Abstract
To improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.
Highlights
Science and technology museums are public science education institutions with exhibition education as its main function
There are a variety of indoor positioning technologies [1], which are broadly divided into the technologies of Wi-Fi positioning [2], radio frequency identification (RFID) positioning [3], ZigBee positioning [4], Bluetooth positioning [5], ultra-wide-band (UWB)
The main work of this paper is to study the indoor positioning method suitable for small- and medium-sized science and technology museums
Summary
Science and technology museums are public science education institutions with exhibition education as its main function. In a Wi-Fi wireless network environment, a Wi-Fi signal access point (i.e., an AP) periodically broadcasts a beacon signal frame, and a mobile terminal device with a Wi-Fi access function module does not establish a connection with any AP, it can receive at least three important parameter indicators of the transmitting source AP from the AP broadcast signal frame: the Wi-Fi AP’s MAC address (BSSID), the Wi-Fi AP name (SSID), and the Wi-Fi signal strength indication (RSSI) These three indicators provide strong support for the model for positioning based on location fingerprint feature matching. The terminal-based method achieves accurate signal strength and high positioning accuracy It takes requires considerable time, and each new positioning area has to be re-acquired and built a location fingerprint database built, this method is adopted in this paper. 2.3.2 Online positioning stage In the matching algorithm used in the positioning phase, the KNN algorithm used in this paper uses some metrics to distinguish the similarity between two different fingerprints, called the dissimilarity
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