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

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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Robust Estimator for Lens-based Hybrid MIMO with Low-Resolution Sampling 20

Robust Estimator for Lens-based Hybrid MIMO with Low-Resolut...

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20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Vlachos, Evangelos Thompson, John Abbasi, Muhammad Ali Babar Fusco, Vincent F. Matthaiou, Michail Univ Edinburgh Inst Digital Commun Edinburgh Midlothian Scotland Queens Univ Belfast Inst Elect Commun & Informat Technol Belfast Antrim North Ireland

ISBN: (纸本)9781538665282

It is well known that large antenna arrays with beamforming capabilities are required to compensate for the high path-loss at millimeter-wave (mmWave) frequencies. Recently, a practical two-stage Rotman lens beamformer has demonstrated increased antenna gain with reduced implementation complexity, since the conventional beam selection network was omitted. In this work, we adopt this system and we investigate its performance in terms of symbol estimation when analog-to-digital converters (ADCs) with low-resolution sampling being employed. Although this design is characterized by low-complexity and low-cost, the analog beamformer and the ADCs introduce several impairments to the received signal. To mitigate these effects, we have developed a robust maximum a posteriori (MAP) estimator based on the expectation-Maximization (EM) iterative algorithm. The proposed algorithm outperforms the conventional EM approach, exhibiting small mean-square-error in the medium to high signal-to-noise ratio regimes.

关键词： analogue-digital conversion array signal processing expectation-maximisation algorithm iterative methods mean square error methods millimetre wave antenna arrays MIMO communication signal sampling millimetre wave antenna arrays Mean square error expectation-maximisation algorithm Analog-digital conversion MIMO communication signal sampling array signal processing iterative methods antenna gain Antenna arrays Maximum a posteriori estimation

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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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Maximum Likelihood Estimation from Interval-Valued Data. Application to Fuzzy Clustering 13th

Maximum Likelihood Estimation from Interval-Valued Data. App...

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13th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS)

作者： Hamdan, Hani Univ Paris Saclay Univ Paris Sud CNRS Lab Signaux & SystCent Supelec Paris France

ISBN: (纸本)9783030041649

Interval-valued data are used in many applications where they represent data imprecision, measurement inaccuracy, or measurand variability. As a result of the increasing use of such data in data mining, many data analysis methods have been extended to interval data this last decade. The expectation-Maximization (EM) algorithm has been widely used for maximum likelihood estimation of parameters in statistical models, where the model depends on unobserved latent variables. In our keynote talk, we will present the EM algorithm to interval-valued data. In this contribution, we provide an original likelihood expression for interval data. Then, we propose an original method to introduce the imprecision and the variability of data into the mathematical expectation of the EM algorithm. The maximization of the obtained expectation gives place to the EM algorithm for interval-valued data. We apply this EM algorithm to mixture model for maximum likelihood estimation of mixture model parameters from interval-valued data. A special attention is paid for the case of Gaussian mixture models. In order to show the usefulness of our approach, we apply it on real interval-valued data issued from a flaw diagnosis application using acoustic emission.

关键词： Application Fuzzy Clustering expectation-maximisation algorithm maximum likelihood estimation Fuzzy Cluster Analysis

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Parameter estimation of G0 distribution based on improved recursive expectation-maximisation method for clutter modelling

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 6759-6762页

作者： Lu, Jiaxin Sun, Yuze Zhuo, Bangsheng Yang, Xiaopeng Beijing Inst Technol Sch Informat & Elect Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Modelling and simulation of clutter are important in radar signal processing, the G0 distribution is generally adopted to simulate the ground clutter in radar echoes. In order to improve the modelling accuracy of clutter modelling, the actual data should be used for model parameter estimation. However, in some special situations, the actual data sample is very small. Existing methods cannot estimate the parameters of G0 distribution efficiently. To solve this problem, an improved recursive expectation-maximisation (EM) method is proposed to estimate the parameters of clutter in this article. This method combines the expectation step and maximisation step in one equation. Through recursive method and simplification of the positive definite matrix, this proposed method can obtain maximum likelihood estimation more efficiently than the conventional EM method and recursive EM method. Simulation results show that the performance of the proposed method is better than that of the conventional methods for a small data sample.

关键词： recursive estimation radar signal processing expectation-maximisation algorithm radar clutter maximum likelihood estimation parameter estimation improved recursive expectation–maximisation method clutter modelling radar signal processing ground clutter radar echoes modelling accuracy model parameter estimation actual data sample expectation step maximisation step recursive method maximum likelihood estimation

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Off-grid DOA estimation for passive radar

Off-grid DOA estimation for passive radar

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IET International Radar Conference (IET IRC 2020)

作者： J. Wen J. Yi X. Wan Wuhan University School of Electronic Information Wuhan People's Republic of China

To solve the problem that the true target direction of arrival (DOA) is not on the discrete grid points, we adopt a new off-grid sparse strategy to estimate the target DOA of passive radar. This strategy is the root sparse Bayesian learning method, which utilizes a coarse grid and treats the sampling position in the coarse grid as an adaptive parameter. Then, the coarse mesh is iteratively refined using the expectation maximization algorithm, and the estimated angle is the root of a certain polynomial. Simulation results show that the method has higher estimation accuracy and lower computational complexity in DOA estimation of passive radar. In addition, this method can not only estimate the DOA of multi-target signals in a single snapshot, but also provide new ideas for DOA estimation of coherent signals.

