Under this framework, the observation probabilistic model is one of main factors for infrared targets tracking performance. To effectively tackle the nonlinear and non-Gaussian state estimation problems, particle filtering is introduced to construct the theory framework of infrared target tracking. Robust infrared target tracking is an important and challenging research topic in many military and security applications, such as infrared imaging guidance, infrared reconnaissance, scene surveillance, etc. Robust infrared targets tracking with covariance matrix representation It would be discussed how Virtual Target Tracking would bring more diversity to target tracking research. The objective of this paper is to introduce a new concept, i.e., Virtual Target Tracking (VTT) for commercial applications of multi- target tracking algorithms and techniques as applied to mobile satellite communication networks. Mobile communication and computing can very well appreciate a huge market for Cellular Communication and Tracking Devices (CCTD), which will be tracking networked devices at the cellular level. On the other hand, the rapid growth of Global Communication Systems, Global Information Systems (GIS), and Global Positioning Systems (GPS) is creating new and more diverse challenges for multi- target tracking applications. Although the speed and maneuvering capability of current aerospace targets demand more efficient algorithms, many complex techniques have already been proposed in the literature, which primarily cover the defense applications of tracking methods. Traditionally, target tracking has been used for aerospace applications, such as, tracking highly maneuvering targets in a cluttered environment for missile-to- target intercept scenarios.

Virtual target tracking (VTT) as applied to mobile satellite communication networks Although the choice of the density control algorithm has little impact on the tracking precision, OGDC outperforms GAF and A3 in terms of tracking time. Moreover, among the evaluated density control algorithms, OGDC is the best option among the three. Our results show that DPE is a better choice for target tracking applications than RPE. In addition, we analyze the impact of network density, residual integration with density control, and k-coverage on both target tracking accuracy and network lifetime.

We adapt the density control algorithms to address the k-coverage problem. In this work, we analyze the impact of localization algorithms (RPE and DPE) and density control algorithms (GAF, A3 and OGDC) on target tracking applications. Therefore, density control algorithms are used to increase network lifetime while maintaining its sensing capabilities. In addition, sensor networks are often deployed in remote or hostile environments. Consequently, localization systems are essential for target tracking applications. The networks' ability to locate and track an object is directed linked to the nodes' ability to locate themselves. Target tracking is an important application of wireless sensor networks. On the Impact of Localization and Density Control Algorithms in Target Tracking Applications for Wireless Sensor NetworksĬampos, Andre N. In particular it is shown that an approximate filter known as the labeled multi-Bernoulli filter can simultaneously track one thousand five hundred targets in clutter on a standard laptop computer. This article demonstrates the capability of the random finite set approach to provide large scale multi- target tracking algorithms. Multi- target tracking for on-line applications involving a large number of targets is extremely challenging.

Multi- target tracking is intrinsically an NP-hard problem and the complexity of multi- target tracking solutions usually do not scale gracefully with problem size. Vo, Ba-Ngu Vo, Ba-Tuong Reuter, Stephan Lam, Quang Dietmayer, Klaus Towards large scale multi- target tracking

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