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In target-tracking applications, there may be situations where measurements from a given target arrive out of sequence at the processing center. This problem is commonly referred to as out-of-sequence measurements (OOSMs). So far, most of the existing solutions to this problem are based on retrodiction, where backward prediction of the current state is used to incorporate the OOSMs at the appropriate...
The paper addresses the problem of target-tracking in tactical military surveillance operations. More specifically, a closed-loop approach to adapt the sensing and tracking operations is proposed and compared to the conventional open-loop and static approach. The objective is to control and maintain, over a certain volume of interest and by way of clustering and scheduling strategies, the level of...
This paper discusses the application of holonic control paradigm to sensor management in military surveillance operations. Sensor management is described both as part of the data fusion process and as a control problem. The choice of holonic control as the most adequate architecture for sensor management in the military environment is explained and its application to surveillance operations illustrated...
In military command & control applications, the information quality requirements are very context-dependent and seldom predefined. This leaves much room for adaptation. In this paper, the duration of the search & lock-on operations of the fire control radar is estimated and used as an adaptation trigger. The proposed estimation process aims at establishing a quantitative relationship between...
This paper contributes to solve effectively stochastic resource allocation problems known to be NP-complete. To address this complex resource management problem, the merging of two approaches is made: The Q-decomposition model, which coordinates reward separated agents through an arbitrator, and the Labeled Real-Time Dynamic Programming (LRTDP) approaches are adapted in an effective way. The Q-decomposition...
This paper contributes to solve effectively stochastic resource allocation problems known to be NP-complete. To address this complex resource management problem, the merging of two approaches is made: The Q-decomposition model, which coordinates reward separated agents through an arbitrator, and the labeled real-time dynamic programming (LRTDP) approaches are adapted in an effective way. The Q-decomposition...
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