Davide Macagnano
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Davide Macagnano has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Giuseppe Thadeu Freitas de Abreu
Constructor University
2 shared publications - Flavio Esposito
Department of Computer Science, Saint Louis University, USA
1 shared publication - Giuseppe Destino
Center for Wireless Communications, University of Oulu, Finland
1 shared publication
Latest Publications
- 1 September 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21669570,p. 1-5
Tracking Multiple Dynamic Targets with Multidimensional Scaling
We consider the problem of tracking multiple targets in the presence of imperfect and incomplete ranging information, focusing on the impact of target dynamics. The targets are assumed to describe independent, continuous and differentiable trajectories with non-stationary (dynamic) statistics, i.e., with variable velocities and accelerations. The impact of such dynamics onto the performance, computational complexity and memory requirements of two tracking techniques, namely, the Kalman filter (KF) and multidimensional scaling (MDS), is investigated. The main feature of the MDS-based tracking algorithm, which we proposed in an earlier work, is that tracking is performed over the eigenspace of a Nystrom-Gram kernel matrix constructed with no a-priori knowledge of the statistics of target trajectories. Consequently, tracking becomes a problem of updating the eigenspace given new input data, which is achieved with an iterative Jacobian eigen-decomposition technique. An advantage of this technique over the KF is that tracking accuracy is independent on target dynamics. Furthermore, the number of iterations required to update the eigenspace, is shown to grow only logarithmically with the target dynamics and with the number of simultaneously tracked targets. As a result, the MDS-based tracking algorithm with Jacobian eigenspace updating becomes more efficient than the KF as soon as a relatively small number of targets are simultaneously tracked, and/or target dynamics exceeds a certain threshold.
- 1 September 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21669570,p. 1-5
Tracking Multiple Dynamic Targets with Multidimensional Scaling
We consider the problem of tracking multiple targets in the presence of imperfect and incomplete ranging information, focusing on the impact of target dynamics. The targets are assumed to describe independent, continuous and differentiable trajectories with non-stationary (dynamic) statistics, i.e., with variable velocities and accelerations. The impact of such dynamics onto the performance, computational complexity and memory requirements of two tracking techniques, namely, the Kalman filter (KF) and multidimensional scaling (MDS), is investigated. The main feature of the MDS-based tracking algorithm, which we proposed in an earlier work, is that tracking is performed over the eigenspace of a Nystrom-Gram kernel matrix constructed with no a-priori knowledge of the statistics of target trajectories. Consequently, tracking becomes a problem of updating the eigenspace given new input data, which is achieved with an iterative Jacobian eigen-decomposition technique. An advantage of this technique over the KF is that tracking accuracy is independent on target dynamics. Furthermore, the number of iterations required to update the eigenspace, is shown to grow only logarithmically with the target dynamics and with the number of simultaneously tracked targets. As a result, the MDS-based tracking algorithm with Jacobian eigenspace updating becomes more efficient than the KF as soon as a relatively small number of targets are simultaneously tracked, and/or target dynamics exceeds a certain threshold.
- 1 March 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 297-302
MAC Performances for Localization and Tracking in Wireless Sensor Networks
Time delay rather then throughput, is a constraint of greater importance in tracking systems. In particular, the maximum accces delay permissible by the application ins strongly related to the dynamics of theracked objects. The purpose of this article is to study the performance of a new media access control (MAC) technology specifically suited for LDR UWB systems [3] under the point of view of a tracking application. Specifically, the time delay necessary to collect the ranging information in both, star and meshed topology networks, have been studied as function of the number of mobiles in the network. More importantly, we propose two new ranging packet to be used inside the aforementioned MAC, in order to achieve in both the network topologies, a clear advantage compared to the current solution.
- 1 March 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 297-302
MAC Performances for Localization and Tracking in Wireless Sensor Networks
Time delay rather then throughput, is a constraint of greater importance in tracking systems. In particular, the maximum accces delay permissible by the application ins strongly related to the dynamics of theracked objects. The purpose of this article is to study the performance of a new media access control (MAC) technology specifically suited for LDR UWB systems [3] under the point of view of a tracking application. Specifically, the time delay necessary to collect the ranging information in both, star and meshed topology networks, have been studied as function of the number of mobiles in the network. More importantly, we propose two new ranging packet to be used inside the aforementioned MAC, in order to achieve in both the network topologies, a clear advantage compared to the current solution.