Indoor localization is very critical for medical care applications, e.g., the patient localization or tracking inside the building of the hospital. Traditional Radio Frequency Identification (RFID) technologies are very popular in this area since their cost is very low. In such technologies, each tag acts as the transmitter and the Radio Signal Strength Indicator (RSSI) information is measured from the readers. However, RSSI information suffers severely from the mult i-path phenomenon. As a result, if in a very large area, the localization accuracy will be affected seriously. In order to solve this problem, we introduce Wireless Sensor Networks (WSNs) with only a few nodes, each of which acts as both transmitter and receiver. In such networks, the change of signal strength (referred as dynamic of RSSI) is leveraged to select a cluster of reference tags as candidates. Then the final target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Thus, the localization accuracy and scalability are able to be improved. We proposed two algorithms, SA-LANDMARC, and COCKTAIL. Experiments show that the localization accuracy of the two algorithms can reach 0.7m and 0.45m, respectively. Compared to most traditional Radio Frequency (RF)-based approaches, the localization accuracy is improved at least 50%.
- radio frequency
- support vector regression