{"id":1755,"date":"2015-03-21T06:20:03","date_gmt":"2015-03-21T06:20:03","guid":{"rendered":"http:\/\/www.kam.k.leang.com\/academics\/?page_id=1755"},"modified":"2024-09-12T19:20:19","modified_gmt":"2024-09-12T19:20:19","slug":"feedforward_pubs","status":"publish","type":"page","link":"http:\/\/www.kam.k.leang.com\/academics\/publications\/feedforward_pubs\/","title":{"rendered":"Feedforward control related work"},"content":{"rendered":"
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2024<\/h3>
53.<\/div>
\"Information-Theoretic<\/div>

N. R. Olsen; S. M. McKee; O. S. Haddadin; S. M. Lyon; J. E. Campbell; K. K. Leang<\/p>

Information-Theoretic Bayesian Inference for Multi-Agent Localization and Tracking of an RF Target with Unknown Waveform<\/a> Journal Article<\/span> <\/p>

In: <\/span>ASME J. Dyn. Syst. Meas. and Cont., Special Issue on Data-Driven Modeling and Control of Dynamical Systems (https:\/\/doi.org\/10.1115\/1.4066453), <\/span>vol. 146, <\/span>iss. 6, <\/span>pp. 061104, <\/span>2024<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{OlsenNR_2024_JDSMC,
\r\ntitle = {Information-Theoretic Bayesian Inference for Multi-Agent Localization and Tracking of an RF Target with Unknown Waveform},
\r\nauthor = {N. R. Olsen and S. M. McKee and O. S. Haddadin and S. M. Lyon and J. E. Campbell and K. K. Leang},
\r\nurl = {http:\/\/www.kam.k.leang.com\/academics\/pubs\/OlsenNR_2024_JDSMC.pdf},
\r\ndoi = {10.1115\/1.4065592},
\r\nyear  = {2024},
\r\ndate = {2024-08-22},
\r\nurldate = {2024-08-22},
\r\njournal = {ASME J. Dyn. Syst. Meas. and Cont., Special Issue on Data-Driven Modeling and Control of Dynamical Systems (https:\/\/doi.org\/10.1115\/1.4066453)},
\r\nvolume = {146},
\r\nissue = {6},
\r\npages = {061104},
\r\nabstract = {Information-theoretic motion planning and machine learning through Bayesian inference are exploited to localize and track a dynamic radio frequency (RF) emitter with unknown
\r\nwaveform (uncooperative target). A target-state estimator handles non-Gaussian distributions, while mutual information is utilized to coordinate the motion control of a network of mobile sensors (agents) to minimize measurement uncertainty. The mutual information is computed for pairs of sensors through a four-permutation-with-replacement process. The information surfaces are combined to create a composite map, which is then used by agents to plan their motion for more efficient and effective target estimation and tracking. Simulations and physical experiments involving micro-aerial vehicles with time difference of arrival (TDOA) measurements are performed to evaluate the performance of the algorithm. Results show that when two or three agents are used, the algorithm outperforms state-of-the-art methods. Results also show that for four or more agents, the performance is as competitive as an idealized static sensor network.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
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Information-theoretic motion planning and machine learning through Bayesian inference are exploited to localize and track a dynamic radio frequency (RF) emitter with unknown
\r\nwaveform (uncooperative target). A target-state estimator handles non-Gaussian distributions, while mutual information is utilized to coordinate the motion control of a network of mobile sensors (agents) to minimize measurement uncertainty. The mutual information is computed for pairs of sensors through a four-permutation-with-replacement process. The information surfaces are combined to create a composite map, which is then used by agents to plan their motion for more efficient and effective target estimation and tracking. Simulations and physical experiments involving micro-aerial vehicles with time difference of arrival (TDOA) measurements are performed to evaluate the performance of the algorithm. Results show that when two or three agents are used, the algorithm outperforms state-of-the-art methods. Results also show that for four or more agents, the performance is as competitive as an idealized static sensor network.<\/div>
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