— In this paper we study the problem of dynamic optimization of ping schedule in an active sonar buoy network deployed to provide persistent surveillance of a littoral area through multistatic detection. The goal of ping scheduling is to dynamically determine when to ping and which ping source to engage in order to achieve the desirable detection… (More)
A localization algorithm using radio interferometric measurements is presented. A probabilistic model is constructed that accounts for general noise models and lends itself to distributed computation. A message passing algorithm is derived that exploits the geometry of radio interferometric measurements and can support sparse network topologies and noisy… (More)
The problem of optimized distributed detection in a system of networked sensors involves a number of design aspects, including balancing probabilities of missed detection and false alarm as well as managing the communication resources through proper in-network information fusion. Moreover, a number of tradeoffs must be exercised, such as the one between the… (More)
A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to microorganism detection from mass spectrometry data.