| Summary: | To position indoor objects accurately and robustly,a novel node localization based on kernel function and Markov chains was presented,which employs Bayesian filter framework and radio fingerprinting technology.It uses kernel function to construct likelihood function to take full advantage of the similarity between observation and several training samples,which avoids the error brought by employing a priori determined distribution model.Furthermore,the proposed algorithm uses Markov chains to improve the localization accuracy and shorten the positioning time.It limits the search space of the matching grids with object’s previous state and the environment layout,and refuses the object’s impossible position jump during the moving process.Experiments confirm that the proposed localization outperforms the algorithm with Gaussian distribution model.
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