Convex Optimization
Semidefinite programming based algorithms for sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Second-Order Cone Programming Relaxation of Sensor Network Localization
SIAM Journal on Optimization
IEEE Transactions on Signal Processing
An accurate algebraic solution for moving source location using TDOA and FDOA measurements
IEEE Transactions on Signal Processing
A simple and efficient estimator for hyperbolic location
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
On the Solution of the GPS Localization and Circle Fitting Problems
SIAM Journal on Optimization
A flexible semi-definite programming approach for source localization problems
Digital Signal Processing
Hi-index | 35.68 |
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem. We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.