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Estimation and detection based on correlated observations from a heterogeneous sensor network

This paper considers the problem of parameter estimation and hypothesis testing based on observations from a network of heterogeneous sensors. The data is assumed to be correlated among the samples collected over time as well as among the data collected by different sensors. Moreover, it is assumed that the model parameters for the sensor data is not available. Correlation in the data is modeled using copula theory and a Markov chain, with unknown model parameters. Our proposed method uses the expectation maximization (EM) algorithm to estimate the unknown parameters of the model and to detect the state of nature. Numerical results are presented from simulations showing significant improvements for both parameter estimation and hypothesis testing compared to methods that ignore the correlation in sensors measurements.