scholarly journals Efficient Approximation of the Labeled Multi-Bernoulli Filter for Online Multitarget Tracking

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Ping Wang ◽  
Liang Ma ◽  
Kai Xue
Keyword(s):  
Measurement Model ◽  
Gaussian Mixture ◽  
Approximation Technique ◽  
Gaussian Component ◽  
Multitarget Tracking ◽  
Target Number ◽  
Track Maintenance ◽  
Online Tracking ◽  
Tracking Time ◽  
Efficient Approximation

Online tracking time-varying number of targets is a challenging issue due to measurement noise, target birth or death, and association uncertainty, especially when target number is large. In this paper, we propose an efficient approximation of the Labeled Multi-Bernoulli (LMB) filter to perform online multitarget state estimation and track maintenance efficiently. On the basis of the original LMB filer, we propose a target posterior approximation technique to use a weighted single Gaussian component representing each individual target. Moreover, we present the Gaussian mixture implementation of the proposed efficient approximation of the LMB filter under linear, Gaussian assumptions on the target dynamic model and measurement model. Numerical results verify that our proposed efficient approximation of the LMB filer achieves accurate tracking performance and runs several times faster than the original LMB filer.

Generating realistic Arabic handwriting dataset

2019 ◽  
Vol 8 (4) ◽  
pp. 460
Author(s):  
Mahmoud I. Abdalla ◽  
Mohsen A. Rashwan ◽  
Mohamed A. Elserafy
Keyword(s):  
Word Recognition ◽  
Word List ◽  
Holistic Approach ◽  
Gaussian Mixture ◽  
Normalization Method ◽  
Gaussian Component ◽  
Efficient System ◽  
Mixture Regression ◽  
Word Generation ◽  
Input Text

During the previous year's holistic approach showing satisfactory results to solve ‎the ‎problem of Arabic handwriting word  recognition instead of word letters ‎‎segmentation.‎ ‎In this paper, we present an efficient system for ‎ generation realistic Arabic handwriting dataset from ASCII input ‎text. We carefully selected simple word list that contains most Arabic ‎letters normal and ligature connection cases. To improve the ‎performance of new letters reproduction we developed our ‎normalization method that adapt its clustering action according to ‎created Arabic letters families. We enhanced  Gaussian Mixture ‎Model process to learn letters template by detecting the ‎number and position of Gaussian component by implementing ‎Ramer-Douglas-Peucker‎ algorithm which improve the new letters ‎shapes reproduced by using and Gaussian Mixture Regression. ‎‎We learn the translation distance between word-part to achieve ‎real handwriting word generation shape.‎ Using combination of LSTM and CTC layer as a recognizer to validate the ‎efficiency of our approach in generating new realistic Arabic handwriting words inherit user handwriting style as shown by the experimental results.‎ 

Strategies to Enhance the Performance of Gaussian Mixture Model Fitting for Uncertainty Quantification by Conditioning to Production Data

2021 ◽  
Author(s):  
Guohua Gao ◽  
Jeroen Vink ◽  
Fredrik Saaf ◽  
Terence Wells
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
History Matching ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Gaussian Component ◽  
Large Set ◽  
Cpu Time ◽  
Fitting Method ◽  
New Strategies

Abstract When formulating history matching within the Bayesian framework, we may quantify the uncertainty of model parameters and production forecasts using conditional realizations sampled from the posterior probability density function (PDF). It is quite challenging to sample such a posterior PDF. Some methods e.g., Markov chain Monte Carlo (MCMC), are very expensive (e.g., MCMC) while others are cheaper but may generate biased samples. In this paper, we propose an unconstrained Gaussian Mixture Model (GMM) fitting method to approximate the posterior PDF and investigate new strategies to further enhance its performance. To reduce the CPU time of handling bound constraints, we reformulate the GMM fitting formulation such that an unconstrained optimization algorithm can be applied to find the optimal solution of unknown GMM parameters. To obtain a sufficiently accurate GMM approximation with the lowest number of Gaussian components, we generate random initial guesses, remove components with very small or very large mixture weights after each GMM fitting iteration and prevent their reappearance using a dedicated filter. To prevent overfitting, we only add a new Gaussian component if the quality of the GMM approximation on a (large) set of blind-test data sufficiently improves. The unconstrained GMM fitting method with the new strategies proposed in this paper is validated using nonlinear toy problems and then applied to a synthetic history matching example. It can construct a GMM approximation of the posterior PDF that is comparable to the MCMC method, and it is significantly more efficient than the constrained GMM fitting formulation, e.g., reducing the CPU time by a factor of 800 to 7300 for problems we tested, which makes it quite attractive for large scale history matching problems.

scholarly journals Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3193 ◽  
Author(s):  
Xueli Sheng ◽  
Yang Chen ◽  
Longxiang Guo ◽  
Jingwei Yin ◽  
Xiao Han
Keyword(s):  
Data Fusion ◽  
Detection Probability ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Statistical Characteristics ◽  
Multitarget Tracking ◽  
Fusion Algorithm ◽  
Acoustic Shadow ◽  
Multisensor Data Fusion ◽  
Lower Detection

Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.

scholarly journals Speaker Recognition System Based on Wavelet Features and Gaussian Mixture Models

2019 ◽  
Vol 9 (1) ◽  
pp. 5363-5367
Keyword(s):  
Mixture Models ◽  
Speaker Recognition ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Gaussian Component ◽  
Discrete Wavelet ◽  
Signal Features ◽  
Wavelet Features ◽  
Gmm Classifier

