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19 results on '"Sofia Suvorova"'

1. Bayesian Detection of a Sinusoidal Signal With Randomly Varying Frequency

Author

Changrong Liu, Sofia Suvorova, Rob J. Evans, Bill Moran, and Andrew Melatos

Subjects
Bayesian detector, Hidden Markov Model, Markov Chain Monte Carlo, posterior distribution, randomly varying frequency, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical communications and, recently, continuous gravitational wave astronomy. In this paper we describe a Markov Chain Monte Carlo based procedure to compute a specific detection posterior density. We demonstrate via simulation that our approach results in an up to 25 percent higher detection rate than Hidden Markov Model based solutions, which are generally considered to be the leading techniques for these problems.

Published
2022
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2. Gaussian mixture multitarget–multisensor Bernoulli tracker for multistatic sonobuoy fields

Author

Branko Ristic, Daniel Angley, Sofia Suvorova, Bill Moran, Fiona Fletcher, Han Gaetjens, and Sergey Simakov

Subjects
Gaussian mixture multitarget‐multisensor Bernoulli tracker, multistatic sonobuoy fields, emitter sonar sensors, receiver sonar sensors, underwater target detection, underwater target tracking, Telecommunication, TK5101-6720
Abstract

Sonobuoy fields, consisting of a large network of emitter and receiver sonar sensors on buoys, are increasingly being used for detection and tracking of underwater targets in a defined maritime area. This study presents a Gaussian mixture version of a multitarget–multisensor (MS) Bayesian‐type tracker developed specifically for multistatic sonobuoy fields. Its foundation is the optimal Bayesian MS filter for a single target in clutter. The multi target feature is incorporated using the linear‐multitarget paradigm, which is a fast and accurate approximation assuming the density of underwater targets is low. Reliable track initiation and false track discrimination for low signal‐to‐noise ratio targets are achieved using the amplitude feature of reported detections. The developed tracker is capable of processing measurements with different modalities, depending on the transmitted signal waveform. It is integrated and tested within a realistic multistatic sonar emulator developed by DST Group.

Published
2017
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3. Non‐myopic sensor scheduling for multistatic sonobuoy fields

Author

Daniel Angley, Branko Ristic, Sofia Suvorova, Bill Moran, Fiona Fletcher, Han Gaetjens, and Sergey Simakov

Subjects
nonmyopic sensor scheduling, multistatic sonobuoy fields, distributed emitter, receiver sonar sensors, stationary scheduling algorithm, continuous probability states algorithm, Telecommunication, TK5101-6720
Abstract

Sonobuoy fields, consisting of many distributed emitter and receiver sonar sensors on buoys, are used to seek and track underwater targets in a defined search area. The authors seek a scheduling protocol, selecting both the emitter and its waveform in each time interval that optimises tracking performance. This study describes a stationary scheduling algorithm for sonobuoy fields called the continuous probability states algorithm. The algorithm replaces a full partially observed Markov decision process by a computationally feasible Markov decision process by focusing on probability of target detection. This approach is shown to result in high‐quality tracks for multiple targets in a realistic simulation of a sonobuoy field.

Published
2017
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4. Golay Complementary Waveforms in Reed–Müller Sequences for Radar Detection of Nonzero Doppler Targets

Author

Jiahua Zhu, Xuezhi Wang, Xiaotao Huang, Sofia Suvorova, and Bill Moran

Subjects
Golay complementary waveforms, Reed–Müller sequences, sidelobes suppression, multiple nonzero Doppler targets, detection, Chemical technology, TP1-1185
Abstract

Golay complementary waveforms can, in theory, yield radar returns of high range resolution with essentially zero sidelobes. In practice, when deployed conventionally, while high signal-to-noise ratios can be achieved for static target detection, significant range sidelobes are generated by target returns of nonzero Doppler causing unreliable detection. We consider signal processing techniques using Golay complementary waveforms to improve radar detection performance in scenarios involving multiple nonzero Doppler targets. A signal processing procedure based on an existing, so called, Binomial Design algorithm that alters the transmission order of Golay complementary waveforms and weights the returns is proposed in an attempt to achieve an enhanced illumination performance. The procedure applies one of three proposed waveform transmission ordering algorithms, followed by a pointwise nonlinear processor combining the outputs of the Binomial Design algorithm and one of the ordering algorithms. The computational complexity of the Binomial Design algorithm and the three ordering algorithms are compared, and a statistical analysis of the performance of the pointwise nonlinear processing is given. Estimation of the areas in the Delay–Doppler map occupied by significant range sidelobes for given targets are also discussed. Numerical simulations for the comparison of the performances of the Binomial Design algorithm and the three ordering algorithms are presented for both fixed and randomized target locations. The simulation results demonstrate that the proposed signal processing procedure has a better detection performance in terms of lower sidelobes and higher Doppler resolution in the presence of multiple nonzero Doppler targets compared to existing methods.

Published
2018
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5. Hidden Markov model tracking of continuous gravitational waves from a neutron star with wandering spin. III. Rotational phase tracking

Author

P. Clearwater, Andrew Melatos, Sofia Suvorova, Rob Evans, L. Sun, and William Moran

Subjects
Physics, 010308 nuclear & particles physics, Gravitational wave, Block (permutation group theory), FOS: Physical sciences, Binary number, General Relativity and Quantum Cosmology (gr-qc), Astrophysics, 01 natural sciences, LIGO, General Relativity and Quantum Cosmology, Neutron star, Pulsar, 0103 physical sciences, 010303 astronomy & astrophysics, LIGO Scientific Collaboration, Spin-½, Mathematical physics
Abstract

A hidden Markov model (HMM) solved recursively by the Viterbi algorithm can be configured to search for persistent, quasimonochromatic gravitational radiation from an isolated or accreting neutron star, whose rotational frequency is unknown and wanders stochastically. Here an existing HMM analysis pipeline is generalized to track rotational phase and frequency simultaneously, by modeling the intra-step rotational evolution according to a phase-wrapped Ornstein-Uhlenbeck process, and by calculating the emission probability using a phase-sensitive version of the Bayesian matched filter known as the $\mathcal{B}$-statistic. The generalized algorithm tracks signals from isolated and binary sources with characteristic wave strain $h_0 \geq 1.3\times 10^{-26}$ in Gaussian noise with amplitude spectral density $4\times 10^{-24}\,{\rm Hz^{-1/2}}$, for a simulated observation composed of $N_T=37$ data segments, each $T_{\rm drift}=10\,{\rm days}$ long, the typical duration of a search for the low-mass X-ray binary (LMXB) Sco X$-$1 with the Laser Interferometer Gravitational Wave Observatory (LIGO). It is equally sensitive to isolated and binary sources and $\approx 1.5$ times more sensitive than the previous pipeline. Receiver operating characteristic curves and errors in the recovered parameters are presented for a range of practical $h_0$ and $N_T$ values. The generalized algorithm successfully detects every available synthetic signal in Stage I of the Sco X$-$1 Mock Data Challenge convened by the LIGO Scientific Collaboration, recovering the frequency and orbital semimajor axis with accuracies of better than $9.5\times 10^{-7}\,{\rm Hz}$ and $1.6\times 10^{-3}\,{\rm lt\,s}$ respectively. The Viterbi solver runs in $\approx 2\times 10^3$ CPU-hr for an isolated source and $\sim 10^5$ CPU-hr for a LMXB source in a typical, broadband ($0.5$-${\rm kHz}$) search., 33 pages, 16 figures, accepted for publication in Physical Review D

Published
2021

6. Erratum: Search for gravitational waves from Scorpius X-1 in the second Advanced LIGO observing run with an improved hidden Markov model [Phys. Rev. D 100, 122002 (2019)]

