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2010 49th IEEE Conference on Decision and Control

作者： Adrian Wills Thomas B. Schon Brett Ninness School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia Division of Automatic Control Linkoping University SE-581 83 Linkoping Sweden

ISBN: (纸本)9781424477456

The expectation maximisation (EM) algorithm has proven to be effective for a range of identification problems. Unfortunately, the way in which the EM algorithm has previously been applied has proven unsuitable for the commonly employed innovations form model structure. This paper addresses this problem, and presents a previously unexamined method of EM algorithm employment. The results are profiled, which indicate that a hybrid EM/gradient-search technique may in some cases outperform either a pure EM or a pure gradient-based search approach.

关键词： expectation-maximisation algorithm Technological Innovations

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Smart tracking algorithm for multi-static sonar based on expectation maximisation

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IET RADAR SONAR AND NAVIGATION 2020年第10期14卷 1624-1630页

作者： Son, Hyun-Seung Agcy Def Dev Yuseong POB 35 Daejeon South Korea

This study proposes an intelligent tracking method based on the expectation maximisation approach. To obtain the consistent tracking performance, the observer should maintain contact with the target and continuously update its tracking data. Unfortunately, a significant problem arises from the uncertainty of the target motion. The reasons behind this uncertainty include losses, time-varying noises, external inputs, and others. Above all, a sharp manoeuvre by the arbitrary acceleration input mainly degrades the tracking performance. A variety of techniques were needed and studied to enhance performance levels. This study focuses on acceleration as the cause of the uncertainty and a consistent approximation of the acceleration, leading to highly efficient tracking performance. The EM makes it possible to separate the noise term from the acceleration inputs and the noise components. Finally, a manoeuvring submarine with three-dimensional coordinates is provided as an example to show the effectiveness of the proposed algorithm.

关键词： sonar expectation-maximisation algorithm target tracking underwater vehicles vehicle dynamics tracking object tracking consistent tracking performance observer tracking data target motion time-varying noises external inputs sharp manoeuvre arbitrary acceleration input consistent approximation highly efficient tracking performance noise term acceleration inputs noise components manoeuvring submarine smart tracking algorithm multistatic sonar intelligent tracking method expectation maximisation approach

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Semi-blind MIMO-OFDM channel estimation using expectation maximisation like techniques

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IET COMMUNICATIONS 2019年第20期13卷 3452-3462页

作者： Ladaycia, Abdelhamid Belouchrani, Adel Abed-Meraim, Karim Mokraoui, Anissa Univ Paris 13 Inst Galilee Sorbonne Paris Cite L2TI Villetaneuse France Ecole Natl Polytech LDCCP lab Elect Engn Dept Algiers Algeria Univ Orleans PRISME Orleans France

This study deals with semi-blind (SB) channel estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system using maximum likelihood (ML) technique. For the ML cost optimisation function, new expectation maximisation (EM) algorithms for the channel taps estimation are introduced. Different approximation/simplification approaches are proposed for the algorithm's computational cost reduction. The first approach consists of decomposing the MIMO-OFDM system into parallel multiple-input single-output OFDM systems. The EM algorithm is then applied to estimate the MIMO channel in a parallel way. The second approach takes advantage of the SB context to reduce the EM cost from exponential to linear complexity by reducing the size of the search space. Finally, the last proposed approach uses a parallel interference cancellation technique to decompose the MIMO-OFDM system into several single-input multiple-output OFDM systems. The latter are identified in a parallel scheme and with a reduced complexity. The performance of the proposed approaches are discussed, assessed through numerical experiments and compared with respect to the Cramer Rao Bound and to other EM-based solutions reported in the literature.

关键词： OFDM modulation MIMO communication interference suppression channel estimation MISO communication SIMO communication wireless channels radiofrequency interference expectation-maximisation algorithm optimisation approximation theory computational complexity parallel interference cancellation technique single-input multiple-output maximum likelihood technique ML cost optimisation function expectation maximisation algorithms multiple input multiple output orthogonal frequency division multiplexing system semiblind MIMO-OFDM channel taps estimation EM algorithm approximation-simplification approach parallel multiple-input single-output OFDM systems linear complexity single-input multiple-output OFDM system

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Impact of channel sounder phase noise on directional channel estimation by space-alternating generalised expectation maximisation

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IET MICROWAVES ANTENNAS & PROPAGATION 2007年第3期1卷 803-808页

作者： Taparugssanagorn, A. Alatossava, M. Holappa, V.-M. Ylitalo, J. Univ Oulu Ctr Wireless Commun FIN-90014 Oulu Finland

Multi-input multi-output (MIMO) radio channel sounders commonly employ time-division multiplexing (TDM) technique to switch between transmit and receive antennas. The TDM method offers single-input single-output sounders the MIMO capability in a cost-effective manner. However, the TDM approach leads to errors in MIMO channel measurements because of phase noise in local oscillators. Phase noise varies in the short term during the switching sequence for a MIMO channel sample. The high-resolution space-alternating generalised expectation maximisation (SAGE) algorithm has been efficiently applied for the estimation of the channel parameters. The impact of phase noise on the directional channel parameters, which are estimated by the SAGE algorithm, has been studied. Although the previous studies have investigated long-term random walk noise, we concentrate on the short-term noise which affects consecutive MIMO channel matrix measurements. It has been shown that errors in direction of arrival and direction of departure estimation because of the short-term phase noise can be effectively decreased by time domain filtering of the impulse responses prior to the SAGE processing.

