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 “Statistics 103” for Multitarget Tracking

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 202 ◽  
Author(s):  
Ronald Mahler
Keyword(s):  
Point Process ◽  
Information Fusion ◽  
Theoretical Foundation ◽  
Measure Theory ◽  
Multitarget Tracking ◽  
Mathematical Abstraction ◽  
Finite Set ◽  
Theoretic Point

The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and measure-theoretic point process (p.p.) theory. Recently, however, an allegedly more general theoretical foundation for multitarget tracking has been proposed. In it, the constituent components of FISST have been systematically replaced by mathematically more complicated concepts—and, especially, by the very measure theory and measure-theoretic p.p.’s that FISST eschews. It is shown that this proposed alternative is actually a mathematical paraphrase of part of FISST that does not correctly address the technical idiosyncrasies of the multitarget tracking application.

scholarly journals Finite Set Statistics Based Multitarget Tracking.

2017 ◽  
Author(s):  
Keith LeGrand ◽  
Raymond H. Byrne ◽  
Pavan Datta ◽  
David K. Melgaard ◽  
Johnathan Mulcahy-Stanislawczyk
Keyword(s):  
Multitarget Tracking ◽  
Finite Set

Random Vibrations of Constructions and their Damping

2009 ◽  
Vol 147-149 ◽  
pp. 345-349
Author(s):  
Danielius Gužas ◽  
A. Čiučelis
Keyword(s):  
Statistical Theory ◽  
Closed Form ◽  
Statistical Characteristic ◽  
Degrees Of Freedom ◽  
Viscous Friction ◽  
Spectral Approach ◽  
Mechanical Vibrations ◽  
Vibratory System ◽  
Long Wave ◽  
Wave Size

In the article, the equation of the equivalent movement of the oscillator with viscous friction is provided. The statistical specifications and the spectral approach for the density of intensity of the established vibrations are provided. Non-stationary vibrations in the closed form are investigated. The statistical characteristic for intensity of vibrations is obtained. The work presents a method of investigation of vibration processes of random mechanical vibrations in the equipment and apparatus. The vibratory system of a sufficiently small (as compared to vibrations of a long wave) size is described; the models with the limited number of degrees of freedom are applied. The statistical theory for discussing of vibrations at constructional damping is applied.

scholarly journals Distributed Multiagent Control Approach for Multitarget Tracking

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Liang Ma ◽  
Kai Xue ◽  
Ping Wang
Keyword(s):  
Information Fusion ◽  
Multitarget Tracking ◽  
Control Approach ◽  
Numerical Studies ◽  
Utility Optimization ◽  
Partially Observed ◽  
Markov Decision ◽  
Multiagent Control ◽  
Bernoulli Filter ◽  
Effectiveness And Efficiency

In multiagent systems, tracking multiple targets is challenging for two reasons: firstly, it is nontrivial to dynamically deploy networked agents of different types for utility optimization; secondly, information fusion for multitarget tracking is difficult in the presence of uncertainties, such as data association, noise, and clutter. In this paper, we present a novel control approach in distributed manner for multitarget tracking. The control problem is modelled as a partially observed Markov decision process, which is a NP-hard combinatorial optimization problem, by seeking all possible combinations of control commands. To solve this problem efficiently, we assume that the measurement of each agent is independent of other agents’ behavior and provide a suboptimal multiagent control solution by maximizing the local Rényi divergence. In addition, we also provide the SMC implementation of the sequential multi-Bernoulli filter so that each agent can utilize the measurements from neighbouring agents to perform information fusion for accurate multitarget tracking. Numerical studies validate the effectiveness and efficiency of our multiagent control approach for multitarget tracking.

