channels simple adaptation variational Bayesian expectation-maximization procedure iterative methods Delay-Doppler domain OTFS system posteriori problem unknown sparse support vector unknown vector channel gains expectation-maximisation algorithm Markov processes

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Novel optimal time selection method for synthetic aperture radar data processing via expectation-maximization algorithm

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ELECTRONICS LETTERS 2022年第15期58卷 594-596页

作者： Cao, Rui Wang, Yong Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China

A novel optimal time selection method for synthetic aperture radar (SAR) data processing via the expectation-maximization (EM) algorithm is proposed, which can reduce the computational complexity and improve the image quality simultaneously. First, the Doppler frequency feature after the SAR processing is analyzed from the point of probability. Then, the Doppler frequency is modelled as the Gaussian mixture model (GMM), and the issue of optimal time selection can be implemented by the parameter estimation of GMM. The results of simulated and real measured data are given to demonstrate the effectiveness of the proposed method.

关键词： simulated measured data radar imaging SAR data processing Gaussian mixture model expectation-maximization algorithm Gaussian processes Doppler frequency feature image quality real measured data optimal time selection method synthetic aperture radar computational complexity expectation-maximisation algorithm synthetic aperture radar data processing mixture models parameter estimation

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CAUSAL INFERENCE FOR TIME-VARYING TREATMENTS IN LATENT MARKOV MODELS: AN APPLICATION TO THE EFFECTS OF REMITTANCES ON POVERTY DYNAMICS

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ANNALS OF APPLIED STATISTICS 2022年第3期16卷 1962-1985页

作者： Tullio, Federico Bartolucci, Francesco Bank Italy Directorate Gen Econ Stat & Res Rome Italy Univ Perugia Dept Econ Perugia Italy

To assess the effectiveness of remittances on the poverty level of recipient households, we propose a causal inference approach that may be applied with longitudinal data and time-varying treatments. The method relies on the integration of a propensity score based technique, the inverse propensity weighting, with a general latent Markov (LM) framework. It is particularly useful when the outcome of interest is a characteristic that is not directly observable, and the analysis is focused on: (i) clustering units in a finite number of classes according to this latent characteristic and (ii) modelling the evolution of this characteristic across time depending on the received treatment. Parameter estimation is based on a two-step procedure. First, individual propensity score weights are computed accounting for predetermined covariates. Then, a weighted version of the standard LM model likelihood, based on such weights, is maximised by means of an expectation-maximisation algorithm or, alternatively, adopting a stepwise procedure. Finite-sample properties of the proposed estimators are studied by simulation. The application is focused on the effect of remittances on the poverty status of Ugandan households, based on a longitudinal survey spanning the period 2009-2014, and where manifest variables are indicators of deprivation. We find that remittances reduce the probability of falling into poverty, whereas they exert no impact on the probability of moving out of poverty.

关键词： Causal inference expectation-maximisation algorithm potential outcomes weighted maximum likelihood

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Coherent compensation and high-resolution technology of multi-band inverse synthetic aperture radar fusion imaging

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IET RADAR SONAR AND NAVIGATION 2021年第2期15卷 167-180页

作者： Zhu, Xiaoxiu Liu, Limin Guo, Baofeng Hu, Wenhua Ma, Juntao Shi, Lin Army Engn Univ Dept Elect & Opt Engn Shijiazhuang Campus Shijiazhuang Hebei Peoples R China

Multi-band inverse synthetic aperture radar (ISAR) fusion imaging technology can effectively improve the range resolution without incurring high hardware cost. The coherent phase between sub-bands is a prerequisite to achieve multi-band ISAR fusion imaging. Here, a joint approach of coherent compensation and high-resolution imaging is proposed to compensate the incoherent phase and obtain high-resolution ISAR fusion images. First, an incoherent phase estimation model based on sparse representation is established, and the phase estimation accuracy is improved by a modified coherent dictionary in case of off-grid. Then, a multi-band ISAR fusion imaging model based on sparse representation is established. The complex Gaussian scale mixture priors and the complex Gaussian priors are imposed on the scatterers and noise, respectively. The solution is derived in the complex domain based on the variational Bayesian expectation maximization framework. The proposed method can not only achieve better incoherent phase compensation in the case of off-grid, but also obtain high-quality ISAR fusion images under low signal-to-noise ratio and low bandwidth sampling ratio. Experimental results verify the effectiveness and robustness of the proposed method based on both numerical simulations and real data.

