Robust Sensor Network Positioning Based on Projections onto Circular and Hyperbolic Convex Sets (POCS)

We consider the problem of locating a signal-source node, using characteristic signals emitted by the node that are captured by a set of sensor nodes. This estimation problem has often been formulated as a weighted least-squares problem in the literature. Received signal strength and asynchronous time-of-arrival measurements, however, give rise to objective functions with multiple local minima and saddle-points, complicating the optimization process. Recently, the method of projection onto convex sets (POCS) was suggested as a means to estimate source position, when received signal strength measurements are available. POCS has been shown to be robust to local minima in the objective function, is of low complexity, and is possible to distribute over the sensor nodes in the network. The drawback of POCS, when convex sets bounded by circles are used, is its poor performance in locating source nodes outside the outer perimeter of sensor nodes. We propose an extension to the presented POCS algorithm, called hyperbolic POCS, that increases the performance of circular POCS, allows for positioning of source nodes outside the outer perimeter of sensor nodes, and is applicable also for the case of asynchronous time-of-arrival measurements