Author

S. J. Jadhav, Samuele Cortese, Charlie Hoy, V. Srivastava, M. Fays, Rebecca Fisher, J. Li, Chris A. Collins, T. Hinderer, A. Di Lieto, Rana X. Adhikari, Al Imran Malik, S. J. Chamberlin, M. Vasúth, Nikhil Sarin, Jonathan Cripe, M. Fyffe, Gabriela Gonzalez, K. R. Corley, F. Donovan, C. F. Da Silva Costa, V. Bossilkov, D. Lumaca, M. A. Okada, K. Mason, S. A. Bilgili, J. D. E. Creighton, Soichiro Morisaki, Jun Liu, E. C. Ferreira, Sanjeev Dhurandhar, D. B. Tanner, Sebastiano Bernuzzi, O. Halim, K. W. Tsang, W. Katzman, M. Yvert, R. M. S. Schofield, M. Nery, L. Naticchioni, R. M. Martin, David A. Nichols, M. Korobko, P. J. Veitch, W. A. Campbell, Ettore Majorana, Benjamin J. Owen, J. F. J. van den Brand, J. Junker, S. Sunil, M. Tacca, S. Hochheim, Martin Hendry, Cecilio García-Quirós, P. Ehrens, Paul M. Ricker, H. Khan, T. Prestegard, G. Cho, H. Fong, G. Greco, M. Bitossi, G. Bergmann, P. A. Altin, Antonios Kontos, S. Walsh, J. G. Bartlett, Márton Tápai, P.H. Nguyen, Junwei Cao, Y. Boetzel, A. Samajdar, K. Wette, F. Thies, John J. Oh, E. A. M. Muniz, Riccardo Ciolfi, Peter T. H. Pang, B. W. Schulte, L. Xiao, F. Ricci, C. Affeldt, R. Goetz, D. C. Vander-Hyde, C. Adams, A. A. Ciobanu, B. Mours, Mandar Patil, Abhirup Ghosh, Liam Cunningham, Archana Pai, F. Paoletti, Z. B. Etienne, Tristan Briant, P. Ruggi, P. Fulda, S. H. Huttner, A. Mullavey, T. J. N. Nelson, Peter Aufmuth, Gregorio Carullo, R. M. Blair, D. Sigg, T. Di Girolamo, S. W. Ballmer, D. M. Shoemaker, Hua Wang, L. Sun, S. R. P. Mohapatra, J. D. Lough, G. Giordano, K. Toland, D. J. Hofman, M. Punturo, Slawomir Gras, S. W. S. Ng, G. Moreno, S. Kumar, C. Buy, Callum Dale Booth, A. M. Sintes, Andrew Melatos, M. Lorenzini, A. Masserot, S. E. Barclay, J. L. Willis, J. McIver, J. H. Briggs, Mark Hannam, Ofek Birnholtz, S. Ghosh, László Á. Gergely, D. M. Wysocki, A. Macquet, B. Lantz, Daichi Tsuna, R. D. Kennedy, José A. Font, N. Singh, Eric Howell, S. Leavey, Chandra Kant Mishra, R. Bhandare, Piotr Jaranowski, K. Mogushi, N. Bode, D. George, Dániel Barta, R. K. Lanza, P. Dupej, Eric B. Flynn, B. Gateley, Aaron Buikema, R. W. L. Jones, C. C. Wipf, E. J. King, A. Strunk, N. Kijbunchoo, Sheelu Abraham, M. J. Cowart, Hai-Ping Cheng, A. Iess, L. K. Nuttall, S. T. Countryman, Surabhi Sachdev, Christopher J. Moore, S. Biscoveanu, Antoni Ramos-Buades, V. V. Frolov, S. Mitra, Xiaohui Fan, Richard O'Shaughnessy, B. Barr, A. D. Viets, D. Meacher, D. Tuyenbayev, Kevin Barkett, I. Dorrington, I. Di Palma, R. Kirchhoff, B. L. Pearlstone, Kendall Ackley, R. K. Nayak, H. Y. Chia, F. Magaña-Sandoval, A. Allocca, M. F. Carney, Ajay Kumar, Jörg Hennig, Charalampos Markakis, R. Pedurand, Benno Willke, A. Grado, T. Sadecki, W. Ren, S. Grunewald, R. Prasanna, R. L. Ward, P. J. Sutton, Alyssa Garcia, J. H. Romie, P. J. King, J. T. Whelan, K. H. Lai, S. G. Crowder, J. Feicht, Joey Shapiro Key, J. R. Sanders, M. Bawaj, M. M. Hanke, S. B. Anderson, M. R. Ganija, S. Ponrathnam, S. Karki, D. Verkindt, O. Puncken, M. B. Shaner, S. B. Coughlin, A. Neunzert, P.-F. Cohadon, M. Heurs, Reed Essick, Kazuhiro Agatsuma, F. Frasconi, T. Hardwick, D. Steinmeyer, F. Travasso, Michał Bejger, M. Standke, M. Croquette, A. Effler, J. Jiang, A. Perreca, G. Gemme, M. Drago, Guenakh Mitselmakher, Zhihui Du, A. Parida, S. S. Eikenberry, K. E. Ramirez, S. Barnum, Ryan Lynch, D. C. Williams, Sung-Po Chao, R. De Rosa, Kwan-Lok Li, D. Passuello, I. Nardecchia, A. Cirone, Hiroyuki Ohta, Robert J. McCarthy, Tim Dietrich, Jennifer Watchi, Sergio Gaudio, C. J. Richardson, J. Healy, V. Kringel, A. M. Holgado, R. Macas, G. Billingsley, S. Macfoy, N. Mukund, L. R. Cominsky, R. Gustafson, Linqing Wen, R. Flaminio, B. A. Boom, A. M. Sergeev, B. Pang, Z. Tornasi, P. Kumar, S. Kandhasamy, M. Fishbach, B. Sorazu, A. Kutynia, Paul T. Baker, A. K. Lenon, S. Mastrogiovanni, M. Pedraza, F. Cavalier, M. Fitz-Axen, Marcus E. Lower, J. S. Kissel, Hartmut Grote, S. Doravari, A. Singhal, Alena Ananyeva, Odylio D. Aguiar, I. W. Martin, Andrea Chincarini, A. K. Zadrożny, T. Z. Summerscales, Leo Tsukada, E. Z. Hamilton, S. A. Usman, Roman Schnabel, J. E. Brau, V. Boschi, M. van Beuzekom, R. Tso, Jonathan Richardson, L. Mereni, G. Losurdo, R. Metzdorff, M. C. Milovich-Goff, J. L. Wright, Patrick M. Koch, S. Antier, S. Banagiri, Alessandra Corsi, S. Raja, S. E. Strigin, H. S. Chen, S. Husa, S. Bose, J. Zweizig, V. Germain, J. Steinlechner, C. Cahillane, V. Gayathri, H. Fair, D. H. Shoemaker, P. Brockill, R. L. Savage, A. S. Bell, Hyun Lee, J. Calderón Bustillo, Edward K. Porter, C. Messenger, M. J. Yap, C. Palomba, S. Ascenzi, S. Privitera, M. S. Shahriar, Lee McCuller, T. Bulik, T. D. Abbott, G. Cagnoli, Devon S. Johnson, C. Horst, Alberto Vecchio, Geoffrey Mo, J. M. Fishner, M. Montani, H. J. Bulten, Geoffrey Lovelace, T. Mistry, G. Rutins, Graham Woan, M. H. Wimmer, M. Constancio, D. Rosińska, A. A. van Veggel, Richard Gray, Efim A. Khazanov, G. Ballardin, Moritz Mehmet, Kiran Prasai, Alessandra Buonanno, C.-J. Haster, Gabrielle Allen, J.-M. Isac, F. Marion, N. Leroy, Nancy Aggarwal, Zahoor Ali Khan, M. Phelps, S. Babak, Alexander C. Jenkins, Xie Chen, A. Markowitz, S. Vitale, G. Dálya, M. Laxen, Anchal Gupta, F. Y. Khalili, Robert L. Byer, Stuart Reid, F. J. Hayes, Jacob Scheuer, B. Bécsy, M. Kasprzack, Bala R. Iyer, S. S. Y. Chua, S. McCormick, C. Casentini, G. Wang, L. Matone, Vuk Mandic, Neil J. Cornish, M. J. Szczepańczyk, Denis Martynov, Marco Bazzan, F-L. Lin, D. J. McManus, C. Ingram, L. Pinard, B. R. Hall, Héctor Estellés, N. Letendre, Alec Mishkin, R. Cavalieri, I. Dave, C. Talbot, E. J. Daw, Maurizio Canepa, Antonio Marquina, Rosa Poggiani, V. Frey, A. K. Mehta, E. K. Wessel, S. Farinon, P. Raffai, T. L. Lam, Alessandro Longo, Richard J. Oram, François Bondu, F. Badaracco, Daniel E. Holz, B. J. J. Slagmolen, B. Patricelli, D. S. Wu, Margaret Millhouse, N. Sennett, Patrick Godwin, K. L. Dooley, Marta Colleoni, J. A. Giaime, L. Wallace, A. Królak, R. Frey, Joseph Smith, S. Brunett, D. Barker, A. Bisht, Maher Yazback, Y. K. Lecoeuche, G. M. Harry, K. Venkateswara, R. Cotesta, T. Etzel, J. B. Kanner, Robert Stone, Alessandro Nagar, J. C. Barayoga, A. Dmitriev, A. R. Williamson, Albrecht Rüdiger, Xing-Jiang Zhu, R. A. Eisenstein, Jishnu Suresh, Federico Ferrini, E. L. Merilh, N. Koper, Sebastian Steinlechner, A. Pasqualetti, M. Tse, D. M. Macleod, B. L. Swinkels, L. Glover, Vivishek Sudhir, V. Tiwari, Yann Bouffanais, Lijing Shao, G. Vajente, S. M. Gaebel, A. K. Srivastava, A. E. Pace, E. Schreiber, A. Basti, Ryan N. Lang, M. Rakhmanov, G. Pagano, G. D. Meadors, J. Eichholz, Li Ju, J.-P. Coulon, E. A. Chase, Z. Frei, Sofia Suvorova, T. Zelenova, D. H. Reitze, S. Caride, A. F. Brooks, P. J. Quinonez, J.