Discriminant Minimization Search for Large-Scale RF-Based Localization Systems

— In large-scale fingerprinting localization systems, fine-grained location estimation and quick location determination are conflicting concerns. To achieve finer-grained localization, we have to collect signal patterns at a larger number of training locations. However, this will incur higher computation cost during the pattern-matching process. In this paper, we propose a novel discriminant minimization search (DMS)-based localization methodology. Continuous and differentiable discriminant functions are designed to extract the spatial correlation of signal patterns at training locations. The advantages of the DMSbased methodology are threefold. First, with through slope of discriminant functions, the exhaustive pattern-matching process can be replaced by an optimization search process, which could be done by a few quick jumps. Second, the continuity of the discriminant functions helps predict signal patterns at untrained locations so as to achieve finer-grained localization. Th...

• Type: Journal
• Year: 2011
• Where: TMC
• Authors: Sheng-Po Kuo, Yu-Chee Tseng
• Keywords: Discriminant Functions, Pattern-matching Process, TMC 2011, finer-grained Localization