{"id":1319,"date":"2014-03-20T04:46:08","date_gmt":"2014-03-20T04:46:08","guid":{"rendered":"http:\/\/www.kam.k.leang.com\/academics\/?page_id=1319"},"modified":"2024-09-12T19:17:47","modified_gmt":"2024-09-12T19:17:47","slug":"proceedings","status":"publish","type":"page","link":"http:\/\/www.kam.k.leang.com\/academics\/publications\/proceedings\/","title":{"rendered":"Conference papers\/presentations"},"content":{"rendered":"
<\/a><\/form>

2023<\/h3>
89.<\/div>
\"A<\/div>

D. Y. Abramovitch, S. Andersson, K. K. Leang, W. S. Nagel; S. Ruben<\/p>

A Tutorial on Real-Time Computing Issues for Control Systems Proceedings Article<\/span> <\/p>

In: <\/span>American Control Conference, San Diego, CA, May 31-June 2, <\/span>2023<\/span>.<\/p>

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

@inproceedings{AbramovitchDY_2023_ACC,
\r\ntitle = {A Tutorial on Real-Time Computing Issues for Control Systems},
\r\nauthor = {D. Y. Abramovitch, S. Andersson, K. K. Leang, W. S. Nagel and S. Ruben},
\r\nyear  = {2023},
\r\ndate = {2023-06-02},
\r\nurldate = {2023-06-02},
\r\nbooktitle = {American Control Conference, San Diego, CA, May 31-June 2},
\r\nabstract = {This paper presents a tutorial on the elements of computation in a real-time control system. Unlike conventional computation or even computation in digital signal processing systems, computation in a feedback loop must be sensitive to issues of latency and noise around the loop. This presents some fundamental requirements, limitations, and design constraints not seen in other computational applications. The logic of presenting such a tutorial is that while the computer technology changes at a rapid pace, the principles of how we match that technology to the constraints of a feedback loop remain consistent over the years. We will discuss the different computational chains in a feedback system, ways to conceptualize the effects of time delay and jitter on the system, and present a three-layer-model for programming real-time computations. The tutorial also presents some filter and state-space structures that are useful for real-time computation. It concludes with an overview of the different sample rate ranges currently used in some typical control problems and a short discussion of how business models affect our choices in real-time computation.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
This paper presents a tutorial on the elements of computation in a real-time control system. Unlike conventional computation or even computation in digital signal processing systems, computation in a feedback loop must be sensitive to issues of latency and noise around the loop. This presents some fundamental requirements, limitations, and design constraints not seen in other computational applications. The logic of presenting such a tutorial is that while the computer technology changes at a rapid pace, the principles of how we match that technology to the constraints of a feedback loop remain consistent over the years. We will discuss the different computational chains in a feedback system, ways to conceptualize the effects of time delay and jitter on the system, and present a three-layer-model for programming real-time computations. The tutorial also presents some filter and state-space structures that are useful for real-time computation. It concludes with an overview of the different sample rate ranges currently used in some typical control problems and a short discussion of how business models affect our choices in real-time computation.<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
2022<\/h3>
88.<\/div>
\"Information-Based<\/div>
N. R. Olsen, S. M. McKee, O. S. Haddadin, S. Lyon, J. Campbell, K. K. Leang<\/p>
Information-Based Mobile Sensor Network Localization and Tracking of an Uncooperative Target<\/a> Proceedings Article<\/span> <\/p>
In: <\/span>IEEE Military Communications Conference (MILCOM), November 28 - December 2, Rockville, MD, USA, <\/span>pp. 490-495, <\/span>2022<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@inproceedings{OlsenNR_2022_MILCOM,
\r\ntitle = {Information-Based Mobile Sensor Network Localization and Tracking of an Uncooperative Target},
\r\nauthor = {N. R. Olsen, S. M. McKee, O. S. Haddadin, S. Lyon, J. Campbell, K. K. Leang},
\r\ndoi = {10.1109\/MILCOM55135.2022.10017826},
\r\nyear  = {2022},
\r\ndate = {2022-12-02},
\r\nurldate = {2022-12-02},
\r\nbooktitle = {IEEE Military Communications Conference (MILCOM), November 28 - December 2, Rockville, MD, USA},
\r\npages = {490-495},
\r\nabstract = {This paper focuses on the localization and tracking of a radio frequency (RF) emitter with unknown waveform (uncooperative target) using an information-based centralized network of mobile, collaborative sensors (agents). Each agent utilizes the measured waveform parameters such as time difference of arrival (TDOA), frequency difference of arrival (FDOA), or differential received signal strength (DRSS). The proposed algorithm leverages Bayesian estimation to handle non-Gaussian measurements and it employs information-theoretic motion planning to move sensors to areas of high information-gain to minimize measurement uncertainty. Mutual information is computed for pairs of sensor through a four-permutation with replacement process. The generated mutual information surfaces are combined and then used to plan the motion of each sensor in the network. Simulation results involving TDOA measurements are presented to illustrate the effectiveness of the algorithm, where it is shown that three agents are able to accurately track a target that moves at the same speed as the agents.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
This paper focuses on the localization and tracking of a radio frequency (RF) emitter with unknown waveform (uncooperative target) using an information-based centralized network of mobile, collaborative sensors (agents). Each agent utilizes the measured waveform parameters such as time difference of arrival (TDOA), frequency difference of arrival (FDOA), or differential received signal strength (DRSS). The proposed algorithm leverages Bayesian estimation to handle non-Gaussian measurements and it employs information-theoretic motion planning to move sensors to areas of high information-gain to minimize measurement uncertainty. Mutual information is computed for pairs of sensor through a four-permutation with replacement process. The generated mutual information surfaces are combined and then used to plan the motion of each sensor in the network. Simulation results involving TDOA measurements are presented to illustrate the effectiveness of the algorithm, where it is shown that three agents are able to accurately track a target that moves at the same speed as the agents.<\/div>
Close<\/a><\/p><\/div>