Research On Indoor Location Technology Based On RSS Fingerprinting Databases

With the rapid development of information technology in recent years and the rapid growth of the performance of Internet of Things devices,Location awareness is playing an increasingly important role because of the proliferation of location-aware applications.As a result,wireless location technology has become one of the most active and fastest-growing areas of communication.However,due to the special characteristics of the indoor environment,the attenuation of signal transmission,the fading of multi-path,the existing of non-line-of-sight,the research method of indoor location technology is totally different from outdoor location technology and it becomes a new and difficult research field.Indoor positioning system based on RSS fingerprint database is a solution,which provides a set of signal transmission access point.Fingerprint method is divided into two stages: offline acquisition and online operation.The location coordinates and the corresponding signal strengths for every base station are collected by the device and stored in the RSS fingerprint database in offline acquisition stage.The online operation phase means that the positioning system compares the currently measured signal to the previously collected signal strength and derives the location from the result of the comparison.The RSS fingerprint database can be initialized and different locations will have their specific RSS values when an indoor positioning system is built.However these RSS fingerprints may become outdated when there are unanticipated environmental changes.Re-measuring RSS fingerprints for all locations to maintain an up-to-date RSS database incurs high operational cost,which is impractical in dynamically changed environment,especially in highly congested areas.In this study,we use BLE4.0 as the signaling access point and propose a dynamic estimation resampling method for certain locations chosen from a set of remeasurement fingerprinting databases.Our proposed method adaptively applies different,newly updated and offline fingerprinting points according to the temporal and spatial strength of the location.To achieve accuracy within a simulated area,the proposed method requires approximately 3% of the feedback to attain a double correctness probability comparable to similar methods;in the actual environment,our proposed method can obtain 1.5 m accuracy errors,which is a 3.6×16.8m restaurant with a highly crowded flow.