Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
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One of the main reasons in forming a sensor network is to combine the information seen from different sensors to produce a single integrated picture that is an accurate representation of the scene of interest. An often overlooked problem in network design is the proper registration of the sensors in the network. Sensor registration can be seen as the process of removing (accounting for) nonrandom errors, or biases, in the sensor data. Without properly accounting for these errors, the quality of the composite picture can, and oftentimes does, degrade. In this paper, we present an approach for solving the sensor registration problem, based on a new continuous meta-heuristic, when not all data is seen by all sensors, and the correspondence of data seen by the different sensors is not known a priori. Considering a real problem from the defense industry, we show that this approach performs better than other approaches in the literature.