This paper presents a new coverage formulation addressing the quality of service of sensor networks that cooperatively detect targets traversing a region of interest. The problem of track coverage consists of finding the positions of n sensors such that a Lebesgue measure on the set of tracks detected by at least k sensors is optimized. This paper studies the geometric properties of the network, addressing a deterministic track-coverage formulation and binary sensor models. It is shown that the tracks detected by a network of heterogeneous omnidirectional sensors are the geometric transversals of non-translates families of circles. A novel methodology based on cone theory is presented for representing and measuring sets of transversals in closed-form. Then, the solution of the track-coverage problem can be formulated as a nonlinear program (NLP). The numerical results show that this approach can improve track coverage by up to two orders of magnitude compared to grid and random deployments. Also, it can be used to reduce the number of sensors required to achieve a desired detection performance by up to 50%, and to optimally replenish or reposition existing sensor networks.
['Kelli Baumgartner', 'Silvia Ferrari']
A Geometric Transversal Approach to Analyzing Track Coverage in Sensor Networks