Radio Frequency Identification (RFID)-based fingerprint indoor positioning and tracking technology is one of the key technologies in the study of wireless sensor network, and has been widely used in noisy environment. However, due to the time and space fluctuation in Received Signal Strength Indicator (RSSI)of RFID, indoor positioning accuracy is not satisfactory. In this work, we present a Multi-Direction Weight Position Kalman Filter (MDWPKF) according to the spacial feature of RSSI. This algorithm combines the Multi-Direction data collection method, with Standard Kalman Filter and fingerprint matching algorithm to achieve the signal fluctuation reduction, noise removal and 2D fingerprint mapping. At the same time, the Improved Position Kalman Filter (IPKF) in our proposed MDWPKF takes the advantages of Gaussian weight computation and velocity estimator to refine the position and velocity estimates. Compared with traditional PKF, the MDWPKF improves the positioning accuracy by 17.7%, and the velocity accuracy by 10.2%. Compared with Fingerprint Kalman Filter (FKF), the MDWPKF can be used for the tracking of both moving target (including position and velocity estimates) and stationary object.

Recently, person identification has been a prerequisite in many applications of the Internet of Things. As a new biometric identification technology, gait recognition has a wide application prospect with the advantages of long-distance recognition and difficulty to forge. However, the existing gait recognition methods have some problems, such as complex algorithm calculation, high user participation, and large equipment overhead. In this paper, we propose RF-Gait, a method that identifies a person through unobtrusive gait perception with COTS RFID. The key insight is that wireless signal fluctuation can be exploited to distinguish each person’s unique gait behavior. To this end, we first collect and preprocess the gait-induced data composed of multiple RFID tags. Furthermore, multivariate variational mode decomposition is utilized to extract the intrinsic features in the spatial multichannels cooperatively. By developing a support vector machine model, we identify a person via the intrinsic walking pattern. Finally, extensive experiments show that our method can identify a person with an average accuracy of 96.3% from a group of twenty persons in a complex indoor environment.

Nowadays RFID indoor targets positioning results display methods are simplified. The positioning results visual effect is also unsatisfied. To solve this problem, we design and realize a comprehensive method to implement RFID indoor localization algorithms in a geographic information system. In our method, the RFID indoor targets positioning results can display in the real geographic environment, which can improve the application value of the results. First, we establish the geographic environment using geographic information system in order to combine RFID indoor positioning results with the real environment. Then we design a distributed system architecture to filter the useless data and implement the positioning algorithm. In this way, indoor targets real-time positioning and the results visualization display are realized. And the positioning results can also be combined with the real geographic information environment. System test results indicate that this method achieves ideal indoor positioning data display effect under well indoor-positioning precision.

The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments, we found that the performance score of the TDBF algorithm was 5.2, while the Time Bloom Filter (TBF) score was only 0.03, which indicates that the TDBF algorithm can achieve a lower false negative rate, lower false positive rate, and higher data compression rate. Furthermore, in a dynamic scenario, the TDBF algorithm can filter out valid data according to the actual scenario requirements.