Support-based and ML approaches to DOA estimation in a dumb sensor network

A recent paper by Marano et al. shows that a network of unconnected and completely direction-of-arrival (DOA)-blind sensors ("beepers") is able to perform DOA estimation quite effectively within the SENMA architecture (unlabeled polling performed by a mobile agent). The idea is that the mobile agent collects the periodic emissions of the polled sensors, with the time origin of such emissions being the passage of the acoustic wavefront. Depending on the relative orientation between the acoustic wavefront and the field of view of the mobile agent, the impinging times over different sensors are more or less clustered and so are the recorded emissions. On this basis, the DOA may be inferred. Here, two new estimators are proposed. One method (support-based) exploits the maximum spread between recorded times and is simple to implement, and its performance, measured in terms of mean square error, is improved significantly versus that proposed in the recent paper by Marano et al. In fact, the support-based estimator achieves performance close to that of the maximum-likelihood (ML) estimator-also investigated here-indicating that the support-based structure is perhaps suitable for tasks that involve cheap robust designs, such as sea/ground surveillance and sniper location.