In most conventional tracking systems. only the tar-et kinematic information is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improv...
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ISBN:
(纸本)0819450774
In most conventional tracking systems. only the tar-et kinematic information is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. In addition. the use of tar-et class information in data association can improve discrimination by yielding purer tracks and preserve their continuity. In this paper. we present the integrated use of target classification information and target kinematic information for target tracking. In our approach, target class information is integrated into the data association process using the two-dimensional (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The latter is an optimization based MHT. A generic model of the classifier output is considered and its use in association likelihoods is discussed. The multiframe association likelihood is developed to include the classification results based on the confusion matrix that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparison with and without the use of class information in data association is presented on a ground target tracking problem where targets are moving in an open field and their tracks can merge, branch and cross. Simulation results quantify the benefits of classification aided data association for improved target tracking, especially in the presence of association uncertainty in kinematic measurements. Also the benefit of S-D (multiframe) association vs. 2-D association is investigated for different quality classifiers. The main contribution is the development of the methodology to incorporate exactly the classification information into multidimensional (mu
Network design for sliding scheduled traffic is a highly-complex task that has been dealt by multistep approaches. We propose a scalable method, based on Lagrangean Relaxation, which can perform integrated scheduling,...
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ISBN:
(纸本)9781424426065
Network design for sliding scheduled traffic is a highly-complex task that has been dealt by multistep approaches. We propose a scalable method, based on Lagrangean Relaxation, which can perform integrated scheduling, routing and wavelength assignment. (C) 2009 Optical Society of America
We consider a fully loaded, worst-case SNR degradation as an abstraction metric for impairment aware link virtualization. For the NSF topology with minimal fully connected load, the power optimized SNR is only 0.6 dB ...
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ISBN:
(纸本)9781943580071
We consider a fully loaded, worst-case SNR degradation as an abstraction metric for impairment aware link virtualization. For the NSF topology with minimal fully connected load, the power optimized SNR is only 0.6 dB better.
We investigate potential network-cost savings due to the multichannel compensation of nonlinearities generated by subcarriers within the same superchannel. Two case studies (European and German networks) demonstrated ...
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ISBN:
(纸本)9781557529930
We investigate potential network-cost savings due to the multichannel compensation of nonlinearities generated by subcarriers within the same superchannel. Two case studies (European and German networks) demonstrated tangible cost reductions (6-11%) using 5 adjacent subcarriers.
We propose an integrated control mechanism of optical circuit switching for both general data center traffics and deep distributed learning applications. Semi-physical evaluations show a relative throughput of 1.27 an...
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ISBN:
(纸本)9798350377583
We propose an integrated control mechanism of optical circuit switching for both general data center traffics and deep distributed learning applications. Semi-physical evaluations show a relative throughput of 1.27 and a 6.18x speedup in a 256-block network constructed by MEMS-based optical switches. (c) 2024 The Author(s)
The combinatorial optimization problem of multidimensional assignment has been treated with renewed interest because of its extensive application in target tracking, cooperative control, robotics and image processing....
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ISBN:
(纸本)081945351X
The combinatorial optimization problem of multidimensional assignment has been treated with renewed interest because of its extensive application in target tracking, cooperative control, robotics and image processing. In this work we particularly concentrate on data association in multisensor-multitarget tracking algorithms, in which solving the multidimensional assignment is an essential step. Current algorithms generate good suboptimal solutions (with quantifiable accuracy) to these problems in pseudo polynomial time. However, in dense scenarios these methods can become inefficient because of the resulting dense candidate association tree. Also, in order to generate the top m (or ranked) solutions these algorithms need to solve a number of optimization problems, which increases the computational complexity significantly. In this paper we develop a Randomized Heuristic Approach (RHA), in which, in each step, instead of choosing the best solution indicated by the heuristic, one of the solutions is chosen randomly depending on the "probability" associated with it. The resulting algorithm produces solutions that are as good as or better than those produced by Lagrange relaxation-based algorithms that have much higher computational complexity. This method also produces other ranked best solutions with no further computational requirement.
The combinatorial optimization problem of multidimensional assignment has been treated with renewed interest because of its extensive application in target tracking, cooperative control, robotics and image processing....
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ISBN:
(纸本)0819457949
The combinatorial optimization problem of multidimensional assignment has been treated with renewed interest because of its extensive application in target tracking, cooperative control, robotics and image processing. In this work, we particularly concentrate on data association in multisensor-multitarget tracking algorithms, in which solving the multidimensional assignment is an essential step. Current algorithms generate good suboptimal solutions (with quantifiable accuracy) to these problems in pseudo-polynomial time. However, in dense scenarios these methods can become inefficient because of the resulting dense candidate association tree. Also, in order to generate the top m (or ranked) solutions these algorithms need to solve a number of optimization problems, which increases the computational complexity significantly. In this paper we develop a Randomized Heuristic Approach (RHA) for multidimensional assignment problems with decomposable costs (likelihoods). This algorithm builds the initial solution by solving successive 2-D problems optimally by auction algorithm and then performs a number of local search iterations to update the solution using a randomized heuristic technique. Unlike many assignment algorithms the RHA does not need the complete candidate assignment tree to start with. Instead, it constructs this tree as required. Results show that the RHA requires only a small fraction of the assignment tree and these results in a considerable reduction of computational cost in problems, for example, related to target tracking. Results show that the RHA, on an average, produces better solutions than those produced by Lagrange relaxation-based multidimensional assignment algorithm which has higher computational complexity. Also, using the different solutions obtained in RHA iterations, top m solutions can be constructed with no further computational requirement. These solutions can be utilized in a soft decision based algorithm which performs much better tha
This paper presents a hardware accelerated QoT estimation tool used in the DICONET impairment-aware optical network. Performance evaluation is given by examining different network scenarios in terms of network size an...
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ISBN:
(纸本)9781557528841
This paper presents a hardware accelerated QoT estimation tool used in the DICONET impairment-aware optical network. Performance evaluation is given by examining different network scenarios in terms of network size and number of wavelengths. (C) 2010 Optical Society of America
We provide a guideline on the size of the MxM WSS needed to create future scalable OXCs. Analyses show that 10x10 WSSs can create an OXC that is scalable to 40x40 with marginal routing-performance offset.
ISBN:
(纸本)9781943580071
We provide a guideline on the size of the MxM WSS needed to create future scalable OXCs. Analyses show that 10x10 WSSs can create an OXC that is scalable to 40x40 with marginal routing-performance offset.
Sleep-mode enabled transponders and regenerators yield to substantial energy savings;however, their non-negligible wake-up time may degrade the network performance. We show that an appropriate dimensioning of the devi...
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ISBN:
(纸本)9781557529930
Sleep-mode enabled transponders and regenerators yield to substantial energy savings;however, their non-negligible wake-up time may degrade the network performance. We show that an appropriate dimensioning of the devices per node can compensate such effect.
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