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Adaptive one-bit quantisation via approximate message passing with nearest neighbour sparsity pattern learning

IET SIGNAL PROCESSING 2018年第5期12卷 629-635页

作者： Cao, Hangting Zhu, Jiang Xu, Zhiwei Zhejiang Univ Ocean Coll Key Lab Ocean Observat Imaging Testbed Zhejiang P 1 Zheda Rd Zhoushan 316021 Peoples R China

In this study, the problem of recovering structured sparse signals with a priori distribution whose structure patterns are unknown is studied from one-bit adaptive (AD) quantised measurements. A generalised approximate message passing (GAMP) algorithm is utilised, and an expectation maximisation (EM) method is embedded in the algorithm to iteratively estimate the unknown a priori distribution. In addition, the nearest neighbour sparsity pattern learning (NNSPL) method is adopted to further improve the recovery performance of the structured sparse signals. Numerical results demonstrate the effectiveness of GAMP-EM-AD-NNSPL method with both simulated and real data.

关键词： quantisation (signal) message passing learning (artificial intelligence) compressed sensing expectation-maximisation algorithm iterative methods adaptive one-bit quantisation approximate message passing nearest neighbour sparsity pattern learning structured sparse signal recovery a priori distribution generalised approximate message passing expectation maximisation method iterative estimation GAMP-EM-AD-NNSPL method

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Switching LDS detection for GNSS-based train integrity monitoring system

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IET INTELLIGENT TRANSPORT SYSTEMS 2017年第5期11卷 299-307页

作者： Li, Sihui Cai, Baigen Wei Shangguan Schnieder, Eckehard Toro, Federico Grasso Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Tech Univ Carolo Wilhelmina Braunschweig Inst Traff Safety & Automat Engn Braunschweig Germany

Train integrity whilst in service establishes the foundation for railway safety. This study investigates train integrity detection which reliably deduces whether the train consists remain intact. A switching linear dynamic system (SLDS) based train integrity detection method is proposed for Global Navigation Satellite System (GNSS) based train integrity Monitoring System (TIMS) using the relative distance, velocity and acceleration of the locomotive and the last van. There, expectation maximisation (EM) algorithm estimates the parameters of SLDS model while the Gaussian Sum Filter infers train integrity state. After that, to cope with false detection and misdetection, a verification procedure and train parting time estimation are designed. The approach is evaluated with both field trials and simulated data. Results show that the false alarm rate and misdetection rate of SLDS-based integrity detection approach are 0 and 0.09% respectively, which proves better than the estimated train length based detection model and Hidden Markov Model (HMM).

关键词： satellite navigation railway safety expectation-maximisation algorithm hidden Markov models GNSS-based train integrity monitoring system railway safety switching linear dynamic system train integrity detection method global navigation satellite system TIMS relative distance expectation maximisation algorithm EM algorithm SLDS model Gaussian sum filter verification procedure train parting time estimation false alarm rate misdetection rate train length based detection model hidden Markov model HMM

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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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Joint estimation and identification for stochastic systems with unknown inputs

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IET CONTROL THEORY AND APPLICATIONS 2013年第10期7卷 1377-1386页

作者： Lan, Hua Liang, Yan Yang, Feng Wang, Zengfu Pan, Quan Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China

Motivated by tracking a manoeuvring target in electronic counter environments, the authors present the problem of joint estimation and identification of a class of discrete-time stochastic systems with unknown inputs in both the plant and sensors. Based on the expectation-maximum criterion, the joint optimisation scheme of state estimation, parameter identification and iteration terminate decision were derived. A numerical example of tracking a manoeuvring target accompanied range gate pull-off is utilised to verify the proposed scheme.

