| 词条 | Draft:Ronald Mahler |
| 释义 |
| caption = | birth_name = Ronald Paxton Sheets Mahler[1] | birth_date = {{birth date and age|1948|8|15}} | birth_place = Great Falls, Montana, U.S. | alma_mater = University of Chicago {{small|(B.A. in Mathematics, 1970)}}; Brandeis University {{small|(PhD in Mathematics, 1974)}}; University of Minnesota {{small|(B.E.E. in Electrical Engineering, 1980)}} | known_for = PHD filter;[2] CPHD filter;[3] Multi-Bernoulli filter;[4] | awards =Lockheed Martin MS2 Author of the Year (2008, 2004) JDL-DFG Joseph Mignogna Data Fusion Award {{small|(2007)}} {{url|http://ieee-aess.org/membership/awards/harry-rowe-mimno-award|Harry Rowe Mimno Award}} (2005) {{url|http://ieee-aess.org/membership/awards/m-barry-carlton-award|M. Barry Carlton Award}} (2007) | field = Electrical Engineering; Mathematics; Target Tracking; Data Fusion | work_institution = University of Minnesota {{small|(1974-1979)}}; Lockheed Martin Corporation {{small|(1980-present)}}; {{url|https://randomsets.com/|Random Sets LLC}}
}} Ronald P.S. Mahler (born August 15, 1948) is an American mathematician and electrical engineer. He is most noted for his invention and development of the Probability Hypothesis Density (PHD) filter, a mathematical algorithm for maintaining probabilistic information on multi-object systems from surveillance data such as radar plots. It is the second most cited article in IEEE Transactions on Aerospace and Electronic Systems. Professional biographyMahler joined University of Minnesota as an Assistant Professor in Mathematics. In 1980 Mahler joined Sperry Corporation (after 1986 a part of Lockheed Martin) working on magnetometry. He is a member of data fusion information group (DFIG). The PHD filter{{Main|The PHD filter}}state estimationIs a sequential Bayesian density approximation algorithm. This algorithm has been described by the author as a "multitarget statistical analog of the constant-gain Kalman filter (e.g., an α-β-γ filter)". In 2003 Mahler published an article that has become of the most cited articles in Aerospace community of IEEE. It deliver one of the most important result, the Probability Hypothesis Density filter. This filter is a moment approximation. It propagates the first moment of intensity measure of a Poisson spatial point process. In 2007 an updated version of this filter was proposed by Mahler. Some alternative derivations has been presented from the perspective of point processes and from physical space. In his original article Mahler has not provided an implementation that has later proposed by Vo et. al., including the Sequential Monte-Carlo[5] and Gaussian Mixture[6] implementations. The PHD filter has been used for various advanced sensing techniques, including simultaneous localization and mapping (SLAM)[7][8] and sensor management.[9][10][11] Estimated object states are not directly available from a running PHD filter. In order to obtain useful information, various clustering techniques, including k-means or Expectation Maximization (EM) have been used. However, it is a common opinion that these clustering techniques are heuristics.[12] Finite set statistics (FISST) and random finite sets{{Main|FISST}}Mahler has described his approach as "a radical new paradigm for multitarget detection and tracking".[13] Finite set statistics (FISST)[14][15][16] However, we adopt the Finite Set Statistic (FISST) notion of density since to by-passes measure theoretic constructs. [86]. His random finite set approach has been described as one of the most popular approaches to multi-target tracking, along with PMHT and JPDAF algorithms[17]. Awards and honorsMahler received a numerous awards. The IEEE Aerospace and Electronic Systems Society presented the Harry Rowe Mimno Award which is established to "recognize and foster excellence in clear communication of technical material of widespread interest to AESS members". And was awarded to Mahler in recognition for the tutorial "Statistics 101 for Multi-Sensor Multi-Target Data Fusion"[14] published in AES Systems Magazine in January 