-D. Fournier, Riccardo Bassiri, Magnus Manske, M. C. Araya, Srishti Tiwari, C. Lazzaro, Philip F. Hopkins, Leo Singer, Patricia Schmidt, V. Brisson, M. Obergaulinger, E. Maros, L. Trozzo, S. Katsanevas, I. Fiori, T. A. Ferreira, K. D. Giardina, J. K. Wofford, P. Leaci, P. Booker, K. W. Chung, S. Kaufer, Shawn Rosofsky, Haocun Yu, Archisman Ghosh, A. P. Lundgren, M. A. Page, Hongyu Shen, D. C. Coyne, K. Izumi, E. Cesarini, S. D. Linker, R. Robie, Mark A. Scheel, M. Fletcher, B. Hughey, Albert Lazzarini, S. G. Gaonkar, Jimin George, M. Sieniawska, K. Chakravarti, Fabio Marchesoni, P. Hello, Haixing Miao, Y. R. Chen, Samaya Nissanke, M. A. Bizouard, B. K. Berger, Ken K. Y. Ng, A. Hreibi, F. Clara, Alessandro Bertolini, Achamveedu Gopakumar, S. L. Danilishin, G. Bogaert, J. J. Bero, Manuela Rizzo, R. Taylor, Daniel A. Shaddock, Brittany Kamai, Rainer Weiss, G. Venugopalan, J. C. Driggers, D.B. DeBra, C. V. Kalaghatgi, J. Hanson, C. Whittle, Kyungmin Kim, B. O'Reilly, P. Gruning, K. Siellez, B. F. Schutz, N. V. Krishnendu, J. Derby, K. A. Thorne, M. Was, Isabel Cordero-Carrión, J. S. Lange, B. Irwin, M. C. Tringali, A. C. Green, Andrew Matas, J. Hanks, Fausto Acernese, C. Gray, G. H. Ogin, T. J. Shaffer, A. Ain, C. Fee, R. J. Trudeau, J. Birch, S. Bae, E. Coccia, C. S. Unnikrishnan, B. U. Gadre, W. M. Farr, B. C. Pant, J.-G. Ducoin, M. Eisenmann, R. C. Walet, Fangchen Feng, S. E. Gossan, M. Boer, Shubhanshu Tiwari, M. E. Zucker, C. Graef, B. A. Weaver, J. G. Rollins, Lindsay DeMarchi, L.-W. Wei, V. P. Mitrofanov, Lisa Barsotti, Anirban Dasgupta, L. Milano, W. Del Pozzo, Hang Yu, R. J. G. Jonker, H. Heitmann, M. A. Aloy, J. S. Areeda, K. A. Santiago, A. Gennai, Rahul Kumar, Harald P. Pfeiffer, Kris Ryan, E. A. Quintero, I. Belahcene, John Worden, A. S. Sengupta, H. W. Lee, P. Couvares, H. Vocca, Eugeniy E. Mikhailov, G. Vedovato, L. E. H. Datrier, D. B. Kozak, C. Messick, Imre Bartos, D. Stocks, M. Mantovani, Francesco Pannarale, C. Vorvick, L. Sammut, David Blair, S. Koley, Jan Harms, David E. McClelland, Lionel London, B. Idzkowski, V. Loriette, V. Skliris, G. Kang, A. Pal-Singh, A. Rocchi, B. B. Lane, C. Cocchieri, M. Khursheed, N. V. Keerthana, M.L. Walker, B. Goncharov, R. Birney, M. Kinley-Hanlon, G. Koekoek, F. Brighenti, D. Ugolini, P. Clearwater, E. J. Fauchon-Jones, M. Lormand, V. Quetschke, Martin M. Fejer, R. Bork, S. Ghonge, K. AultONeal, M. Hulko, G. D. O'Dea, E. Cuoco, Bruno Giacomazzo, V. Avendano, P. G. Murray, D. J. Stops, Fabien Kéfélian, J. S. H. Lee, T. Dal Canton, Felix Wellmann, J. Warner, R. Passaquieti, X. Liu, R. Mittleman, S. D'Antonio, Binlei Ding, D. Pascucci, C. C. Yancey, Nelson Christensen, K. Bossie, C. Rajan, M. Agathos, S. Meshkov, Seog Oh, A. Cumming, G. A. Prodi, S. M. Scott, L. van der Schaaf, S. A. Pai, E. Genin, J. Zhang, Jay D. Tasson, M. Landry, R. DeSalvo, L. Di Fiore, F. Baldaccini, L. F. Ortega, Felice Sorrentino, A. Chiummo, W. G. Anderson, J. H. Hough, P. W. F. Forsyth, S. R. Thondapu, J. R. Palamos, D. Moraru, O. V. Palashov, Sean T. McWilliams, F. Piergiovanni, G. Cella, F.L. Linde, N. Sanchis-Gual, N. Radulescu, N. A. Robertson, Giuseppe Intini, D. Moffa, Giacomo Ciani, K. Rao, J. J. McCann, B. P. Abbott, Michele Zanolin, D. W. Yeeles, D. Chatterjee, T. J. Slaven-Blair, S. ShyamSundar, C. Stachie, T. J. Massinger, T. Vo, E. K. Gustafson, V. Kalogera, C. De Rossi, C. Austin, J. Bosveld, M. Barsuglia, Y. Levin, R. J. E. Smith, Luca Gammaitoni, Sebastien Biscans, A. Trovato, M. Davier, Jordan Camp, F. Vetrano, S. Vass, J. K. Blackburn, E. Chassande-Mottin, Sylvain Marsat, Sheila Rowan, R. Coyne, Fabrice Matichard, G. D. Hammond, Javed Rana, S. E. Dwyer, R. M. Magee, David Keitel, L. Zhang, J. H. Klika, L. Rolland, Christopher P. L. Berry, K. S. Phukon, S. J. Cooper, A. Lartaux-Vollard, N. Man, Z. Márka, K. A. Strain, M. K. M. Bader, O. Chaibi, P. Thomas, M. Arène, M. Pürrer, M. Merzougui, Matthew Evans, T. D. Creighton, R. Inta, K. Haughian, L. Kuo, H. Yamamoto, E. Capocasa, M. Chan, A. Schönbeck, B. Rajbhandari, S. Fairhurst, J. Prasad, S. Frasca, S. Klimenko, Kai Staats, L. Conti, M. Chaturvedi, Carl Blair, M. Thomas, H. Vahlbruch, J. C. Mills, K. Riles, J. Y. Vinet, H. Middleton, D. Talukder, Rob Evans, I. W. Harry, Z. A. Warden, R. Gouaty, V. Raymond, M. Weinert, H. A. G. Gabbard, F. Fidecaro, G. M. Guidi, Steven Bloemen, N. A. Lockerbie, Michael Steinke, Arnold Miller, H. N. Isa, Carlos O. Lousto, S. Sitmukhambetov, C. North, Thomas Dent, P. Corban, P. Ajith, A. J.R. Weinstein, M. Muratore, M. C. Heintze, F. Carbognani, E. Katsavounidis, Xiuling Li, H. Radkins, A. Bozzi, M. Brinkmann, M. C. Díaz, D. Huet, David Coward, Mikhail L. Gorodetsky, R. Bonnand, Mariana Fazio, Olivier Minazzoli, M. Razzano, R. Colgan, F. Martelli, Karan Jani, R. Shink, Pradeep Kumar Sharma, L. Rei, Huang-Wei Pan, Gijs Nelemans, E. D. Hall, Turlough P. Downes, K. Holt, Chunglee Kim, V. Sandberg, Karsten Danzmann, Koh Ueno, A. W. Jones, R. De Pietri, Rocco Romano, John D. Scott, R. A. Mercer, S. Ossokine, Jinsook Kim, C. K. Cheong, S. Stevenson, F. Aubin, G. Stratta, Jesper Munch, D. Buskulic, M. De Laurentis, C-H. Lee, Andreas Freise, Paul D. Lasky, J. W. Westhouse, Matthew Pitkin, D. Cohen, M. Wade, M. Granata, Minchuan Zhou, K. S. Karvinen, S. Mukherjee, T. M. Evans, P. B. Covas, V. Kondrashov, Arunava Mukherjee, M. C. Edwards, J. M. Gonzalez Castro, Alexander Urban, B. D. Cheeseboro, G. Cerretani, P. J. Easter, Christian Pedersen, Y. Minenkov, P. Bacon, A. Viceré, T. Huynh-Dinh, M. Cabero, H. Cao, D. J. Vine, W. Z. Korth, J. C. Bayley, Marc Favata, L. E. Wade, V. J. Roma, H. Wittel, M. Deenadayalan, A. P. Spencer, D. D. Brown, F. Di Renzo, Torrey Cullen, Anthony A. Amato, N. van Bakel, G. Valdes, David Jones, Marco Cavaglia, K. Kawabe, S. G. Schwalbe, J. Z. Wang, R. G. Ormiston, S. M. Aston, Michael W. Coughlin, A. W. Heptonstall, Pablo Cerdá-Durán, Peter R. Saulson, V. Sequino, I. Khan, R. Abbott, Terry G. McRae, L. G. Prokhorov, J. R. Gair, Samuel Deléglise, A. Fernandez-Galiana, A. L. Stuver, J. P. Zendri, C. Pankow, P. M. Meyers, F. Cipriano, Paul J. Groot, M. Zevin, S. Appert, Y. Setyawati, A. Giazotto, P. Shawhan, I. Ferrante, F. J. Raab, Rico K. L. Lo, P. Charlton, Kejia Lee, S. C. Tait, Yongjung Kim, Peter Weßels, I. A. Bilenko, L. Haegel, John Veitch, J. Lehmann, N. Arnaud, Vijay Varma, M. MacInnis, S. Caudill, Marco Romanelli, P. Rapagnani, David J. Ottaway, A. Paoli, W. Parker, P. Popolizio, E. J. Sanchez, John A. Clark, Ben Farr, W. S. Kim, Christophe Collette, Ho-Gyu Lee, C. J. Perez, B. D. O'Brien, C. Bradaschia, P. Oppermann, M. Di Giovanni, Ik Siong Heng, S. Márka, V. Mangano, A. L. Miller, P. Astone, Mairi Sakellariadou, G. Pillant, S. H. R. Yuen, O. de Varona, Keith Rose, Tenglin Li, D. Estevez, V. Fafone, D. Mukherjee, A. Grant, A. Pele, Nergis Mavalvala, Jerome Degallaix, Sebastian Khan, B. C. Barish, O. J. Piccinni, E. Bonilla, Irina Kowalska, M. Tonelli, Koya Arai, R. Quitzow-James, N. N. Janthalur, B. Sassolas, Fabrizio Barone, J. Betzwieser, E. Goetz, Tarun Souradeep, S. Penn, V. B. Adya, G. L. Mansell, V. Dattilo, Edwin J. Son, Stefan Hild, Y. Ma, A. Brillet, M. Vardaro, F. Robinet, Antoine Heidmann, António Alves da Silva, Q. Chu, D. Töyrä, H. Overmier, H. Qi, S. V. Angelova, L. Magaña Zertuche, Lili Yang, M. Branchesi, Zefeng Zhou, D. Y. T. Pong, E. Massera, Edward Seidel, O. E. S. Sauter, J. Woehler, S. Chung, N. Mazumder, A. Avila-Alvarez, K. Gill, Liam Dunn, Walter Winkler, A. S. Markosyan, Harald Lück, M. Cho, N. A. Holland, G. Gaur, S. C. McGuire, J. F. Read, S. Di Pace, K. Haris, D. Sellers, N. Demos, I. Maksimovic, G. Hemming, J. Meidam, Jeff R. Powell, S. M. Koehlenbeck, Riccardo Sturani, D. Hoak, D. Sentenac, Patrick Brady, M. Haney, T. A. Callister, H. S. Cho, William Moran, H. K. Gulati, Rahul Kashyap, Alejandro Torres-Forné, J. Neilson, B. D. Lackey, P. Grassia, Z. Doctor, D. M. Wilken, O. A. Hannuksela, Andrea Taracchini, P. Puppo, S. J. Kapadia, E. A. Huerta, C. L. Romel, F. Garufi, Surendra Nadh Somala, Tyson