Low-Cost Indoor Wireless Fingerprint Location Database Construction Methods: A Review

Abstract

The fingerprint positioning has achieved remarkable results in indoor localization tasks, but the method usually relies on a large amount of fingerprint data to build a fingerprint database, and the amount and diversity of fingerprint data will directly affect the effectiveness of fingerprint positioning. Since fingerprint acquisition is limited and disturbed by space and time, it consumes a lot of labor and time costs to collect fingerprint data in the localization environment, and wireless fingerprint data is time-sensitive and environment-dependent, and changes in the localization environment will reduce the usability of the existing fingerprint database. The complex and repetitive fingerprint acquisition work seriously affects the feasibility of practical deployment of fingerprint positioning systems in the positioning environment. Therefore, the study of low-cost wireless fingerprint database construction methods has become an inevitable part of promoting the widespread deployment of indoor fingerprint positioning systems. In this paper, we introduce the traditional data augmentation-based approach and the advanced machine learning model-based approach, systematically presenting the underlying models and algorithms of both. The former reviews the application of two traditional data enhancement methods, namely channel propagation models and interpolation or regression, to the construction of low-cost wireless fingerprint databases, while the latter taps into techniques for reducing the cost of fingerprint database construction by combining generative adversarial networks and small-sample learning models with the indoor localization domain. Finally, we discuss the current challenges and future research directions for achieving high-performance indoor localization based on low-cost wireless fingerprint databases, and suggest some useful research guidelines.

Indoor positioning fingerprint database construction based on CSA-DBSCAN and RCVAE-GAN

“…The accuracy of the fingerprint positioning system depends mainly on the level of similarity between the fingerprint data being located and the fingerprint data of the actual position. The diversity and quantity of fingerprint data collected during the offline phase will significantly affect the accuracy of the positioning system [7][8][9]. Building an offline fingerprint database requires technicians to collect data at multiple reference points over a period of time.…”

A Performance Improvement for Indoor Positioning Systems Using Earth’s Magnetic Field

“…Using a fixed access point (AP) for mobile phones, the values for wireless signal strength, which is the received signal strength indicator (RSSI), are applied for recent indoor positioning algorithms. RADAR positioning technology was released by Microsoft in 2000 for indoor Wi-Fi positioning [ 1 , 2 ].…”

“…Use Wi-Fi signal strength and APs' locations to generate four kinds of the area-partitioned methods, which are 2, 4, 6, and 12, to enhance the accuracy of geomagnetic field strength positioning with KNN algorithm. (2). Using the signal processing technology of the rotation matrix, the signal coordinates of the geomagnetic field strength measured by the smartphone can be cost-effectively normalized.…”