2004. {{Quote|text="This tutorial summarizes the motivations, concepts, techniques, and applications of finite-set statistics (FISST), a system-level, “top-down” direct generalization of ordinary single-sensor, single-target engineering statistics to the multisensor, multitarget realm. FISST provides powerful new conceptual and computational methods for dealing with multisensor, multitarget, and multi-evidence data fusion problems. The paper begins with a broad-brush overview of the basic concepts of FISST. It describes how conventional single-sensor, single-target formal Bayesian modeling is carefully extended to general data fusion problems. We examine a simple example: joint detection and tracking of a possibly non-existent maneuvering target in heavy clutter. The tutorial concludes with a commentary on certain criticisms of FISST." }} In recognition of the Cardinalized Probability Density Hypothesis (CPHD) filter by 2007 M. Barry Carlton Award of IEEE AESS, it has been stated: {{Quote|text=“Dr. Mahler pioneered the random set framework for tracking and data fusion. For nearly two decades he has worked tirelessly in developing this rather esoteric branch of mathematics into practical engineering tools... The idea of propagating the intensity function and the cardinality distribution of the multi-target state is innovative in an engineering context and bold in its large departure from the traditional methodologies... Overall, this is a phenomenal amount of work and the contribution is ahead of its time.”[18] |author=Professor {{url|http://ba-ngu.vo-au.com/|Ba-Ngu Vo}}, University of Melbourne (as of 2007) }}{{Quote |text=“The concept of jointly propagating a cardinality distribution with an intensity function of a point process is a ground-breaking and fundamentally important result, not just in the target tracking community, but more broadly in many research areas including spatial statistics, Bayesian estimation and signal processing.”[18] |author=Professor {{url|http://home.eps.hw.ac.uk/~dec1/|Daniel E. Clark}}, Heriot-Watt University (as of 2007) }} Selected publications
| title = Mathematics of Data Fusion| year = 1997| publisher = Springer| location = Netherlands| isbn = 978-0-7923-4674-6}}
| title = Random Sets: Theory and Applications | year = 1997| publisher = Springer-Verlag| location = New York| isbn = 978-0-387-98345-5}}
See also
References1. ^Technical Program Committee (The 9th International Conference on Information Fusion) 2. ^{{cite journal | last1 = Mahler | first1 = Ronald P.S. | title = Multitarget Bayes filtering via first-order multitarget moments | journal = IEEE Transactions on Aerospace and Electronic systems | volume = 39 | issue = 4 | pages = 1152 - 1178 | year = 2003 | doi = 10.1109/TAES.2003.1261119 | publisher = IEEE}} 3. ^{{cite journal | last1 = Mahler | first1 = Ronald P.S. | title = PHD filters of higher order in target number | journal = IEEE Transactions on Aerospace and Electronic systems | volume = 43 | issue = 4 | pages = 1523 - 1543 | year = 2007 | doi = 10.1109/TAES.2007.4441756 | publisher = IEEE}} 4. ^{{cite book | last=Mahler | first=Ronald | title=Statistical multisource-multitarget information fusion | publisher=Artech House | publication-place=Boston | year=2007 | isbn=978-1-59693-092-6 | oclc=318540336 | page=}} 5. ^{{cite journal | last1 = Vo | first1 = B.-N. | last2 = Singh | first2 = S. | last3 = Doucet | first3 = A. | title = Sequential Monte Carlo methods for multitarget filtering with random finite sets | journal = IEEE Transactions on Aerospace and Electronic systems | volume = 41 | issue = 4 | pages = 1224 - 1245 | year = 2005 | doi = 10.1109/TAES.2003.1261119 |url=http://ba-ngu.vo-au.com/vo/VSD_SMCRFS_AES05.pdf | publisher = IEEE}} 6. ^{{cite journal | last=Vo | first=B.-N. | last2=Ma | first2=W.