Statistical Multisource-Multitarget Information Fusion

By Ronald P. S. Mahler. 2007

2,227 citations recorded by the source corpus.

Subfields: Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

Model-based classification and novelty detection for point pattern data (2016, background): “A random finite set (RFS) X of X is a random variable taking values in F(X ) [19], [11], [12], [20], [13].”
PHD filters for nonstandard targets, I: Extended targets (2009, background): “In [12] I discussed several, including these: • a model for fields of scanning bearing-only sensors (pp.”
Tracking of extended objects and group targets using random matrices — a new approach (2008, background): “In particular, the probability hypothesis density (PHD) filter [3]–[5] has been suggested as one possibility to track an unknown number of possibly closely spaced targets while simultaneously solving the association problem in an approximate yet very efficient manner, especially in its Gaussian…”
A Metric for Performance Evaluation of Multi-Target Tracking Algorithms (2011, methodology): “The multi-target tracking algorithm used in demonstration is based on the Bernoulli/JoTT filter [13] and includes the linear multitarget (LM) data association [14] (details omitted, being irrelevant for the demonstration of the metric).”
Consensus CPHD Filter for Distributed Multitarget Tracking (2013, background): “The total number of nodes in the network will be denoted by , the cardinality of .”
Adaptive Target Birth Intensity for PHD and CPHD Filters (2012, methodology): “x k = xs+(z[1]+3⁄4rv (n) 1 )sin(z[2]+3⁄4bv (n) 2 ) y k = ys+(z[1]+3⁄4rv (n) 1 )cos(z[2]+3⁄4bv (n) 2 ) where n= 1, : : : ,N k , z[1] and z[2] are the measured range and bearing and v 1 ,v (n) 2 »N (¢;0,1).”
A phd Filter for Tracking Multiple Extended Targets Using Random Matrices (2012, methodology): “The PHD filter propagates the target set’s PHD in time [11], [12], and represents an approximation to the optimal multitarget Bayes filter.”
Joint Detection and Estimation of Multiple Objects From Image Observations (2010, background): “B.-N. Vo and B.-T. Vo are with the School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, WA 6009, Australia (e-mail: ba-ngu.vo@uwa.edu.au; ba-tuong.vo@uwa.edu.au).”
Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations (2010, background): “To handle such complications in a more complicated multitarget multisensor environment, the joint probabilistic data association (JPDA) filter [7], [12], the multihypothesis tracking (MHT) filter [7], [12] and the probability hypothesis density (PHD) filter [34], [42], [43] are often used.”
Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models (2018, background): “Fusing and integrating information from multiple models is a trend because no perfect model exists for a certain problem [34], [35].”
Joint Learning of Set Cardinality and State Distribution (2017, background): “…tools for dealing with random finite sets, based on the notion of integration and density that is consistent with the point process theory (Mahler 2007).1 For example, similar to the definition of a PDF for a random variable, the PDF of an RFS must sum to unity over all possible…”
Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, methodology): “RFS-based methods [5] have been developed in order to conduct statistical inference in problems in which the variables of interest and/or the observations form finite sets.”
Model-Based Multiple Instance Learning (2017, methodology): “In the generalization of the maximum aposteriori (MAP) estimator to point patterns [33], Mahler circumvented such inconsistency by replacing |X|!U |X| with c|X| , where c is an arbitrary constant.”
Multisensor data fusion: A review of the state-of-the-art (2013, methodology): “We also describe the new emerging field of fusion using random sets, which could be used to develop a unified framework for treatment of data imperfections [39].”
On performance evaluation of multi-object filters (2008, methodology): “test the new metric are the Gaussian mixture MeMBer filter [1] and the Gaussian mixture PHD filter [13].”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, methodology): “FastSLAM is an efficient implementation of particle filtering (Montemerlo, Thrun, Koller, & Wegbreit, 2003) (Grisetti et al., 2007) feature view Pros: effective for loop closure, performs full and online SLAM, logarithmic complexity in number of features, no need for parametrization Cons: quality of”
The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations (2009, methodology): “Explicit expressions for and can be derived from (6) and (7) using FISST, see for example [3], [4], [19].”