Littenberg, D. Fiorucci, A. K. Prajapati, A. R. Wade, G. Kuehn, C. M. Mow-Lowry, Sergey P. Vyatchanin, L. Aiello, M. Gosselin, R. Green, E. E. Cowan, B. F. Whiting, J. Casanueva Diaz, P. Schale, C. Van Den Broeck, E. Sowell, T. Zhang, Thibaut Jacqmin, T. Regimbau, C. I. Torrie, M. Saleem, M. Oliver, Douglas Davis, Geraint Pratten, D. Bersanetti, G. Ashton, J. V. van Heijningen, Yu Wang, Francisco Hernandez Vivanco, Eric Thrane, C. Michel, T. D. Knowles, F. Ohme, F. Cleva, M. P. Ross, T. B. Edo, M. Pichot, Maximiliano Isi, E. M. Gretarsson, Ying Guo, D. Beniwal, Peter Fritschel, Wenxiao Wang, M. Masso-Reid, K. C. Cannon, S. Kwang, L. E. Sanchez, Enrico Calloni, G. Traylor, Thomas Corbitt, L. Salconi, C. A. Costa, Laura Cadonati, D. A. Steer, L. Kleybolte, Chad Hanna, A. Bramley, C. Kimball, J. Oberling, M. Pirello, J. Bidler, F. Nocera, C. Zhao, Milan Gupta, G. Mendell, Laboratoire des matériaux avancés (LMA), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Laboratoire de l'Accélérateur Linéaire (LAL), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Astrophysique Relativiste Théories Expériences Métrologie Instrumentation Signaux (ARTEMIS), Centre National de la Recherche Scientifique (CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institut des Fonctions Optiques pour les Technologies de l'informatiON (Institut FOTON), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire Kastler Brossel (LKB [Collège de France]), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Collège de France (CdF (institution)), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL), Institut des Hautes Etudes Scientifiques (IHES), IHES, LIGO Scientific, VIRGO, Abbott, B. P., Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, G., Allocca, A., Aloy, M. A., Altin, P. A., Amato, A., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arene, M., Arnaud, N., Ascenzi, S., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., Aultoneal, K., Austin, C., Avendano, V., Avila-Alvarez, A., Babak, S., Bacon, P., Badaracco, F., Bader, M. K. M., Bae, S., Baker, P. T., Baldaccini, F., Ballardin, G., Ballmer, S. W., Banagiri, S., Barayoga, J. C., Barclay, S. E., Barish, B. C., Barker, D., Barkett, K., Barnum, S., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Becsy, B., Bejger, M., Belahcene, I., Bell, A. S., Beniwal, D., Berger, B. K., Bergmann, G., Bernuzzi, S., Bero, J. J., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bidler, J., Bilenko, I. A., Bilgili, S. A., Billingsley, G., Birch, J., Birney, R., Birnholtz, O., Biscans, S., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bloemen, S., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Booth, C. D., Bork, R., Boschi, V., Bose, S., Bossie, K., Bossilkov, V., Bosveld, J., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. E., Briant, T., Briggs, J. H., Brighenti, F., Brillet, A., Brinkmann, M., Brisson, V., Brockill, P., Brooks, A. F., Brown, D. D., Brunett, S., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Buy, C., Byer, R. L., Cabero, M., Cadonati, L., Cagnoli, G., Cahillane, C., Calderon Bustillo, J., Callister, T. A., Calloni, E., Camp, J. B., Campbell, W. A., Canepa, M., Cannon, K. C., Cao, H., Cao, J., Capocasa, E., Carbognani, F., Caride, S., Carney, M. F., Carullo, G., Casanueva Diaz, J., Casentini, C., Caudill, S., Cavaglia, M., Cavalier, F., Cavalieri, R., Cella, G., Cerda-Duran, P., Cerretani, G., Cesarini, E., Chaibi, O., Chakravarti, K., Chamberlin, S. J., Chan, M., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chaturvedi, M., Cheeseboro, B. D., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Cheong, C. K., Chia, H. Y., Chincarini, A., Chiummo, A., Cho, G., Cho, H. S., Cho, M., Christensen, N., Chu, Q., Chua, S., Chung, K. W., Chung, S., Ciani, G., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirone, A., Clara, F., Clark, J. A., Clearwater, P., Cleva, F., Cocchieri, C., Coccia, E., Cohadon, P. -F., Cohen, D., Colgan, R., Colleoni, M., Collette, C. G., Collins, C., Cominsky, L. R., Constancio, M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrion, I., Corley, K. R., Cornish, N., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Couvares, P., Covas, P. B., Cowan, E. E., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Cripe, J., Croquette, M., Crowder, S. G., Cullen, T. J., Cumming, A., Cunningham, L., Cuoco, E., Dal Canton, T., Dalya, G., Danilishin, S. L., D'Antonio, S., Danzmann, K., Dasgupta, A., Da Silva Costa, C. F., Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davis, D., Daw, E. J., Debra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deleglise, S., Del Pozzo, W., Demarchi, L. M., Demos, N., Dent, T., De Pietri, R., Derby, J., De Rosa, R., De Rossi, C., Desalvo, R., De Varona, O., Dhurandhar, S., Diaz, M. C., Dietrich, T., Di Fiore, L., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Ding, B., Di Pace, S., Di Palma, I., Di Renzo, F., Dmitriev, A., Doctor, Z., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Downes, T. P., Drago, M., Driggers, J. C., Du, Z., Ducoin, J. -G., Dupej, P., Dwyer, S. E., Easter, P. J., Edo, T. B., Edwards, M. C., Effler, A., Ehrens, P., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Essick, R. C., Estelles, H., Estevez, D., Etienne, Z. B., Etzel, T., Evans, M., Evans, T. M., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Fee, C., Feicht, J., Fejer, M. M., Feng, F., Fernandez-Galiana, A., Ferrante, I., Ferreira, E. C., Ferreira, T. A., Ferrini, F., Fidecaro, F., Fiori, I., Fiorucci, D., Fishbach, M., Fisher, R. P., Fishner, J. M., Fitz-Axen, M., Flaminio, R., Fletcher, M., Flynn, E., Fong, H., Font, J. A., Forsyth, P. W. F., Fournier, J. -D., Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fritschel, P., Frolov, V. V., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gammaitoni, L., Ganija, M. R., Gaonkar, S. G., Garcia, A., Garcia-Quiros, C., Garufi, F., Gateley, B., Gaudio, S., Gaur, G., Gayathri, V., Gemme, G., Genin, E., Gennai, A., George, D., George, J., Gergely, L., Germain, V., Ghonge, S., Ghosh, A., Ghosh, S., Giacomazzo, B., Giaime, J. A., Giardina, K. D., Giazotto, A., Gill, K., Giordano, G., Glover, L., Godwin, P., Goetz, E., Goetz, R., Goncharov, B., Gonzalez, G., Gonzalez Castro, J. M., Gopakumar, A., Gorodetsky, M. L., Gossan, S. E., Gosselin, M., Gouaty, R., Grado, A., Graef, C., Granata, M., Grant, A., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, R., Gretarsson, E. M., Groot, P., Grote, H., Grunewald, S., Gruning, P., Guidi, G. M., Gulati, H. K., Guo, Y., Gupta, A., Gupta, M. K., Gustafson, E. 