| 1. |
- Alam, Assad, et al.
(author)
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Cooperative driving according to Scoop
- 2011
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Reports (other academic/artistic)abstract
- KTH Royal Institute of Technology and Scania are entering the GCDC 2011 under the name Scoop –Stockholm Cooperative Driving. This paper is an introduction to their team and to the technical approach theyare using in their prototype system for GCDC 2011.
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| 2. |
- Blasco-Serrano, Ricardo, et al.
(author)
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A Measurement Rate-MSE Tradeoff for Compressive Sensing Through Partial Support Recovery
- 2014
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In: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 62:18, s. 4643-4658
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Journal article (peer-reviewed)abstract
- We study the fundamental relationship between two relevant quantities in compressive sensing: the measurement rate, which characterizes the asymptotic behavior of the dimensions of the measurement matrix in terms of the ratio m/ log n (m being the number of measurements and n the dimension of the sparse signal), and the mean square estimation error. First, we use an information-theoretic approach to derive sufficient conditions on the measurement rate to reliably recover a part of the support set that represents a certain fraction of the total signal power when the sparsity level is fixed. Second, we characterize the mean square error of an estimator that uses partial support set information. Using these two parts, we derive a tradeoff between the measurement rate and the mean square error. This tradeoff is achievable using a two-step approach: first support set recovery, then estimation of the active components. Finally, for both deterministic and random signals, we perform a numerical evaluation to verify the advantages of the methods based on partial support set recovery.
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| 3. |
- Blasco-Serrano, Ricardo, et al.
(author)
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An Achievable Measurement Rate-MSE Tradeoff in Compressive Sensing Through Partial Support Recovery
- 2013
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In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - New York : IEEE. - 9781479903566 ; , s. 6426-6430
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Conference paper (peer-reviewed)abstract
- For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example.
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| 4. |
- Chatterjee, Saikat, et al.
(author)
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Hybrid greedy pursuit
- 2011
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In: 19th European Signal Processing Conference (EUSIPCO 2011). - : European Association for Signal and Image Processing. ; , s. 343-347
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Conference paper (peer-reviewed)abstract
- For constructing the support set of a sparse vector in the standardcompressive sensing framework, we develop a hybridgreedy pursuit algorithm that combines the advantages ofserial and parallel atom selection strategies. In an iterativeframework, the hybrid algorithm uses a joint sparsity informationextracted from the independent use of serial and parallelgreedy pursuit algorithms. Through experimental evaluations,the hybrid algorithm is shown to provide a significantimprovement for the support set recovery performance.
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| 5. |
- Chatterjee, Saikat, et al.
(author)
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Look ahead orthogonal matching pursuit
- 2011
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In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. ; , s. 4024-4027
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Conference paper (peer-reviewed)
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| 6. |
- Chatterjee, Saikat, et al.
(author)
-
Projection-based and look ahead strategies for atom selection
- 2012
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In: IEEE Transactions on Signal Processing. - : IEEE. - 1053-587X .- 1941-0476. ; 60:2, s. 634-647
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Journal article (peer-reviewed)abstract
- In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms.
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| 7. |
- Chatterjee, Saikat, et al.
(author)
-
Robust matching pursuit for recovery of Gaussian sparse signal
- 2011
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In: 2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings. - 9781612842271 ; , s. 420-424
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Conference paper (peer-reviewed)abstract
- For compressive sensing (CS) recovery of Gaussian sparse signal, we explore the framework of Bayesian linear models to achieve a robust reconstruction performance in the presence of measurement noise. Using a priori statistical knowledge, we develop a minimum mean square error (MMSE) estimation based iterative greedy search algorithm. Through experimental evaluations, we show that the new algorithm provides a robust CS reconstruction performance compared to an existing least square based algorithm.
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| 8. |
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| 9. |
- Mårtensson, Jonas, 1976-, et al.
(author)
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The development of a cooperative heavy-duty vehicle for the GCDC 2011: Team Scoop
- 2012
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In: IEEE transactions on intelligent transportation systems (Print). - : IEEE Press. - 1524-9050 .- 1558-0016. ; 13:3, s. 1033-1049
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Journal article (peer-reviewed)abstract
- The first edition of the Grand Cooperative Driving Challenge (GCDC) was held in the Netherlands in May 2011. Nine international teams competed in urban and highway platooning scenarios with prototype vehicles using cooperative adaptive cruise control. Team Scoop, a collaboration between KTH Royal Institute of Technology, Stockholm, Sweden, and Scania CV AB, Sodertalje, Sweden, participated at the GCDC with a Scania R-series tractor unit. This paper describes the development and design of Team Scoop's prototype system for the GCDC. In particular, we present considerations with regard to the system architecture, state estimation and sensor fusion, and the design and implementation of control algorithms, as well as implementation issues with regard to the wireless communication. The purpose of the paper is to give a broad overview of the different components that are needed to develop a cooperative driving system: from architectural design, workflow, and functional requirement descriptions to the specific implementation of algorithms for state estimation and control. The approach is more pragmatic than scientific; it collects a number of existing technologies and gives an implementation-oriented view of a cooperative vehicle. The main conclusion is that it is possible, with a modest effort, to design and implement a system that can function well in cooperation with other vehicles in realistic traffic scenarios.
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| 10. |
- Sundin, Martin, et al.
(author)
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Beamformers For Sparse Recovery
- 2013
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In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - New York : IEEE. - 9781479903566 ; , s. 5920-5924
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Conference paper (peer-reviewed)abstract
- In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers insteadof the standard correlation filter. Using these beamformers, improved performance in the algorithms is obtained. In particular, we discuss beamformers for the average and worst case scenario and give methods for constructing them.
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