-K. | title=The Gaussian Mixture Probability Hypothesis Density Filter | journal=IEEE Transactions on Signal Processing | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=54 | issue=11 | year=2006 | issn=1053-587X | doi=10.1109/tsp.2006.881190 | url=http://ba-ngu.vo-au.com/vo/VM_GMPHD_SP06.pdf | pages=4091–4104}} 7. ^{{cite journal | last=Adams | first=Martin | last2=Vo | first2=Ba-Ngu | last3=Mahler | first3=Ronald | last4=Mullane | first4=John | title=SLAM Gets a PHD: New Concepts in Map Estimation | journal=IEEE Robotics & Automation Magazine | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=21 | issue=2 | year=2014 | issn=1070-9932 | doi=10.1109/mra.2014.2304111 |url=http://repositorio.uchile.cl/bitstream/handle/2250/126808/SLAM-Gets-a-PHD-New-Concepts-in-Map-Estimation.pdf | pages=26–37}} 8. ^{{cite journal | last=Lee | first=Chee Sing | last2=Nagappa | first2=Sharad | last3=Palomeras | first3=Narcis | last4=Clark | first4=Daniel E. | last5=Salvi | first5=Joaquim | title=SLAM with SC-PHD Filters: An Underwater Vehicle Application | journal=IEEE Robotics & Automation Magazine | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=21 | issue=2 | year=2014 | issn=1070-9932 | doi=10.1109/mra.2014.2310132 | url=http://eia.udg.es/~qsalvi/papers/2014-RAM.pdf | pages=38–45}} 9. ^{{cite journal | last=Ristic | first=Branko | last2=Vo | first2=Ba-Ngu | last3=Clark | first3=Daniel | title=A Note on the Reward Function for PHD Filters with Sensor Control | journal=IEEE Transactions on Aerospace and Electronic Systems | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=47 | issue=2 | year=2011 | issn=0018-9251 | doi=10.1109/taes.2011.5751278 | url=http://ba-ngu.vo-au.com/vo/RVC_Renyi.pdf | pages=1521–1529}} 10. ^{{cite thesis |last= Delande|first= Emmanuel D.|date= |title= Multi-sensor PHD filtering with application to sensor management | year = 2012| type= |chapter= |publisher= École centrale de Lille |docket= |oclc= |url= https://tel.archives-ouvertes.fr/tel-00688304/document|access-date= 2019-02-03}} 11. ^{{cite conference | last=Andrecki | first=Marian | last2=Delande | first2=Emmanuel D. | last3=Houssineau | first3=Jeremie | last4=Clark | first4=Daniel E. | title=Sensor Management with Regional Statistics for the PHD Filter | publisher=IEEE | year=2015 | isbn=978-1-4799-7444-3 | doi=10.1109/sspd.2015.7288522 | url=https://udrc.eng.ed.ac.uk/sites/udrc.eng.ed.ac.uk/files/publications/Andrecki-EtAl_SensorManagement.pdf | page=}} 12. ^{{cite conference | last=Baum | first=Marcus | last2=Willett | first2=Peter | last3=Hanebeck | first3=Uwe D. | title=MMOSPA-based track extraction in the PHD filter - a justification for k-means clustering | publisher=IEEE | year=2014 | isbn=978-1-4673-6090-6 | doi=10.1109/cdc.2014.7039662 |url=http://isas.uka.de/Publikationen/CDC14_Baum.pdf | page=}} 13. ^ 14. ^1 {{cite journal | last=Mahler | first=R.P.S. | title="Statistics 101" for multisensor, multitarget data fusion | journal=IEEE Aerospace and Electronic Systems Magazine | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=19 | issue=1 | year=2004 | issn=0885-8985 | doi=10.1109/maes.2004.1263231 | url=http://ieee-aess.org/sites/ieee-aess.org/files/documents/2004%20January%20Systems%20Magazine.pdf | pages=53–64}} 15. ^{{cite journal | last=Mahler | first=Ronald | title=“Statistics 102” for Multisource-Multitarget Detection and Tracking | journal=IEEE Journal of Selected Topics in Signal Processing | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=7 | issue=3 | year=2013 | issn=1932-4553 | doi=10.1109/jstsp.2013.2253084 | url=http://www.atl.lmco.com/papers/2134.pdf | pages=376–389}} 16. ^{{cite journal | last=Mahler | first=Ronald | title=“Statistics 103” for Multitarget Tracking | journal=Sensors | publisher=MDPI AG | volume=19 | issue=1 | date=2019-01-08 | issn=1424-8220 | doi=10.3390/s19010202 | url=https://www.mdpi.com/1424-8220/19/1/202/pdf | page=202}} 17. ^ 18. ^1 {{cite journal | last=Willett | first=P.K. | title=From the Editor-in-Chief | journal=IEEE Transactions on Aerospace and Electronic Systems | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=46 | issue=1 | year=2010 | issn=0018-9251 | doi=10.1109/taes.2010.5417142 |url=https://ieeexplore.ieee.org/ielx5/7/5417139/05417142.pdf| pages=1–1}} |
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