关键词： high-resolution imaging modified coherent dictionary high-quality ISAR fusion images phase estimation accuracy coherent phase phase estimation multiband inverse synthetic aperture radar fusion imaging technology sparse representation incoherent phase compensation high-resolution ISAR fusion images radar imaging range resolution multiband ISAR fusion imaging model high hardware cost coherent compensation complex Gaussian scale mixture priors radar resolution image fusion incoherent phase estimation model high-resolution technology synthetic aperture radar expectation-maximisation algorithm

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Nonlinear state-space system identification with robust laplace model

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INTERNATIONAL JOURNAL OF CONTROL 2021年第6期94卷 1492-1501页

作者： Liu, Xin Yang, Xianqiang Liu, Xiaofeng Hohai Univ Coll IoT Engn Changzhou Peoples R China Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin Heilongjiang Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin Heilongjiang Peoples R China Hohai Univ Changzhou Key Lab Robot & Intelligent Technol Changzhou Peoples R China Hohai Univ Jiangsu Key Lab Special Robots Changzhou Peoples R China

This paper investigates a robust identification solution for the nonlinear state-space model in which the outputs are polluted by unknown outliers. The problem of outliers is frequently encountered in practical industries that can greatly challenge the modelling of industrial processes. In order to overcome the obstacles brought by the outliers, the heavy-tailed Laplace distribution is applied to describe the output measurement process. Specifically, the Laplace distribution can be decomposed as a scale mixture of Gaussian distributions, which makes it robust for the outliers. The unknown model parameters are estimated with the expectation-maximisation algorithm while the particle smoother is used to solve the latent state estimation problem. The usefulness and robustness of the proposed algorithm are verified through the numerical examples including the model of a common chemical process.

关键词： Nonlinear system identification robustness laplace distribution expectation-maximisation algorithm particle smoother

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Online variational inference on finite multivariate Beta mixture models for medical applications

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IET IMAGE PROCESSING 2021年第9期15卷 1869-1882页

作者： Manouchehri, Narges Kalra, Meeta Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada

Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous development and various statistical approaches have been suggested. In particular, data clustering has received a lot of attention. Finite mixture models have been revealed to be one of the flexible and popular approaches in data clustering. Considering mixture models, three crucial aspects should be addressed. The first issue is choosing a distribution which is flexible enough to fit the data. In this paper, a model based on multivariate Beta distributions is proposed. The two other challenges in mixture models are estimation of model's parameters and model complexity. To tackle these challenges, variational inference techniques demonstrated considerable robustness. In this paper, two methods are studied, namely, batch and online variational inferences and the models are evaluated on four medical applications including image segmentation of colorectal cancer, multi-class colon tissue analysis, digital imaging in skin lesion diagnosis and computer aid detection of Malaria.

关键词： skin statistical analysis Patient diagnostic methods and instrumentation variational inference techniques Biology and medical computing Gaussian processes mixture models image segmentation cancer multivariate Beta distributions statistical approaches Bayes methods flexible approaches patient diagnosis inference mechanisms online variational inference Biomedical measurement and imaging learning (artificial intelligence) machine learning data clustering finite multivariate Beta mixture models Other topics in statistics expectation-maximisation algorithm

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A blind detection method for spatial modulation in MIMO communications

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IET COMMUNICATIONS 2021年第4期15卷 513-525页

作者： Khanzadeh, Roya Naeiny, Mahmoud Ferdosizade Shahed Univ Dept Elect Engn Tehran Iran

Spatial modulation (SM) is one of the probable candidates to be utilised in the fifth generation of wireless networks due to its power and spectral efficiencies. Since only one active transmit antenna exists in spatial modulation, inter-channel interferences are avoided and the number of radio frequency (RF) chains is reduced. However, channel estimation is a major challenge in spatial modulation communication systems. In this study, a novel blind signal and channel estimation method for spatial modulation-based multi-input multi-output communications, based on the well-known expectation-maximisation algorithm, is presented. This blind detector requires only a few pilot symbols at the beginning of the transmission to resolve the inherent phase and permutation ambiguities. Simulation results demonstrate that the proposed detector performs very similar to the optimum detector with full channel state information and outperforms existing blind detectors.

关键词： 5G mobile communication Electromagnetic compatibility and interference multiinput multioutput communications spectral efficiencies expectation-maximisation algorithm active transmit antenna RF chains signal detection Modulation and coding methods Mobile radio systems spatial modulation communication systems channel estimation adjacent channel interference Signal detection transmitting antennas blind detector modulation blind detection method radio frequency chains antenna arrays Antenna arrays interchannel interferences MIMO communications fifth generation wireless networks radio spectrum management Communication channel equalisation and identification blind signal blind source separation MIMO communication Other topics in statistics

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