Kalman Filtering Under Innovation-Based Power Scheduling and Data Packet Drops

For a wireless sensor network (WSN) with a large number of low-cost, battery-driven, multiple transmission power leveled sensor nodes of limited transmission bandwidth, conservation of transmission power and bandwidth is of paramount importance. This paper considers the problem of Kalman filtering for linear stochastic systems subject to both transmission power constraint and data packet drops. The transmission of the acquired measurement from the sensor to the remote estimator is realized by sequentially transmitting every single component of the measurement to the remote estimator in one time period. The sensor node decides separately whether to use a high or low transmission power to communicate every component to the estimator across a packet-dropping wireless network based on the rule that promotes the power scheduling with the least impact on the estimator mean squared error. Under the standard assumption that the predicted density is Gaussian, the minimum estimation error estimator is therefore derived and furthermore, both the sufficient conditions and the necessary conditions for guaranteeing the stability of mean squared estimation error are established for general linear systems.

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