关键词： discrete time systems expectation-maximisation algorithm optimisation parameter estimation state estimation stochastic systems target tracking stochastic system identification manoeuvring target tracking electronic counter environments stochastic system estimation discrete-time stochastic systems unknown inputs expectation-maximum criterion joint optimisation scheme state estimation parameter identification iteration terminate decision

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Point target probabilistic multiple hypothesis tracking

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IET RADAR SONAR AND NAVIGATION 2011年第6期5卷 632-637页

作者： Song, T. L. Musicki, D. Lee, H. H. Wang, X. Hanyang Univ Dept Elect Syst Seoul South Korea Hanyang Univ Guidance & Control Lab Seoul South Korea Univ Melbourne Melbourne Syst Lab Melbourne Vic 3010 Australia

Probabilistic Multiple Hypothesis Tracking (PMHT) is an algorithm for multi-target tracking in clutter with computational requirements, which are linear in the number of targets and the number of measurements. In order to achieve this, the PMHT removes the point target constraint, and uses the expectation maximisation procedure to optimise both data association probabilities and the target trajectory state estimates. However, PMHT is known to have high track-loss percentage in comparison with Probabilistic Data Association, at least in point target tracking. The authors propose a new PMHT-like algorithm to solve some problems of PMHT. In this study they revert the point target constraint. The authors call the new algorithm Point target PMHT. Simulation results show the efficiency of this method.

关键词： state estimation point target probabilistic multiple hypothesis tracking expectation maximisation procedure target trajectory state estimates probabilistic data association expectation-maximisation algorithm Other topics in statistics target tracking Signal processing and detection multitarget tracking

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Skew Gaussian mixture models for speaker recognition

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IET SIGNAL PROCESSING 2014年第8期8卷 860-867页

作者： Matza, Avi Bistritz, Yuval Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel

Gaussian mixture models (GMMs) are widely used in speech and speaker recognition. This study explores the idea that a mixture of skew Gaussians might capture better feature vectors that tend to have skew empirical distributions. It begins with deriving an expectation maximisation (EM) algorithm to train a mixture of two-piece skew Gaussians that turns out to be not much more complicated than the usual EM algorithm used to train symmetric GMMs. Next, the algorithm is used to compare skew and symmetric GMMs in some simple speaker recognition experiments that use Mel frequency cepstral coefficients (MFCC) and line spectral frequencies (LSF) as the feature vectors. MFCC are one of the most popular feature vectors in speech and speaker recognition applications. LSF were chosen because they exhibit significantly more skewed distribution than MFCC and because they are widely used [together with the related immittance spectral frequencies (ISF)] in speech transmission standards. In the reported experiments, models with skew Gaussians performed better than models with symmetric Gaussians and skew GMMs with LSF compared favourably with both skew symmetric and symmetric GMMs that used MFCC.

关键词： Gaussian processes mixture models speaker recognition vectors expectation-maximisation algorithm speaker recognition GMM speech recognition skew empirical distribution expectation maximisation algorithm EM algorithm two-piece skew Gaussian mixture model Mel frequency cepstral coefflcient MFCC line spectral frequency LSF immittance spectral frequency ISF speech transmission standard feature vectors

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Multi-Target/Multi-Sensor Tracking using Only Range and Doppler Measurements

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2009年第2期45卷 593-611页

作者： Deming, Ross Schindler, John Perlovsky, Leonid USAF Res Lab Sensors Directorate RYHE Hanscom AFB MA 01731 USA

A new approach is described for combining range and Doppler data from multiple radar platforms to perform multi-target detection and tracking. In particular, azimuthal measurements are assumed to be either coarse or unavailable, so that multiple sensors are required to triangulate target tracks using range and Doppler measurements only. Increasing the number of sensors can cause data association by conventional means to become impractical due to combinatorial complexity, i.e., an exponential increase in the number of mappings between signatures and target models. When the azimuthal resolution is coarse, this problem will be exacerbated by the resulting overlap between signatures from multiple targets and clutter. In the new approach, the data association is performed probabilistically, using a variation of expectation-maximization (EM). Combinatorial complexity is avoided by performing an efficient optimization in the space of all target tracks and mappings between tracks and data. The full, multi-sensor, version of the algorithm is tested on simulated data. The results demonstrate that accurate tracks can be estimated by exploiting spatial diversity in the sensor locations. Also, as a proof-of-concept, a simplified, single-sensor range-only version of the algorithm is tested on experimental radar data acquired with a stretch radar receiver. These results are promising, and demonstrate robustness in the presence of nonhomogeneous clutter.