关键词： space-alternating generalised expectation maximisation Communication switching multiinput multioutput radio channel expectation-maximisation algorithm Linear algebra (numerical analysis) directional channel estimation Other topics in statistics time-division multiplexing wireless channels channel estimation local oscillator direction-of-departure estimation random walk noise time-domain analysis impulse response Filtering methods in signal processing TDM technique random noise single-input single-output sounder channel sounder phase noise high-resolution SAGE algorithm antenna arrays Antenna arrays transmitting antenna Interpolation and function approximation (numerical analysis) transient response Radio links and equipment time division multiplexing phase noise Communication channel equalisation and identification time domain filtering filtering theory direction-of-arrival estimation MIMO channel matrix measurement MIMO communication receive antenna

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Space-alternating generalised expectation-maximisation-based H-infinity channel estimator for multiple-input multiple-output-orthogonal frequency division multiplexing systems

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IET COMMUNICATIONS 2011年第14期5卷 2068-2074页

作者： Xu, P. Wang, J. K. Qi, F. Song, X. Northeastern Univ Sch Informat Sci & Engn Qinhuangdao Peoples R China Katholieke Univ Leuven Dept Elect Engn Louvain Belgium

This study presents a low-complexity and robust H-infinity channel estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The H-infinity estimator, which has never been devoted to MIMO-OFDM systems, could be implemented by applying a multiple-order auto-regression (AR) model. However, it may increase the design complexity of receivers and lead to poor real-time property when this model is used for MIMO-OFDM systems, which makes the authors abandon AR model. In order to reduce the number of matrices manipulations because of the each received OFDM symbols from different transmit antennas, the iterative space-alternating generalised expectation-maximisation (SAGE) algorithm is adopted. Furthermore, to deal with the effect of non-Gaussian noise (NGN) channels, because of various natural or man-made impulsive sources, an equivalent signal model (ESM) is introduced to alleviate the effect of this issue and enhance the robust of SAGE-based H-infinity estimator. Simulation results show that H-infinity estimator has almost the same bit error rate performance as optimal maximum a posteriori estimator. The performance gain afforded by using ESM can be substantial when compared with using the traditional signal model, which dramatically enhance the robustness of SAGE-based H-infinity estimator against NGN channels.

关键词： MIMO-OFDM system robust H-infinity channel estimator man-made impulsive sources multiple-order autoregression model expectation-maximisation algorithm multiple-input multiple-output-orthogonal frequency division multiplexing matrices manipulation autoregressive processes transmit antennas Signal processing and detection nonGaussian noise channel Modulation and coding methods Mobile radio systems receivers OFDM modulation channel estimation NGN channels iterative space-alternating generalised expectation-maximisation algorithm radio receivers design complexity next generation networks low-complexity H-infinity channel estimator OFDM symbol equivalent signal model Communication channel equalisation and identification MIMO communication Other topics in statistics

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Tracking of group-housed pigs using multi-ellipsoid expectation maximisation

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IET COMPUTER VISION 2018年第2期12卷 121-128页

作者： Mittek, Mateusz Psota, Eric T. Carlson, Jay D. Perez, Lance C. Schmidt, Ty Mote, Benny Univ Nebraska Lincoln Elect & Comp Engn Lincoln NE 68588 USA Univ Nebraska Lincoln Anim Sci Lincoln NE USA

Maintaining the health and well-being of animals is critical to the efficiency and profitability of livestock operations. However, it can be difficult to monitor the health of animals in large group-housed settings without the assistance of technology. This study presents a system that uses depth images to continuously track individual pigs in a group-housed environment. It is an alternative to traditional manual observation used by both researchers and producers for the analysis of animal activities and behaviours. The tracking method used by the system exploits the consistent shape and fixed number of the targets in the environment by applying expectation maximisation as a policy for fitting an ellipsoid to each target. Results demonstrate that the system can maintain the correct positions and orientations of 15 group-housed pigs for an average of 19.7min between failure events.

关键词： expectation-maximisation algorithm farming tracking computer vision group-housed pigs tracking multiellipsoid expectation maximisation depth images group-housed environment animal activities animal behaviours tracking method failure events

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Joint registration and multi-target tracking based on labelled random finite set and expectation maximisation

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IET RADAR SONAR AND NAVIGATION 2018年第3期12卷 312-322页

作者： Li, Minzhe Jing, Zhongliang Pan, Han Dong, Peng Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China

A new analytical algorithm is developed to address the problem of joint sensor registration and multi-target tracking with varying target number and observation uncertainty based on the labelled random finite set (RFS) and expectation maximisation (EM). A new complete data log-likelihood function is derived with the measurement and state RFS variables, and the EM approach is employed to jointly estimate the sensor biases and target states. Moreover, a recursive implementation is provided to deal with the measurement accumulation, and the situation of the time-varying biases is handled. Since the estimates of the bias and state are analytically calculated, the performance of the proposed method is better than that of the traditional methods. The effectiveness and superiority of the proposed algorithm are verified using numerical simulations.