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S., Jones, A. W., Jones, D. I., Jones, R., Jonker, R. J. G., Ju, L., Junker, J., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Karki, S., Karvinen, K. S., Kashyap, R., Kasprzack, M., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaufer, S., Kawabe, K., Keerthana, N. V., Kefelian, F., Keitel, D., Kennedy, R., Key, J. S., Khalili, F. Y., Khan, H., Khan, I., Khan, S., Khan, Z., Khazanov, E. A., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, W., Kim, W. S., Kim, Y. -M., Kimball, C., King, E. J., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klika, J. H., Klimenko, S., Knowles, T. D., Koch, P., Koehlenbeck, S. M., Koekoek, G., Koley, S., Kondrashov, V., Kontos, A., Koper, N., Korobko, M., Korth, W. Z., Kowalska, I., Kozak, D. B., Kringel, V., Krishnendu, N., Krolak, A., Kuehn, G., Kumar, A., Kumar, P., Kumar, R., Kumar, S., Kuo, L., Kutynia, A., Kwang, S., Lackey, B. D., Lai, K. H., Lam, T. L., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lecoeuche, Y. K., Lee, C. H., Lee, H. K., Lee, H. M., Lee, H. W., Lee, J., Lee, K., Lehmann, J., Lenon, A., Leroy, N., Letendre, N., Levin, Y., Li, J., Li, K. J. L., Li, T. G. F., Li, X., Lin, F., Linde, F., Linker, S. D., Littenberg, T. B., Liu, J., Liu, X., Lo, R. K. L., Lockerbie, N. A., London, L. T., Longo, A., Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lough, J. D., Lousto, C. O., Lovelace, G., Lower, M. E., Luck, H., Lumaca, D., Lundgren, A. P., Lynch, R., Ma, Y., Macas, R., Macfoy, S., Macinnis, M., Macleod, D. M., Macquet, A., Magana-Sandoval, F., Magana Zertuche, L., Magee, R. M., Majorana, E., Maksimovic, I., Malik, A., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Marchesoni, F., Marion, F., Marka, S., Marka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martynov, D. V., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Matichard, F., Matone, L., Mavalvala, N., Mazumder, N., Mccann, J. J., Mccarthy, R., Mcclelland, D. E., Mccormick, S., Mcculler, L., Mcguire, S. C., Mciver, J., Mcmanus, D. J., Mcrae, T., Mcwilliams, S. T., Meacher, D., Meadors, G. D., Mehmet, M., Mehta, A. K., Meidam, J., Melatos, A., Mendell, G., Mercer, R. A., Mereni, L., Merilh, E. L., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. M., Miao, H., Michel, C., Middleton, H., Mikhailov, E. E., Milano, L., Miller, A. L., Miller, A., Millhouse, M., Mills, J. C., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Mishkin, A., Mishra, C., Mistry, T., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Mo, G., Moffa, D., Mogushi, K., Mohapatra, S. R. P., Montani, M., Moore, C. J., Moraru, D., Moreno, G., Morisaki, S., Mours, B., Mow-Lowry, C. M., Mukherjee, A., Mukherjee, D., Mukherjee, S., Mukund, N., Mullavey, A., Munch, J., Muniz, E. A., Muratore, M., Murray, P. G., Nagar, A., Nardecchia, I., Naticchioni, L., Nayak, R. K., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, K. Y., Ng, S., Nguyen, P., Nichols, D., Nissanke, S., Nocera, F., North, C., Nuttall, L. K., Obergaulinger, M., Oberling, J., O'Brien, B. D., O'Dea, G. D., Ogin, G. H., Oh, J. J., Oh, S. H., Ohme, F., Ohta, H., Okada, M. A., Oliver, M., Oppermann, P., Oram, R. J., O'Reilly, B., Ormiston, R. G., Ortega, L. F., O'Shaughnessy, R., Ossokine, S., Ottaway, D. J., Overmier, H., Owen, B. J., Pace, A. E., Pagano, G., Page, M. A., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Pal-Singh, A., Pan, H. -W., Pang, B., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Parida, A., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patil, M., Patricelli, B., Pearlstone, B. L., Pedersen, C., Pedraza, M., Pedurand, R., Pele, A., Penn, S., Perez, C. J., Perreca, A., Pfeiffer, H. P., Phelps, M., Phukon, K. S., Piccinni, O. J., Pichot, M., Piergiovanni, F., Pillant, G., Pinard, L., Pirello, M., Pitkin, M., Poggiani, R., Pong, D. Y. T., Ponrathnam, S., Popolizio, P., Porter, E. K., Powell, J., Prajapati, A. K., Prasad, J., Prasai, K., Prasanna, R., Pratten, G., Prestegard, T., Privitera, S., Prodi, G. A., Prokhorov, L. G., Puncken, O., Punturo, M., Puppo, P., Purrer, M., Qi, H., Quetschke, V., Quinonez, P. J., Quintero, E. A., Quitzow-James, R., Raab, F. J., Radkins, H., Radulescu, N., Raffai, P., Raja, S., Rajan, C., Rajbhandari, B., Rakhmanov, M., Ramirez, K. E., Ramos-Buades, A., Rana, J., Rao, K., Rapagnani, P., Raymond, V., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Ren, W., Ricci, F., Richardson, C. J., Richardson, J. W., Ricker, P. M., Riles, K., Rizzo, M., Robertson, N. A., Robie, R., Robinet, F., Rocchi, A., Rolland, L., Rollins, J. G., Roma, V. J., Romanelli, M., Romano, R., Romel, C. L., Romie, J. H., Rose, K., Rosinska, D., Rosofsky, S. G., Ross, M. P., Rowan, S., Rudiger, A., Ruggi, P., Rutins, G., Ryan, K., Sachdev, S., Sadecki, T., Sakellariadou, M., Salconi, L., Saleem, M., Samajdar, A., Sammut, L., Sanchez, E. J., Sanchez, L. E., Sanchis-Gual, N., Sandberg, V., Sanders, J. R., Santiago, K. A., Sarin, N., Sassolas, B., Saulson, P. R., Sauter, O., Savage, R. L., Schale, P., Scheel, M., Scheuer, J., Schmidt, P., Schnabel, R., Schofield, R. M. S., Schonbeck, A., Schreiber, E., Schulte, B. W., Schutz, B. F., Schwalbe, S. G., Scott, J., Scott, S. M., Seidel, E., Sellers, D., Sengupta, A. S., Sennett, N., Sentenac, D., Sequino, V., Sergeev, A., Setyawati, Y., Shaddock, D. A., Shaffer, T., Shahriar, M. S., Shaner, M. B., Shao, L., Sharma, P., Shawhan, P., Shen, H., Shink, R., Shoemaker, D. H., Shoemaker, D. M., Shyamsundar, S., Siellez, K., Sieniawska, M., Sigg, D., Silva, A. D., Singer, L. P., Singh, N., Singhal, A., Sintes, A. M., Sitmukhambetov, S., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smith, J. R., Smith, R. J. E., Somala, S., Son, E. J., Sorazu, B., Sorrentino, F., Souradeep, T., Sowell, E., Spencer, A. P., Srivastava, A. K., Srivastava, V., Staats, K., Stachie, C., Standke, M., Steer, D. A., Steinke, M., Steinlechner, J., Steinlechner, S., Steinmeyer, D., Stevenson, S. P., Stocks, D., Stone, R., Stops, D. J., Strain, K. A., Stratta, G., Strigin, S. E., Strunk, A., Sturani, R., Stuver, A. L., Sudhir, V., Summerscales, T. Z., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Swinkels, B. L., Szczepanczyk, M. J., Tacca, M., Tait, S. C., Talbot, C., Talukder, D., Tanner, D. B., Tapai, M., Taracchini, A., Tasson, J. D., Taylor, R., Thies, F., Thomas, M., Thomas, P., Thondapu, S. R., Thorne, K. A., Thrane, E., Tiwari, S., Tiwari, V., Toland, K., Tonelli, M., Tornasi, Z., Torres-Forne, A., Torrie, C. I., Toyra, D., Travasso, F., Traylor, G., Tringali, M. C., Trovato, A., Trozzo, L., Trudeau, R., Tsang, K. W., Tse, M., Tso, R., Tsukada, L., Tsuna, D., Tuyenbayev, D., Ueno, K., Ugolini, D., Unnikrishnan, C. S., Urban, A. L., Usman, S. A., Vahlbruch, H., Vajente, G., Valdes, G., Van Bakel, N., Van Beuzekom, M., Van Den Brand, J. F. J., Van Den Broeck, C., Vander-Hyde, D. C., Van Heijningen, J. V., Van Der Schaaf, L., Van Veggel, A. A., Vardaro, M., Varma, V., Vass, S., Vasuth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venkateswara, K., Venugopalan, G., Verkindt, D., Vetrano, F., Vicere, A., Viets, A. D., Vine, D. J., Vinet, J. -Y., Vitale, S., Vo, T., Vocca, H., Vorvick, C., Vyatchanin, S. P., Wade, A. R., Wade, L. E., Wade, M., Walet, R., Walker, M., Wallace, L., Walsh, S., Wang, G., Wang, H., Wang, J. Z., Wang, W. H., Wang, Y. F., Ward, R. L., Warden, Z. A., Warner, J., Was, M., Watchi, J., Weaver, B., Wei, L. -W., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Wessel, E. K., Wessels, P., Westhouse, J. W., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wilken, D. M., Williams, D., Williamson, A. R., Willis, J. L., Willke, B., Wimmer, M. H., Winkler, W., Wipf, C. C., Wittel, H., Woan, G., Woehler, J., Wofford, J. K., Worden, J., Wright, J. L., Wu, D. S., Wysocki, D. M., Xiao, L., Yamamoto, H., Yancey, C. C., Yang, L., Yap, M. J., Yazback, M., Yeeles, D. W., Yu, H., Yuen, S. H. R., Yvert, M., Zadrozny, A. K., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, T., Zhao, C., Zhou, M., Zhou, Z., Zhu, X. J., Zucker, M. E., Zweizig, J., Dunn, L. M., Suvorova, S., Evans, R. J., and Moran, W.

