CDM: Compound dissimilarity measure and an application to fingerprinting-based positioning
This work addresses a specific problem in fingerprinting-based positioning by offering a more flexible similarity measure, though it is incremental as it builds on existing distance metrics.
The paper tackles the challenge of quantifying similarity for collections with missing attributes, such as in fingerprinting-based positioning, by proposing a compound dissimilarity measure (CDM) that improves building and floor identification accuracy by about 5% and reduces 2D positioning errors by half.
A non-vector-based dissimilarity measure is proposed by combining vector-based distance metrics and set operations. This proposed compound dissimilarity measure (CDM) is applicable to quantify similarity of collections of attribute/feature pairs where not all attributes are present in all collections. This is a typical challenge in the context of e.g., fingerprinting-based positioning (FbP). Compared to vector-based distance metrics (e.g., Minkowski), the merits of the proposed CDM are i) the data do not need to be converted to vectors of equal dimension, ii) shared and unshared attributes can be weighted differently within the assessment, and iii) additional degrees of freedom within the measure allow to adapt its properties to application needs in a data-driven way. We indicate the validity of the proposed CDM by demonstrating the improvements of the positioning performance of fingerprinting-based WLAN indoor positioning using four different datasets, three of them publicly available. When processing these datasets using CDM instead of conventional distance metrics the accuracy of identifying buildings and floors improves by about 5% on average. The 2d positioning errors in terms of root mean squared error (RMSE) are reduced by a factor of two, and the percentage of position solutions with less than 2m error improves by over 10%.