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Estimation over heterogeneous sensor networks

Design trade-offs between estimation performance, processing delay and communication cost for a sensor scheduling problem is discussed. We consider a heterogeneous sensor network with two types of sensors: the first type has low-quality measurements, small processing delay and a light communication cost, while the second type is of high quality, but imposes a large processing delay and a high communication cost. Such a heterogeneous sensor network is common in applications, where for instance in a localization system the poor sensor can be an ultrasound sensor while the more powerful sensor can be a camera. Using a time-periodic Kalman filter, we show how one can find an optimal schedule of the sensor communication. One can significantly improve estimation quality by only using the expensive sensor rarely. We also demonstrate how simple sensor switching rules based on the Riccati equation drives the filter into a stable time-periodic Kalman filter.