Subspace Techniques for Vision-Based Node Localization in Wireless Sensor Networks

We present novel techniques for localization of nodes in a wireless image sensor network. Based on visual observations of a moving object by the network nodes, the proposed techniques employ simple image processing functions to produce equations that contain the node positions and orientation angles as the unknown parameters. Observations made at the nodes relate the position of the observed object to the physical coordinates of the node via the mapped position of the object in the node's image plane. In one formulation of the problem, multiple observations by a network node from a moving beacon with known coordinates result in a system of equations with a rank-deficient matrix. Hence, the solution for the desired node coordinates lies in the null space of the data matrix. In a second formulation, a different configuration of image sensor deployment with more degrees of freedom results in a least-squares solution for the unknown parameters. In a third formulation, multiple observations are made at each node from a target which moves at a fixed velocity vector. The solution to this problem formulation is also shown to correspond to the null space of the data matrix. The proposed algorithms are based on in-node processing and hence are scalable to large networks. Simulation and experimental results are provided in the paper