Simple and Fast Convex Relaxation Method for Cooperative Localization in Sensor Networks Using Range Measurements

摘要: We address the sensor network localization problem given noisy range measurements between pairs of nodes. approach nonconvex maximum-likelihood formulation via a known simple convex relaxation. exploit its favorable optimization properties to full obtain an that is completely distributed, has implementation at each node, and capitalizes on optimal gradient method attain fast convergence. offer parallel but also asynchronous flavor, both with theoretical convergence guarantees iteration complexity analysis. Experimental results establish leading performance. Our algorithms top accuracy comparable state-of-the-art by one order magnitude, using magnitude fewer communications.