Subjects
Physics, data analysis method, erratum, 010308 nuclear & particles physics, Gravitational wave, Scorpius X-1, LIGO, gravitational radiation, gravitational radiation: direct detection, 01 natural sciences, gravitational radiation detector, Theoretical physics, cosmic rays, binary: spin, gravitational radiation: emission, 0103 physical sciences, [PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], Experiments in gravity, X-ray: binary, Hidden Markov model, numerical calculations, 010303 astronomy & astrophysics, cosmology
Abstract

International audience; We present results from a semicoherent search for continuous gravitational waves from the low-mass x-ray binary Scorpius X-1, using a hidden Markov model (HMM) to track spin wandering. This search improves on previous HMM-based searches of LIGO data by using an improved frequency domain matched filter, the J-statistic, and by analyzing data from Advanced LIGO’s second observing run. In the frequency range searched, from 60 to 650 Hz, we find no evidence of gravitational radiation. At 194.6 Hz, the most sensitive search frequency, we report an upper limit on gravitational wave strain (at 95% confidence) of h095%=3.47×10-25 when marginalizing over source inclination angle. This is the most sensitive search for Scorpius X-1, to date, that is specifically designed to be robust in the presence of spin wandering.

Published
2021
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7. Pulsar glitch detection with a hidden Markov model

Author

William Moran, Robin J. Evans, Andrew Melatos, Liam Dunn, and Sofia Suvorova

Subjects
High Energy Astrophysical Phenomena (astro-ph.HE), Physics, 010504 meteorology & atmospheric sciences, Markov chain, Astrophysics::High Energy Astrophysical Phenomena, Monte Carlo method, Astrophysics::Instrumentation and Methods for Astrophysics, FOS: Physical sciences, Astronomy and Astrophysics, 01 natural sciences, Glitch, Pulsar, Space and Planetary Science, 0103 physical sciences, Time derivative, Astrophysics::Solar and Stellar Astrophysics, False alarm, Hidden Markov model, Astrophysics - High Energy Astrophysical Phenomena, 010303 astronomy & astrophysics, Algorithm, Realization (probability), 0105 earth and related environmental sciences
Abstract

Pulsar timing experiments typically generate a phase-connected timing solution from a sequence of times-of-arrival (TOAs) by absolute pulse numbering, i.e. by fitting an integer number of pulses between TOAs in order to minimize the residuals with respect to a parametrized phase model. In this observing mode, rotational glitches are discovered, when the residuals of the no-glitch phase model diverge after some epoch, and glitch parameters are refined by Bayesian follow-up. Here an alternative, complementary approach is presented which tracks the pulse frequency $f$ and its time derivative $df/dt$ with a hidden Markov model (HMM), whose dynamics include stochastic spin wandering (timing noise) and impulsive jumps in $f$ and $df/dt$ (glitches). The HMM tracks spin wandering explicitly, as a specific realization of a discrete-time Markov chain. It discovers glitches by comparing the Bayes factor for glitch and no-glitch models. It ingests standard TOAs for convenience and, being fully automated, allows performance bounds to be calculated quickly via Monte Carlo simulations. Practical, user-oriented plots are presented of the false alarm probability and detection threshold (e.g. minimum resolvable glitch size) versus observational scheduling parameters (e.g. TOA uncertainty, mean delay between TOAs) and glitch parameters (e.g. transient and permanent jump sizes, exponential recovery time-scale). The HMM is also applied to $\sim 1$ yr of real data bracketing the 2016 December 12 glitch in PSR J0835-4510 as a proof of principle. It detects the known glitch and confirms that no other glitch exists in the same data with size $> 10^{-7} f$., Accepted for publication in The Astrophysical Journal

Published
2020

8. Phase-Continuous Frequency Line Track-Before-Detect of a Tone With Slow Frequency Variation

Author

P. Clearwater, Rob Evans, William Moran, Sofia Suvorova, L. Sun, and Andrew Melatos

Subjects
Radar tracker, 010308 nuclear & particles physics, Computer science, Gravitational wave, Detector, Phase (waves), 01 natural sciences, Track-before-detect, Sonar, Harmonic analysis, symbols.namesake, Amplitude, Fourier transform, Signal-to-noise ratio, 0103 physical sciences, Signal Processing, symbols, Electrical and Electronic Engineering, 010306 general physics, Hidden Markov model, Algorithm
Abstract

We consider optimal Bayesian detection of a slowly varying tone of unknown amplitude in situations characterized by very low signal-to-noise ratio (SNR) and a large number of measurements, as found in certain gravitational wave and passive sonar problems. We use a hidden Markov model (HMM) framework but, unlike typical HMM-based frequency line tracking methods, we develop a true track-before-detect algorithm, which does not threshold the blocked Fourier data and only considers frequency trails that have phase continuity across all HMM steps. We model the frequency and phase evolution as a phase-wrapped Ornstein–Uhlenbeck process. The resulting optimal detector is computationally efficient. The detectability improvement arising from phase continuity is characterized via comparative simulation for a mock, simplified gravitational wave search problem.

