An Indoor Hybrid Localization Approach Based on Signal Propagation Model and Fingerprinting

RFID-based location awareness is becoming the most important issue in many fields in recent years, such as ubiquitous computing, mobile computing. In indoor localization systems RSSI-based methods are usually used in office buildings. However, RSSI is susceptible to external influences, and performances unstably due to the environmental factors affecting signal propagation. In this paper, we propose a new hybrid localization method for tracking moving object using the two typical methods which are signal propagation model and fingerprinting. According to a threshold which is defined as an effective working distance of signal propagation model between target tag and RFID reader, we choose the different localization algorithm to estimate the location of moving object. The threshold is obtained by calculating the slop of signal attenuation curve. If the distance is within the effective reading range of RFID reader, we revise signal propagation model by maximum likelihood estimation and use it to calculate the object position by minimum cumulative error. Otherwise, the fingerprinting location method is used in the external area, and the particle filter is also used as the core algorithm. The experimental results show that our method not only reduces the computation complexity but also ensures the accuracy in large indoor area.