Real-time tracking for sensor networks via sdp and gradient method
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Accurate distributed range-based positioning algorithm for wireless sensor networks
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Distributed wireless sensor network localization via sequential greedy optimization algorithm
IEEE Transactions on Signal Processing
Universal rigidity: towards accurate and efficient localization of wireless networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Dimension reduction and visualization of large high-dimensional data via interpolation
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Exploiting temporal stability and low-rank structure for localization in mobile networks
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Explicit Sensor Network Localization using Semidefinite Representations and Facial Reductions
SIAM Journal on Optimization
On Equivalence of Semidefinite Relaxations for Quadratic Matrix Programming
Mathematics of Operations Research
Universal Rigidity and Edge Sparsification for Sensor Network Localization
SIAM Journal on Optimization
ACM Transactions on Mathematical Software (TOMS)
Computational Optimization and Applications
Engineering Applications of Artificial Intelligence
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Recently, a semidefinite programming (SDP) relaxation approach has been proposed to solve the sensor network localization problem. Although it achieves high accuracy in estimating the sensor locations, the speed of the SDP approach is not satisfactory for practical applications. In this paper we propose methods to further relax the SDP relaxation, more precisely, to relax the single semidefinite matrix cone into a set of small-size semidefinite submatrix cones, which we call a sub-SDP (SSDP) approach. We present two such relaxations. Although they are weaker than the original SDP relaxation, they retain the key theoretical property, and numerical experiments show that they are both efficient and accurate. The speed of the SSDP is even faster than that of other approaches based on weaker relaxations. The SSDP approach may also pave a way to efficiently solving general SDP problems without sacrificing the solution quality.