关键词： Bayes methods learning (artificial intelligence) iterative methods passive radar direction-of-arrival estimation expectation-maximisation algorithm computational complexity radar signal processing polynomials radar computing

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Cluster analysis of vehicle driving conditions based on K-means algorithm and EM algorithm

Cluster analysis of vehicle driving conditions based on K-me...

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CSAA/IET International Conference on Aircraft Utility Systems (AUS 2020)

作者： Q. Yan Y. Ma Y. Li School of Mechanical Engineering Beijing Institute of Technology Beijing China

The simulation and evaluation of land vehicle performance requires accurate representation of driving conditions. The ability to effectively interpret this condition to estimate the power demand is important for the energy management and plays crucial role in the battery utilization. After collecting driving status of vehicle, two algorithms (K-means Clustering and EM algorithm) are employed for clustering the working condition blocks. Finally, compare the clustering effects of the two algorithms, and draw the conclusion that EM algorithm is better.

关键词： vehicle dynamics energy management systems pattern clustering expectation-maximisation algorithm automobiles kinematics mechanical engineering computing

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Proposal of a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions - application: AEB system

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IET INTELLIGENT TRANSPORT SYSTEMS 2018年第9期12卷 1148-1156页

作者： Chelbi, Nacer Eddine Gingras, Denis Sauvageau, Claude Univ Sherbrooke LIV 2500 Boul Univ Sherbrooke PQ Canada PMG Technol Inc 100 Rue Landais Blainville PQ Canada

This study presents a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions. The approach identifies the worst-case scenarios for a given advanced driver assistance function, AEB system in this study, based on field operational tests (FOT) [safety pilot model deployment (SPMD), in this study]. The authors begin with a description of the studied AEB system and a synthesis of the most relevant tests scenarios. Then, they model the distribution of each test parameter retrieved from the SPMD database by applying two estimation methods (kernel method and expectation-maximisation algorithm). A comparison was made between the two methods to choose the best one. These distributions are then sampled using the proposed sampling strategy based on Metropolis-Hastings algorithm. Then, the idea is to take the samples of each parameter retrieved with this sampler, simulate them on a vehicular software simulator (PreScan) and to get their simulation results. For each test and in case of impact, a proportional score to the speed of impact reduction is attributed. Finally, a risk classification is done based on the scoring results which allows to recover high and very high-risk cases to build a set of worst-case scenarios.

关键词： Markov processes sampling methods driver information systems road vehicles expectation-maximisation algorithm Bayes methods Monte Carlo methods virtual evaluation approach preventive safety applications advanced driver assistance functions worst-case scenarios given advanced driver assistance function field operational tests [safety pilot model deployment studied AEB system relevant tests scenarios test parameter kernel method expectation-maximisation algorithm

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LPV model identification with an unknown scheduling variable in the presence of missing observations - a robust global approach

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IET CONTROL THEORY AND APPLICATIONS 2018年第10期12卷 1465-1473页

作者： Yang, Xianqiang Liu, Xin Han, Boxuan Harbin Inst Technol Res Inst Intelligent Control & Syst Sch Astronaut Harbin 150080 Heilongjiang Peoples R China

This study focuses on identifying the linear parameter varying (LPV) system with an unknown scheduling variable in the presence of missing measurements and the system output data contaminated with outliers. The parameter interpolated LPV autoregressive exogenous (ARX) model with an unknown scheduling variable is considered and the scheduling variable dynamic is described by a non-linear state-space model. The outliers treatment and unknown scheduling variable estimation with missing observations are both taken into consideration. The robust LPV model is established based on the Student's t-distribution in order to handle the outliers and the particle smoother is adopted to estimate the true scheduling variable from incomplete data set. The formulations of the proposed algorithm are finally derived in the expectation-maximisation algorithm scheme and the formulas to estimate the unknown parameters of LPV ARX model and scheduling variable dynamic model are derived simultaneously. A numerical example and a chemical process are used to present the efficacy of the proposed approach.

关键词： nonlinear control systems identification control system synthesis autoregressive processes linear systems state-space methods parameter estimation robust control expectation-maximisation algorithm time-varying systems interpolation LPV model identification missing observations (LPV) system system output data LPV autoregressive exogenous model scheduling variable dynamic state-space model unknown scheduling variable estimation robust LPV model unknown parameters LPV ARX model

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