Asymptotically Optimal Parameter Estimation With Scheduled Measurements

To reduce the communication cost of a sensor node, this paper is concerned with an estimation framework with scheduled measurements for a linear system. A scheduler is designed to control the transmission of measurements from sensor to estimator, which results in that only a subset of measurements is transmitted to the estimator. We propose an innovation based scheduler and derive an analytical expression for the Cramér-Rao lower bound (CRLB) for the given scheduling strategy. Under a communication constraint, an adaptive scheduler and a corresponding recursive estimator are jointly designed to asymptotically attain the CRLB. The structure of the estimator bears close resemblance to the standard least square estimator (LSE) with the full set of sensor measurements. Moreover, we prove that the estimation performance in terms of mean-square estimation error is comparable to the standard LSE even under a moderate communication cost. The theoretical results are verified by simulations.