关键词： target tracking set theory expectation-maximisation algorithm recursive estimation joint sensor registration and multitarget tracking labelled random finite set expectation maximisation approach analytical algorithm observation uncertainty data log-likelihood function state RFS variables EM approach sensor biases estimation target state estimation measurement accumulation time-varying biases numerical simulations

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Iterative (turbo) expectation-maximisation-based time and frequency synchronisation for multiple-input multiple-output-orthogonal frequency-division multiplexing systems

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IET COMMUNICATIONS 2008年第7期2卷 982-993页

作者： Saemi, A. Meghdadi, V. Cances, J. -P. Zahabi, R. Univ Limoges ENSIL Parc ESTER Technopole Xlim C2S2 ESTE CNRSUMR 6172 F-87065 Limoges France

An iterative expectation-maximisation time-frequency synchronisation algorithm joint with channel estimation for multiple-input multiple-output-orthogonal frequency-division multiplexing (OFDM) systems in frequency-selective fading channels is addressed. The receiver iterates between detection and estimation stages. For each iteration, the expectation of ODFM symbols is calculated first by using a posteriori probabilities provided by maximum a posteriori (MAP) decoder, and second, a proposed metric is maximised to obtain both frequency offset and symbol timing. The channel can be identified by means of these estimates. This algorithm can work in transmission mode, and thus, can be used to estimate the residual errors or track the change of the parameters. The performance of the proposed synchronisation approach, in terms of bit-error rate andmean-square error, is shown.

关键词： maximum likelihood decoding maximum a posteriori decoder frequency-division multiplexing systems MIMO communication fading channels time-frequency synchronisation multiple-input multiple-output-orthogonal systems OFDM modulation OFDM mean square error methods iterative expectation-maximisation frequency-selective fading channels error statistics bit-error rate mean-square error channel coding expectation-maximisation algorithm

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expectation-maximisation approach to blind source separation of nonlinear convolutive mixture

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IET SIGNAL PROCESSING 2007年第2期1卷 51-65页

作者： Zhang, J. Woo, W. L. Dlay, S. S. Newcastle Univ Sch Elect Elect & Comp Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

A novel learning algorithm for blind source separation of post-nonlinear convolutive mixtures is proposed. The proposed mixture model characterises both convolutive mixture and post-nonlinear distortions of the sources. A novel iterative technique based on a maximum likelihood approach is developed where the expectation-maximisation (EM) algorithm is generalised to estimate the parameters in the proposed model. In the E-step of the proposed framework, sufficient statistics of the posterior distribution of the source signals are estimated while the model parameters are optimised through these statistics in the M-step. The post-nonlinear distortions, however, render these statistics difficult to express in a closed form, and hence, this causes intractability in the M-step. A computationally efficient algorithm is further proposed to facilitate the E-step tractable and the self-updated multilayer perceptron is developed in the M-step to estimate the nonlinearity. The theoretical foundation of the proposed solution has been rigorously developed and discussed in detail. Both simulations and real-time speech signals have been used to verify the success and efficacy of the proposed algorithm. Remarkable improvement has been obtained when compared with the existing algorithm.

关键词： multilayer perceptrons maximum likelihood approach expectation-maximisation algorithm speech signals expectation-maximisation approach computationally efficient algorithm learning (artificial intelligence) posterior distribution nonlinear convolutive mixture Signal processing and detection Neural computing techniques iterative technique self-updated multilayer perceptron nonlinear distortion learning algorithm EM algorithm Signal processing theory post-nonlinear distortions E-step Interpolation and function approximation (numerical analysis) post-nonlinear convolutive mixtures M-step statistics blind source separation source signals Other topics in statistics

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expectation maximisation-based approach to recovering multiple sparse signals with common sparsity pattern

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ELECTRONICS LETTERS 2018年第3期54卷 140-142页

作者： Sun, Jingjing Cheng, Xiantao UESTC Natl Key Lab Sci & Technol Commun Chengdu Sichuan Peoples R China

The problem of simultaneously recovering multiple sparse signals bearing a common sparsity pattern is addressed. Specifically, a common Gaussian prior to all the sparse signals under consideration is assigned. This can make that the signals share the same sparsity pattern. Then, an expectation maximisation (EM)-based approach to learn the priori parameters from measurements, thereby leading to the recovery of sparse signals is adopted. Simulations verify that the proposed EM approach outperforms the state-of-the-art counterparts.

关键词： expectation-maximisation algorithm Gaussian processes signal processing expectation maximisation-based approach multiple sparse signals common sparsity pattern common Gaussian prior EM approach

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