CSI Amplitude Fingerprinting for Indoor Localization with Dictionary Learning

Abstract

With the rapid growth of the demand for location services in the indoor environment, fingerprint-based indoor positioning has attracted widespread attention due to its high-precision characteristics. This paper proposes a double-layer dictionary learning algorithm based on channel state information (DDLC). The DDLC system includes two stages. In the offline training stage, a two-layer dictionary learning architecture is constructed for the complex conditions of indoor scenes. In the first layer, for the input training data of different regions, multiple sub-dictionaries are generated corresponding to learning, and non-coherent promotion items are added to emphasize the discrimination between sparse coding in different regions. The second-level dictionary learning introduces support vector discriminant items for the fingerprint points inside each region, and uses Max-margin to distinguish different fingerprint points. In the online positioning stage, we first determine the area of the test point based on the reconstruction error, and then use the support vector discriminator to complete the fingerprint matching work. In this experiment, we selected two representative indoor positioning environments, and compared the DDLC with several existing indoor positioning methods. The results show that DDLC can effectively reduce positioning errors, and because the dictionary itself is easy to maintain and update, the characteristic of strong anti-noise ability can be better used in CSI indoor positioning work.

A Fingerprint Dictionary Processing Approach in Indoor Localization System Based on Wi-Fi

“…In this paper, we employed Convolutional Dictionary Learning (CDL) to process the fingerprint data of each RP in radio map to generate a dictionary that captures the characteristic feature of the site. CDL is a technique used in signal processing and image analysis, which involves learning a set of kernels from input signals [18][19][20] .It is an extension of dictionary learning, which aims to represent a signal as a linear combination of kernels from a pre-defined dictionary. The proposed scheme is named as Fingerprint Dictionary Preprocessing (FDP), which relies on CDL to improve the expressiveness and interpretability of data in radio map.…”