A Two-Stage Global Estimation Scheme for Multiband Delay Estimation in Wireless Localization.

In location-aware networks, the time of arrival (TOA)-based localization techniques have been widely employed. To achieve high-accuracy delay estimation, a number of multiband-based algorithms have been proposed recently, which exploit the channel state information (CSI) measurements over multiple non-contiguous frequency bands. However, to our best knowledge, there still lacks an efficient scheme that fully exploits the multiband gains when phase distortion factors are considered, due to that the associated multi-parameter estimation problem contains many local optimums and the existing algorithms can easily get stuck in a "bad" local optimum. To address these issues, we propose a novel two-stage global estimation (TSGE) scheme for multiband delay estimation. In the coarse stage, we propose a weighted multiple signal classification (MUSIC) algorithm to achieve an initial delay estimation based on a coarse signal model. The estimation problem derived from the coarse signal model contains less local optimums and thus a more stable estimation can be achieved than directly using the original signal model. Then in the refined stage, with the help of coarse estimation results to narrow down the search range, we perform a global delay estimation using a particle swarm optimization (PSO) algorithm based on a refined multiband signal model to exploit the multiband gains to further improve the estimation accuracy. Simulation results show that the proposed TSGE significantly outperforms the benchmarks.