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 Phenolic Content of Brown Algae (Pheophyceae) Species: Extraction, Identification, and Quantification

Biomolecules ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 244 ◽  
Author(s):  
Ivana Generalić Mekinić ◽  
Danijela Skroza ◽  
Vida Šimat ◽  
Imen Hamed ◽  
Martina Čagalj ◽  
...  
Keyword(s):  
Brown Algae ◽  
Marine Algae ◽  
Review Paper ◽  
Phenolic Content ◽  
Current Knowledge ◽  
Biological Activities ◽  
Research Groups ◽  
The World ◽  
Health Promoting ◽  
Insight Into

Over the last few decades, isolations and chemical characterizations of secondary metabolites with proved biological activities have been of interest for numerous research groups across the world. Phenolics, as one of the largest and most widely distributed group of phytochemicals, have gained special attention due to their pharmacological activity and array of health-promoting benefits. Reports on phenolic potentials of marine algae, especially brown algae (Pheophyceae) that are characterized by the presence of phlorotannins, are still scarce. The aim of this review paper is to provide an overview of current knowledge about phenolic potential of different brown algae species (74 species from 7 different orders). Studies on brown algae phenolics usually involve few species, thus the focus of this review is to provide information about the phenolic potential of reported algae species and to get an insight into some issues related to the applied extraction procedures and determination/quantification methods to facilitate the comparison of results from different studies. The information provided through this review should be useful for the design and interpretation of studies investigating the brown algae as a source of valuable phytochemicals.

scholarly journals Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 569
Author(s):  
Peng Wang ◽  
Ge Li ◽  
Yong Peng ◽  
Rusheng Ju
Keyword(s):  
Parameter Estimation ◽  
Sequential Monte Carlo ◽  
Stochastic Systems ◽  
Measurement Model ◽  
Unknown Parameters ◽  
Bayesian Parameter Estimation ◽  
Probability Hypothesis Density ◽  
Estimation Algorithms ◽  
Random Finite Set ◽  
Finite Set

Parameter estimation is one of the key technologies for system identification. The Bayesian parameter estimation algorithms are very important for identifying stochastic systems. In this paper, a random finite set based algorithm is proposed to overcome the disadvantages of the existing Bayesian parameter estimation algorithms. It can estimate the unknown parameters of the stochastic system which consists of a varying number of constituent elements by using the measurements disturbed by false detections, missed detections and noises. The models used for parameter estimation are constructed by using random finite set. Based on the proposed system model and measurement model, the key principles and formula derivation of the proposed algorithm are detailed. Then, the implementation of the algorithm is presented by using sequential Monte Carlo based Probability Hypothesis Density (PHD) filter and simulated tempering based importance sampling. Finally, the experiments of systematic errors estimation of multiple sensors are provided to prove the main advantages of the proposed algorithm. The sensitivity analysis is carried out to further study the mechanism of the algorithm. The experimental results verify the superiority of the proposed algorithm.

scholarly journals Production and Characterization of Molecular Dications: Experimental and Theoretical Efforts

Molecules ◽  
2020 ◽  
Vol 25 (18) ◽  
pp. 4157
Author(s):  
Stefano Falcinelli ◽  
Marzio Rosi
Keyword(s):  
Review Paper ◽  
Molecular Level ◽  
Energy Content ◽  
Plasma Chemistry ◽  
High Energy ◽  
Upper Atmosphere ◽  
Translational Energy ◽  
Research Groups ◽  
Doubly Charged

Molecular dications are doubly charged cations of importance in flames, plasma chemistry and physics and in the chemistry of the upper atmosphere of Planets. Furthermore, they are exotic species able to store a considerable amount of energy at a molecular level. This high energy content of several eV can be easily released as translational energy of the two fragment monocations generated by their Coulomb explosion. For such a reason, they were proposed as a new kind of alternative propellant. The present topic review paper reports on an overview of the main contributions made by the authors’ research groups in the generation and characterization of simple molecular dications during the last 40 years of coupling experimental and theoretical efforts.

scholarly journals Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Guohua Liu ◽  
Juan Guan ◽  
Haiying Liu ◽  
Chenlin Wang
Keyword(s):  
Information Fusion ◽  
Navigation System ◽  
Measurement Model ◽  
Lidar Measurement ◽  
Relative Measurement ◽  
Robot System ◽  
Single Node ◽  
Advantages And Disadvantages ◽  
Navigation Algorithms ◽  
Time Update

Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.

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