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

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 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.

Recent advances in multisensor multitarget tracking using random finite set

2021 ◽  
Vol 22 (1) ◽  
pp. 5-24
Author(s):  
Kai Da ◽  
Tiancheng Li ◽  
Yongfeng Zhu ◽  
Hongqi Fan ◽  
Qiang Fu
Keyword(s):  
Multitarget Tracking ◽  
Random Finite Set ◽  
Recent Advances ◽  
Finite Set

scholarly journals Multitarget Tracking with Spatial Nonmaximum Suppressed Sensor Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Liang Ma ◽  
Kai Xue ◽  
Ping Wang
Keyword(s):  
Information Gain ◽  
Sensor Selection ◽  
Multitarget Tracking ◽  
Sensor Management ◽  
Information Quantity ◽  
Partially Observed ◽  
Markov Decision ◽  
Finite Set ◽  
Bernoulli Filter ◽  
Fusion Scheme

Multitarget tracking is one of the most important applications of sensor networks, yet it is an extremely challenging problem since multisensor multitarget tracking itself is nontrivial and the difficulty is further compounded by sensor management. Recently, random finite set based Bayesian framework has opened doors for multitarget tracking with sensor management, which is modelled in the framework of partially observed Markov decision process (POMDP). However, sensor management posed as a POMDP is in essence a combinatorial optimization problem which is NP-hard and computationally unacceptable. In this paper, we propose a novel sensor selection method for multitarget tracking. We first present the sequential multi-Bernoulli filter as a centralized multisensor fusion scheme for multitarget tracking. In order to perform sensor selection, we define the hypothesis information gain (HIG) of a sensor to measure its information quantity when the sensor is selected alone. Then, we propose spatial nonmaximum suppression approach to select sensors with respect to their locations and HIGs. Two distinguished implementations have been provided using the greedy spatial nonmaximum suppression. Simulation results verify the effectiveness of proposed sensor selection approach for multitarget tracking.

Non-repetitive sequences

1970 ◽  
Vol 68 (2) ◽  
pp. 267-274 ◽  
Author(s):  
P. A. B. Pleasants
Keyword(s):  
Repetitive Sequences ◽  
Finite Set ◽  
Infinite Sequences

This note is concerned with infinite sequences whose terms are chosen from a finite set of symbols. A segment of such a sequence is a set of one or more consecutive terms, and a repetition is a pair of finite segments that are adjacent and identical. A non-repetitive sequence is one that contains no repetitions.

The inverse problem of recovering the coefficients of a differential equations on a graph

2020 ◽  
Vol 28 (5) ◽  
pp. 727-738
Author(s):  
Victor Sadovnichii ◽  
Yaudat Talgatovich Sultanaev ◽  
Azamat Akhtyamov
Keyword(s):  
Inverse Problem ◽  
Inverse Problems ◽  
Differential Equations ◽  
Boundary Value Problem ◽  
Boundary Conditions ◽  
Finite Number ◽  
Characteristic Equation ◽  
Arbitrary Number ◽  
New Class ◽  
Finite Set

AbstractWe consider a new class of inverse problems on the recovery of the coefficients of differential equations from a finite set of eigenvalues of a boundary value problem with unseparated boundary conditions. A finite number of eigenvalues is possible only for problems in which the roots of the characteristic equation are multiple. The article describes solutions to such a problem for equations of the second, third, and fourth orders on a graph with three, four, and five edges. The inverse problem with an arbitrary number of edges is solved similarly.

A Parallel Retrodiction Algorithm for Large-Scale Multitarget Tracking

2020 ◽  
pp. 1-1
Author(s):  
Siu Lun Yeung ◽  
Sean Tager ◽  
Paul Wilson ◽  
Ratnasingham Tharmarasa ◽  
Wes Armour ◽  
...  
Keyword(s):  
Large Scale ◽  
Multitarget Tracking

scholarly journals A Novel Simplified Implementation of Finite-Set Model Predictive Control for Power Converters

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jose J. Silva ◽  
Jose R. Espinoza ◽  
Jaime A. Rohten ◽  
Esteban S. Pulido ◽  
Felipe A. Villarroel ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Power Converters ◽  
Finite Set
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