Published
2018
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9. Bayesian Recursive Estimation on the Rotation Group

Author

Stephen D. Howard, Sofia Suvorova, and Bill Moran

Subjects
Signal Processing (eess.SP), Mathematical optimization, Principle of maximum entropy, Approximation algorithm, Rigid body, Orientation (geometry), Prior probability, Maximum entropy probability distribution, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Algorithm, Recursive Bayesian estimation, Mathematics, Rotation group SO
Abstract

Tracking on the rotation group is a key component of many modern systems for estimation of the motion of rigid bodies. To address this problem, here we describe a Bayesian algorithm that relies on directional measurements for tracking on the special orthogonal (rotation) group. Its novelty lies in the use of maximum entropy distributions on these groups as models for the priors, and justifiable approximation algorithms that permit recursive implementation of such a model. We provide the solutions in a recursive closed form. In the two-dimensional case the parameters of the prior and posterior distributions can be computed exactly and the solution has low complexity. Adoption of this approach eliminates the problem of angle wrapping. In higher dimensions the exact solution cannot be computed, and it is necessary to make (very close) approximations, which is done here. We demonstrate in simulations that, in contrast with some other approaches, our algorithm produces very accurate and statistically meaningful outputs., I have posted it by mistake

Published
2020

10. Bounds on Multiple Sensor Fusion

Author

Peter M. Farrell, Sofia Suvorova, Bill Moran, Frederick R. Cohen, Stephen D. Howard, Douglas Cochran, Thomas Taylor, and Zengfu Wang

Subjects
Discrete mathematics, Linear programming, Computer Networks and Communications, Computer science, Computation, G.1.1, 020206 networking & telecommunications, Polytope, Systems and Control (eess.SY), D.6.3, 02 engineering and technology, Sensor fusion, Topology, Orthant, Range (mathematics), H.3.3, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Computer Science - Systems and Control, 020201 artificial intelligence & image processing, Extreme point, Real number
Abstract

We consider the problem of fusing measurements from multiple sensors, where the sensing regions overlap and data are non-negative---possibly resulting from a count of indistinguishable discrete entities. Because of overlaps, it is, in general, impossible to fuse this information to arrive at an accurate estimate of the overall amount or count of material present in the union of the sensing regions. Here we study the range of overall values consistent with the data. Posed as a linear programming problem, this leads to interesting questions associated with the geometry of the sensor regions, specifically, the arrangement of their non-empty intersections. We define a computational tool called the fusion polytope and derive a condition for this to be in the positive orthant thus simplifying calculations. We show that, in two dimensions, inflated tiling schemes based on rectangular regions fail to satisfy this condition, whereas inflated tiling schemes based on hexagons do., 23 pages

Published
2016
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11. Reconstructing Gravitational Wave Core-Collapse Supernova Signals with Dynamic Time Warping

Author

Sofia Suvorova, Jade Powell, and Andrew Melatos

Subjects
Physics, Dynamic time warping, 010308 nuclear & particles physics, Gravitational wave, Frequency band, Detector, FOS: Physical sciences, 01 natural sciences, LIGO, symbols.namesake, Wavelet, Gaussian noise, 0103 physical sciences, symbols, Waveform, 010306 general physics, Astrophysics - Instrumentation and Methods for Astrophysics, Algorithm, Instrumentation and Methods for Astrophysics (astro-ph.IM)
Abstract

Core-collapse supernovae (CCSNe) are a potential source for ground-based gravitational wave detectors, as their predicted emission peaks in the detectors' frequency band. Typical searches for gravitational wave bursts reconstruct signals using wavelets. However, as CCSN signals contain multiple complex features in the time-frequency domain, these techniques often struggle to reconstruct the entire signal. An alternative method developed in recent years involves applying principal component analysis (PCA) to a set of simulated CCSN models. This technique enables model selection between astrophysical CCSN models as well as waveform reconstruction. However, PCA faces its own difficulties, such as being unable to reconstruct signals longer than the simulations; many CCSN simulations are stopped before the emission peaks due to insufficient computational resources. In this study, we show how combining PCA with dynamic time warping improves the reconstruction of CCSN gravitational wave signals in Gaussian noise characteristic of Advanced LIGO at design sensitivity. For the waveforms used in this analysis, we find that the number of PCs needed to represent 90% of the data is reduced from nine to four by applying dynamic time warping and that the match between the original and reconstructed waveforms improves for signal-to-noise ratios in the range [0, 50].

Published
2019
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12. Parametric texture estimation and prediction using measured sea clutter data

Author

Keith Ing, Sofia Suvorova, Mark R. Morelande, and Bill Moran

Subjects
Pixel, Series (mathematics), Estimator, 020206 networking & telecommunications, 02 engineering and technology, Swell, law.invention, law, Computer Science::Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, Range (statistics), Clutter, 020201 artificial intelligence & image processing, Electrical and Electronic Engineering, Radar, Physics::Atmospheric and Oceanic Physics, Geology, Remote sensing, Parametric statistics
Abstract

In this study, the authors present a deterministic parametric sea clutter texture model for high-resolution radar backscatter at low-grazing angles in the open ocean. The clutter texture forms a component of the compound-Gaussian sea clutter model and they exploit the spatiotemporal relationships in the clutter by relating it to its physical source: sea swell. They present an efficient algorithm for the estimation of the spectral components for the parametric texture model through the estimation of two-dimensional (2D) ‘tones’ across contiguous range bins instead of a series of 1D estimates as is used elsewhere. Validation is performed by comparing the predictive fit for their estimator with a series of temporal estimators and a non-parametric estimator using measured sea clutter data from the Atlantic Ocean recorded by the Intelligent PIXel (IPIX) radar of McMaster University in Canada. Implementation of the spatiotemporal estimator results in a more parsimonious estimate with improved reliability due to the increased separation of the tones in 2D space. Their results are shown to agree with an established physical model for the sea swell.

Published
2016
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13. Golay Complementary Waveforms in Reed–Müller Sequences for Radar Detection of Nonzero Doppler Targets

Author

Bill Moran, Jiahua Zhu, Xuezhi Wang, Xiaotao Huang, and Sofia Suvorova

Subjects
Computational complexity theory, detection, 02 engineering and technology, lcsh:Chemical technology, Biochemistry, Article, Analytical Chemistry, law.invention, multiple nonzero Doppler targets, symbols.namesake, 0203 mechanical engineering, Binary Golay code, law, 0202 electrical engineering, electronic engineering, information engineering, Waveform, lcsh:TP1-1185, Electrical and Electronic Engineering, Radar, Reed–Müller sequences, Instrumentation, sidelobes suppression, Pointwise, 020301 aerospace & aeronautics, Signal processing, Golay complementary waveforms, 020206 networking & telecommunications, Atomic and Molecular Physics, and Optics, Transmission (telecommunications), symbols, Algorithm, Doppler effect
Abstract

Golay complementary waveforms can, in theory, yield radar returns of high range resolution with essentially zero sidelobes. In practice, when deployed conventionally, while high signal-to-noise ratios can be achieved for static target detection, significant range sidelobes are generated by target returns of nonzero Doppler causing unreliable detection. We consider signal processing techniques using Golay complementary waveforms to improve radar detection performance in scenarios involving multiple nonzero Doppler targets. A signal processing procedure based on an existing, so called, Binomial Design algorithm that alters the transmission order of Golay complementary waveforms and weights the returns is proposed in an attempt to achieve an enhanced illumination performance. The procedure applies one of three proposed waveform transmission ordering algorithms, followed by a pointwise nonlinear processor combining the outputs of the Binomial Design algorithm and one of the ordering algorithms. The computational complexity of the Binomial Design algorithm and the three ordering algorithms are compared, and a statistical analysis of the performance of the pointwise nonlinear processing is given. Estimation of the areas in the Delay–Doppler map occupied by significant range sidelobes for given targets are also discussed. Numerical simulations for the comparison of the performances of the Binomial Design algorithm and the three ordering algorithms are presented for both fixed and randomized target locations. The simulation results demonstrate that the proposed signal processing procedure has a better detection performance in terms of lower sidelobes and higher Doppler resolution in the presence of multiple nonzero Doppler targets compared to existing methods.

