Distributed, simple and stable network localization

Abstract

We propose a simple, stable and distributed algorithm which directly optimizes the nonconvex maximum likelihood criterion for sensor network localization, with no need to tune any free parameter. We reformulate the problem to obtain a gradient Lipschitz cost; by shifting to this cost function we enable a Majorization-Minimization (MM) approach based on quadratic upper bounds that decouple across nodes; the resulting algorithm happens to be distributed, with all nodes working in parallel. Our method inherits the MM stability: each communication cuts down the cost function. Numerical simulations indicate that the proposed approach tops the performance of the state of the art algorithm, both in accuracy and communication cost.

References in corpus (2)

• Distributed Maximum Likelihood Sensor Network Localization
• Cooperative Wireless Sensor Network Positioning via Implicit Convex Feasibility

Cited by in corpus (6)

• Alternating Minimization Based First-Order Method for the Wireless Sensor Network Localization Problem
• STRONG: Synchronous and asynchronous RObust Network localization, under Non-Gaussian noise
• Range and Bearing Data Fusion for Precise Convex Network Localization
• Dealing with bad apples: Robust range-based network localization via distributed relaxation methods
• DIeSEL: DIstributed SElf-Localization of a network of underwater vehicles
• LocDyn: Robust Distributed Localization for Mobile Underwater Networks