X. Zhong and E.J. Coyle (USA)
Nonhomogeneous Poisson sampling, reconstruction, Sensor Networks
A bandlimited signal emitted by an object moving through a field of sensors is sampled by the sensors it passes during its transit. If the sensors’ locations follow a spatial Poisson distribution, the temporal sampling of the signal can be considered to be Poisson with intensity ).( BA+=λ The clustered communication architecture of sensor networks, however, causes samples further from the clusterhead to be lost with a probability that increases with distance. This leads to sampling times that can be modeled as a non-homogeneous Poisson process with ).cos()( θωλ ++= tBAt We investigate the effect of this sampling strategy on a Kalman-filter-based reconstruction of the sensed signal. Its average performance, when θ is uniformly distributed on ]2,0[ π , is shown via numerical analysis to be essentially equivalent to Poisson sampling with intensity A=λ .
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