Published
2018

14. Controlled Tracking in Urban Terrain: Closing the Loop

Author

Sofia Suvorova, Patricia R. Barbosa, Edwin K. P. Chong, Bill Moran, Yugandhar Sarkale, and Yun Li

Subjects
Signal Processing (eess.SP), 0209 industrial biotechnology, Signal processing, Covariance matrix, Computer science, 02 engineering and technology, Systems and Control (eess.SY), Antenna diversity, law.invention, 020901 industrial engineering & automation, Mathematics (miscellaneous), Control and Systems Engineering, law, 0202 electrical engineering, electronic engineering, information engineering, Curve fitting, FOS: Electrical engineering, electronic engineering, information engineering, Clutter, Waveform, Computer Science - Systems and Control, 020201 artificial intelligence & image processing, Electrical and Electronic Engineering, Radar, Electrical Engineering and Systems Science - Signal Processing, Algorithm, Multipath propagation
Abstract

We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain. We propose a closed-loop active sensing system to address this problem by exploiting three distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. Specifically, at every radar scan, the waveform that yields the minimum trace of the one-step-ahead error covariance matrix is transmitted; the received signal goes through a matched-filter, and curve fitting is used to extract range and range-rate measurements that feed the LMIPDA-VSIMM algorithm for data association and filtering. Monte Carlo simulations demonstrate the effectiveness of the proposed system in an urban scenario contaminated by dense and uneven clutter, strong multipath, and limited line-of-sight., Comment: Submitted to Special Issue: Recent Advances on Data Fusion, Estimation in Navigation and Control in Asian Journal of Control

Published
2018
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15. Hidden Markov model tracking of continuous gravitational waves from young supernova remnants

Author

Andrew Melatos, Sofia Suvorova, W. Moran, L. Sun, and Robin J. Evans

Subjects
Physics, Gravitational-wave observatory, 010308 nuclear & particles physics, Gravitational wave, Noise (signal processing), Matched filter, FOS: Physical sciences, Astrophysics, Viterbi algorithm, 01 natural sciences, LIGO, symbols.namesake, Neutron star, 0103 physical sciences, symbols, Hidden Markov model, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), 010303 astronomy & astrophysics, Algorithm
Abstract

Searches for persistent gravitational radiation from nonpulsating neutron stars in young supernova remnants (SNRs) are computationally challenging because of rapid stellar braking. We describe a practical, efficient, semi-coherent search based on a hidden Markov model (HMM) tracking scheme, solved by the Viterbi algorithm, combined with a maximum likelihood matched filter, the $\mathcal{F}$-statistic. The scheme is well suited to analyzing data from advanced detectors like the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). It can track rapid phase evolution from secular stellar braking and stochastic timing noise torques simultaneously without searching second- and higher-order derivatives of the signal frequency, providing an economical alternative to stack-slide-based semi-coherent algorithms. One implementation tracks the signal frequency alone. A second implementation tracks the signal frequency and its first time derivative. It improves the sensitivity by a factor of a few upon the first implementation, but the cost increases by two to three orders of magnitude.

Published
2017

16. Hidden Markov model tracking of continuous gravitational waves from a neutron star with wandering spin

Author

Andrew Melatos, Sofia Suvorova, L. Sun, William Moran, and Robin J. Evans

Subjects
Physics, Gravitational-wave observatory, 010308 nuclear & particles physics, Gravitational wave, Matched filter, X-ray binary, FOS: Physical sciences, General Relativity and Quantum Cosmology (gr-qc), Astrophysics, Viterbi algorithm, 01 natural sciences, LIGO, General Relativity and Quantum Cosmology, Computational physics, symbols.namesake, Neutron star, 0103 physical sciences, symbols, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), 010303 astronomy & astrophysics, LIGO Scientific Collaboration
Abstract

Gravitational wave searches for continuous-wave signals from neutron stars are especially challenging when the star's spin frequency is unknown a priori from electromagnetic observations and wanders stochastically under the action of internal (e.g., superfluid or magnetospheric) or external (e.g., accretion) torques. It is shown that frequency tracking by hidden Markov model (HMM) methods can be combined with existing maximum likelihood coherent matched filters like the F-statistic to surmount some of the challenges raised by spin wandering. Specifically, it is found that, for an isolated, biaxial rotor whose spin frequency walks randomly, HMM tracking of the F-statistic output from coherent segments with duration T-drift = 10 d over a total observation time of T-obs = 1 yr can detect signals with wave strains h(0) > 2 x 10(-26) at a noise level characteristic of the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). For a biaxial rotor with randomly walking spin in a binary orbit, whose orbital period and semimajor axis are known approximately from electromagnetic observations, HMM tracking of the Bessel-weighted F-statistic output can detect signals with h(0) > 8 x 10(-26). An efficient, recursive, HMM solver based on the Viterbi algorithm is demonstrated, which requires similar to 10(3) CPU hours for a typical, broadband (0.5-kHz) search for the low-mass x-ray binary Scorpius X-1, including generation of the relevant F-statistic input. In a "realistic" observational scenario, Viterbi tracking successfully detects 41 out of 50 synthetic signals without spin wandering in stage I of the Scorpius X-1 Mock Data Challenge convened by the LIGO Scientific Collaboration down to a wave strain of h(0) = 1.1 x 10(-25), recovering the frequency with a root-mean-square accuracy of

Published
2016

17. Tracking in clutter using IMM-IPDA-based algorithms

Author

Sofia Suvorova and Darko Musicki

Subjects
business.industry, Computer science, Gaussian, Probabilistic logic, Aerospace Engineering, Pattern recognition, Tracking (particle physics), Measure (mathematics), symbols.namesake, symbols, Clutter, Artificial intelligence, Electrical and Electronic Engineering, business, Gaussian process, Algorithm
Abstract

We describe three single-scan probabilistic data association (PDA) based algorithms for tracking manoeuvering targets in clutter. These algorithms are derived by integrating the interacting multiple model (IMM) estimation algorithm with the PDA approximation. Each IMM model a posteriori state estimate probability density function (pdf) is approximated by a single Gaussian pdf. Each algorithm recursively updates the probability of target existence, in the manner of integrated PDA (IPDA). The probability of target existence is a track quality measure, which can be used for false track discrimination. The first algorithm presented, IMM-IPDA, is a single target tracking algorithm. Two multitarget tracking algorithms are also presented. The IMM-JIPDA algorithm calculates a posteriori probabilities of all measurement to track allocations, in the manner of the joint IPDA (JIPDA). The number of measurement to track allocations grows exponentially with the number of shared measurements and the number of tracks which share the measurements. Therefore, IMM-JIPDA can only be used in situations with a small number of crossing targets and low clutter measurement density. The linear multitarget IMM-IPDA (IMM-LMIPDA) is also a multitarget tracking algorithm, which achieves the multitarget capabilities by integrating linear multitarget (LM) method with IMM-IPDA. When updating one track using the LM method, the other tracks modulate the clutter measurement density and are subsequently ignored. In this fashion, LM achieves multitarget capabilities using the number of operations which are linear in the: number of measurements and the number of tracks, and can be used in complex scenarios, with dense clutter and a large number of targets.

Published
2008
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18. Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance

Author

Jim Schroeder and Sofia Suvorova

Subjects
Karhunen–Loève theorem, business.industry, Applied Mathematics, Pattern recognition, Target acquisition, Fourier mellin transform, Automatic target recognition, Computational Theory and Mathematics, Artificial Intelligence, Signal Processing, Rigid motion, Computer vision, Computer Vision and Pattern Recognition, Artificial intelligence, Electrical and Electronic Engineering, Statistics, Probability and Uncertainty, business, Digital signal processing, Mathematics
Abstract

Suvorova, S., and Schroeder, J., Automated Target Recognition Using the Karhunen–Loeve Transform with Invariance, Digital Signal Processing 12 (2002) 295–306 In this paper we present an automated target recognition (ATR) algorithm, based on the Karhunen–Loeve transform with rigid motion invariance. The ATR algorithm aims reduce the storage space and the processing time by comparison with a template-matching algorithm without the loss of performance. The algorithm has been tested on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset both for training and for classification.

Published
2002
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19. An Application of Wavelets in Tomography

Author

Sofia Suvorova, B. Moran, and Murk Bottema

Subjects
Discrete wavelet transform, Radon transform, Legendre wavelet, Applied Mathematics, Mathematical analysis, MathematicsofComputing_NUMERICALANALYSIS, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Inversion (meteorology), Data_CODINGANDINFORMATIONTHEORY, Wavelet, Computational Theory and Mathematics, Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Tomography, Electrical and Electronic Engineering, Statistics, Probability and Uncertainty, Algorithm, Continuous wavelet transform, Mathematics
Abstract

A method of tomographic inversion using wavelets is described which relies on the “diagonalization” property of wavelets for the derivative of the Hilber transform. Error bounds are given for reconstruction using the technique and an optimal wavelet which minimizes the error is presented. The method is well suited for inversion of from local data.

Published
1998
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