关键词： Doppler measurement expectation-maximisation algorithm optimisation radar receivers sensor fusion target tracking Doppler data Doppler measurements azimuthal measurements combinatorial complexity data association expectation maximization multiple radar platforms multisensor tracking multitarget tracking nonhomogeneous clutter optimization radar receiver spatial diversity expectation-maximisation algorithm Sensor fusion data association Laser-Doppler Velocimetry Radar receivers Target tracking multiple target tracking spatial diversity

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COMPARISON BETWEEN ML-EM AND WLS-CG algorithmS FOR SPECT IMAGE-RECONSTRUCTION

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 1991年第6期38卷 1766-1772页

作者： TSUI, BMW ZHAO, XD FREY, EC GULLBERG, GT UNIV UTAH DEPT RADIOL SALT LAKE CITY UT 84112 USA UNIV UTAH DEPT BIOMED ENGN SALT LAKE CITY UT 84112 USA

We have studied the properties of two iterative reconstruction algorithms, namely, the maximum likelihood with expectation maximization (ML-EM) and the weighted least squares with conjugate gradient (WLS-CG) algorithms, for use in compensation for attenuation and detector response in cardiac SPECT imaging. A realistic phantom, derived from a patient X-ray CT study to simulate 201T1 SPECT data, was used in the investigation. Both algorithms are effective in compensating for the nonuniform attenuation distribution in the thorax region and the spatially variant detector response function of the imaging system. At low iteration numbers, the addition of detector response compensation provides improvement in both spatial resolution and image noise when compared with attenuation compensation alone. However, at higher iteration numbers, there is a more rapid increase in image noise when detector response compensation is included, and the increase is more dramatic for the WLS-CG algorithm. In general, the convergence rate of the WLS-CG algorithm is about ten times that of the ML-EM algorithm. Also, the WLS-CG exhibits a faster increase in image noise at large iteration numbers than the ML-EM algorithm. This study is valuable in the search for useful and practical reconstruction methods for improved clinical cardiac SPECT imaging.

关键词： expectation-maximisation algorithm algorithms image reconstruction image noise single photon emission computed tomography sup compensated maximum likelihood

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Method for joint estimation for states and parameters concerning non-linear systems with time-correlated measurement noise

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IET CONTROL THEORY AND APPLICATIONS 2019年第5期13卷 721-731页

作者： Liu, Hongqiang Zhou, Zhongliang Yang, Haiyan Air Force Engn Univ Aeronaut & Astronaut Coll Xian Shaanxi Peoples R China Air Force Aviat Univ Aviat Combat & Serv Inst Changchun Jilin Peoples R China Air Force Engn Univ Air Traff Control & Nav Coll Xian Shaanxi Peoples R China

A dimensionality-reduction-augmented non-linear state-space representation has been proposed to reduce the optimisation space for maximum-likelihood estimation. Based on the above representation, an expectation-maximisation algorithm has been derived to realise joint estimation of states and parameters. During the expectation step, the system state was estimated via the use of a fifth-order cubature Kalman filter and Rauch-Tung-Striebel smoother based on the state-augmented method. During the maximisation step, unknown parameters within iterations were estimated using the Newton method. Subsequently, two joint-estimation methods - one containing all measurements and the other involving a sliding window - were developed to estimate the invariants and step parameters, respectively. An example concerning manoeuvring-target tracking has been discussed to demonstrate the performance of proposed algorithms.

关键词： Newton method expectation-maximisation algorithm smoothing methods maximum likelihood estimation linear systems Kalman filters target tracking

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EM-based SNR estimator for faster-than-Nyquist signalling system

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ELECTRONICS LETTERS 2015年第24期51卷 2051-2052页

作者： Liang, Xiaohu Liu, Aijun Pan, Xiaofei Wu, Gang PLA Univ Sci & Technol Inst Commun Engn Nanjing 210007 Jiangsu Peoples R China

Considering the intentionally introduced inter-symbol interference and coloured noise, proposed is a new signal-to-noise ratio (SNR) estimator based on the expectation-maximisation algorithm for the faster-than-Nyquist signalling system. The proposed SNR estimator can maximise the likelihood function by iteratively using the soft information from the channel decoder. Moreover, the new SNR estimator can be applied into high-order modulation. The performance of the proposed SNR estimator is verified and tested.

关键词： intersymbol interference expectation-maximisation algorithm channel coding decoding mobile radio telecommunication signalling electromagnetic based SNR estimator faster-than-Nyquist signalling system intersymbol interference coloured noise signal-to-noise ratio estimator expectation-maximisation algorithm maximum likelihood function channel decoder soft information

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