In this letter, we apply a Bernoulli Filter for moving target detection and tracking using real Multiple Input Multiple Output radar data in a challenging environment. The interest in MIMO radars has been growing since they can provide high performance at lower cost than conventional antenna arrays. The Bernoulli Filter performs tracking using the Track-Before-Detect approach, which is required to achieve satisfactory tracking performance in low signal-to-noise scenarios. The algorithm is validated with real MIMO data of a walking person.

This paper presents a novel approach to multitarget multisensor tracking, based on the combination of a probability hypothesis density (PHD) smoother and hard multisensor multiscan data association (MMDA) used in a feedback connection. The PHD smoother allows to initiate target tracks without resorting to complicated measurement-to-measurement association procedures while the feedback from the hard MMDA, besides providing track labeling, makes the PHD smoother, and hence the overall tracker, more robust to missed detections and false alarms. An application of the proposed tracker to passive multistatic radar tracking is worked out in order to demonstrate its effectiveness in critical situations where the lack of single-sensor observability prevents the use of traditional track initiation methods. As a further contribution, an extension to the multisensor case of the multicommodity approach to multiscan data association, originally presented for the single-sensor case, is provided.

This paper deals with the allocation of resources and the creation of distribution trees for P2P video streaming. Assuming that the video distribution is based on a Multiple Description Codec (MDC), the proposed approach performs jointly the two tasks, in a completely distributed client-based fashion, by means of an evolutionary (socially inspired) game played by all peers. The key idea of the evolutionary game is that each peer continuously measures its own utility (i.e., video quality) and, by periodical comparison with other randomly chosen peers, tries to mimic the behavior (i.e., resource allocation and video distributors) of peers with higher utility. Extensive simulation experiments by Peersim have been carried out in order to assess the performance of the proposed technique. The obtained results have highlighted remarkable benefits in terms of scalability, fast adaptation to churning, high degree of cooperation among peers and self-organization of the distribution multitree network.

A PHD (Probability Hypothesis Density) filter and multiscan association are combined in a feedback fashion in order to provide robust and efficient multitarget tracking. The resulting hybrid tracker, thanks to the feedback connection, provides remarkable performance improvements with respect to both an open-loop PHD filter with estimate extraction via clustering and a traditional tracker equipped with a track formation logic.

The paper will address the estimation of road traffic intensity from available measurements of mobile vehiclespsila coordinates. To this end, the work will jointly exploit PHD (probability hypothesis density) filtering techniques based on the so called particle filter approach and road-map information.

Multiscan data association can significantly enhance tracking performance in critical radar surveillance scenarios involving multiple targets, low detection probability, high false alarm probability, evasive target maneuvers and finite radar resolution. Unfortunately, however, this approach is affected by the curse of dimensionality which hinders its real-time application for tracking problems with short scan periods and/or long association windows and/or many measurements. In this paper it is shown how the formulation of the multiscan association as a single commodity flow optimization problem allows a relaxation of the association problem which, on one hand, guarantees close-to-optimal association performance and, on the other hand, implies a significant reduction of the computational load.

In this paper, a square-root equality-constrained linear filter is used to track a target moving along a road. This choice provides a computationally efficient, algorithmically simple and numerically robust solution for the implementation of a VS-DMM (variable structure-dynamic multiple model) filter. A performance evaluation via Monte Carlo simulations shows the effectiveness of the proposed approach.