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Source localization using channel energy measurements of distributed sensors has been well studied, but assumes the target not to move significantly during both the communication between the sensors and the calculation. We want to estimate the trajectory of a moving target, which passes through a sensor field. The sensors are simple in the sense that they can only measure the received signal strength...
This paper studies sensor network surveillance performance at the automatic tracker output. In particular, we develop a simple analytical model for tracker performance, where the interest is in a compact representation of the impact of sensor revisit time. This model, combined with a previously developed contact fusion model, allows for an analysis of two fusion architectures: a standard centralized...
This paper is concerned with the application of target tracking in a network of sensors that provide binary output. The binary sensor network tracking problem is formulated in a sequential Bayesian estimation framework and is readily solved by means of a particle filter. We will perform sensor selection by means of a newly proposed scheme. This proposed approach is especially suitable for problems...
It has been shown that sensor networks and data fusion are effective in providing an accurate operational picture, even in jammed environments. However, a weakness with methods based on data from several sensors is that there is not always data available from all the sensors all the time. In such cases fusion can not be performed with good results, leading to difficulties in detecting true objects...
This paper presents a method for the simultaneous state and parameter estimation of finite-dimensional models of distributed systems monitored by a sensor network. In the first step, the distributed system is spatially and temporally decomposed leading to a linear finite-dimensional model in state space form. The main challenge is that the simultaneous state and parameter estimation of such systems...
We consider a network of sensors deployed to sense a spatio-temporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by each sensor can be modeled as a state-space process that is perturbed by random noise and parametrized by an unknown parameter. To estimate the unknown parameter from the measurements that the sensors sequentially collect,...
The decentralized quickest change detection problem is studied in sensor networks, where a set of sensors receive observations from a hidden Markov model X and send sensor messages to a central processor, called the fusion center, which makes a final decision when observations are stopped. It is assumed that the parameter thetas in the hidden Markov model for X changes from thetas0 to thetas1 at some...
In target tracking, standard sensors as radar and EO/IR observe the target with a negligible delay, since the speed of light is much larger than the speed of the target. This contribution studies the case where the ratio of the target and the propagation speed is not negligible, as is the case in sensor networks with microphones, geophones or sonars for instance, where the speed of air, ground waves...
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