Identification of a person’s voice from the different voices is known as speaker recognition. The speech signals of individuals are selected by means of speaker recognition or identification. In this work, an efficient method for speaker recognition is made by using Discrete Wavelet Transform (DWT) features and Gaussian Mixture Models (GMM) for classification is presented. The input speech signal features are decomposed by DWT into subband coefficients. The DWT subband coefficient features are the input for the classification. Classification is made by GMM classifier at 4, 8, 16 and 32 Gaussian component levels. Results show a better accuracy of 96.18% speaker signals using DWT features and GMM classifier

scholarly journals On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking

2020 ◽  
Author(s):  
Tiancheng Li ◽  
Xiaoxu Wang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Literature Review ◽  
Information Fusion ◽  
Large Scale ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Computationally Efficient ◽  
Simple Arithmetic ◽  
Arithmetic Average ◽  
The Core ◽  
Comprehensive Literature Review

<div>Recently, the simple arithmetic averages (AA) fusion has demonstrated promising, even surprising, performance for multitarget information fusion. In this paper, we first analyze the conservativeness and Frechet mean properties of it, presenting new empirical analysis based on a comprehensive literature review. Then, we propose a target-wise fusion principle for tailoring the AA fusion to accommodate the multi-Bernoulli (MB) process, in which only significant Bernoulli components, each represented by an individual Gaussian mixture, are disseminated and fused in a Bernoulli-to-Bernoulli (B2B) manner. For internode communication, both the consensus and flooding schemes are investigated, respectively. At the core of the proposed fusion algorithms, Bernoulli components obtained at different sensors are associated via either clustering or pairwise assignment so that the MB fusion problem is decomposed to parallel B2B fusion subproblems, each resolved via exact Bernoulli-AA fusion. Two communicatively and computationally efficient cardinality consensus approaches are also presented which merely disseminate and fuse target existence probabilities among local MB filters. The accuracy and computing and communication cost of these four approaches are tested in two large scale scenarios with different sensor networks and target trajectories. </div>

scholarly journals Exact Closed-Form Multitarget Bayes Filters

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2818 ◽  
Author(s):  
Ronald Mahler
Keyword(s):  
Statistical Theory ◽  
Closed Form ◽  
Information Fusion ◽  
Review Paper ◽  
Measurement Model ◽  
Cutting Edge ◽  
Multitarget Tracking ◽  
Research Groups ◽  
Finite Set

The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion has inspired work by dozens of research groups in at least 20 nations; and FISST publications have been cited tens of thousands of times. This review paper addresses a recent and cutting-edge aspect of this research: exact closed-form—and, therefore, provably Bayes-optimal—approximations of the multitarget Bayes filter. The five proposed such filters—generalized labeled multi-Bernoulli (GLMB), labeled multi-Bernoulli mixture (LMBM), and three Poisson multi-Bernoulli mixture (PMBM) filter variants—are assessed in depth. This assessment includes a theoretically rigorous, but intuitive, statistical theory of “undetected targets”, and concrete formulas for the posterior undetected-target densities for the “standard” multitarget measurement model.

scholarly journals Tracking Multiple Marine Ships via Multiple Sensors with Unknown Backgrounds

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5025 ◽  
Author(s):  
Cong-Thanh Do ◽  
Tran Thien Dat Nguyen ◽  
Weifeng Liu
Keyword(s):  
State Of The Art ◽  
Numerical Study ◽  
Probability Of Detection ◽  
Multitarget Tracking ◽  
Time Varying ◽  
Multiple Sensors ◽  
Doppler Data ◽  
Online Tracking ◽  
Bernoulli Filter ◽  
Parallel Estimation

In multitarget tracking, knowledge of the backgrounds plays a crucial role in the accuracy of the tracker. Clutter and detection probability are the two essential background parameters which are usually assumed to be known constants although they are, in fact, unknown and time varying. Incorrect values of these parameters lead to a degraded or biased performance of the tracking algorithms. This paper proposes a method for online tracking multiple targets using multiple sensors which jointly adapts to the unknown clutter rate and the probability of detection. An effective filter is developed from parallel estimation of these parameters and then feeding them into the state-of-the-art generalized labeled multi-Bernoulli filter. Provided that the fluctuation of these unknown backgrounds is slowly-varying in comparison to the rate of measurement-update data, the validity of the proposed method is demonstrated via numerical study using multistatic Doppler data.

Using Response Times as Collateral Information About Latent Traits in Psychological Tests

Methodology ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 185-196
Author(s):  
Jochen Ranger ◽  
Anett Wolgast
Keyword(s):  
Logistic Model ◽  
Response Times ◽  
Measurement Model ◽  
Gaussian Mixture ◽  
Psychological Tests ◽  
Testing Time ◽  
Modeling Framework ◽  
Collateral Information ◽  
Latent Traits ◽  
Two Parameter

Abstract. In psychological tests, the time needed to respond to the items provides collateral information about the latent traits of the test takers. This, however, requires a measurement model that incorporates the response times in addition to the responses. Such a measurement model is usually based on a full specification of the response time distribution. In the present article, we suggest a novel modeling approach that requires fewer assumptions. In the approach, the responses are modeled with a unidimensional two-parameter logistic model. The single response times are summed to the scale-specific total testing time which is then related to the latent trait of the two-parameter logistic model via a smooth adaptive Gaussian mixture (SAGM) model. The approach can be justified against the background of the bivariate generalized linear item response theory modeling framework ( Molenaar, Tuerlinckx, & van der Maas, 2015a ). Its utility is investigated in two simulation studies and an empirical example.

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