Research On Target Sensing And Recognition Technology Based On RFID

With the rapid development of the Internet of Things(IoT)technology,smart applications have entered thousands of households.Smart life has gradually become a lifestyle pursued by people.Human-computer interaction,intelligent touch,and security control are widely used in all aspects of daily life.With the outbreak of the epidemic in 2020,it has become a general consensus to reduce contact as much as possible,which has promoted the development of non-contact sensing and identification technology.At the same time,travel safety under the epidemic is also the focus of attention.At present,there are still many airport stations adopting traditional methods,where they verify the safety of the liquid by tasting of the liquid,which has a great potential safety hazard.Radio Frequency Identification(RFID)equipment is widely used due to its low cost,small size,no contact,strong anti-pollution ability,and reusability.However,RFID identification also has some problems.For example,data collection is susceptible to external interference,resulting in inaccurate data collection and ultimately poor perception and recognition performance.Therefore,robust RFID identification system need to be studied.This thesis mainly focuses on the research on behavior recognition and liquid recognition in smart life.First,RFID-based human motion recognition is designed and implemented.We use commercial RFID equipment to collect the human behavior data according to the tag array,including phase and received signal strength indication.On this basis,data preprocessing operations(phase unwrapping,data smoothing and filtering,normalization)are designed to process the original collected data to improve the accuracy of the data.Then the preprocessed data is segmented to extract motion-related segments.Finally,the fusion features of phase and signal reception intensity are extracted through a multimodal convolutional neural network to identify human actions.The experimental results show that the designed system can achieve a recognition accuracy of more than97% of human actions,which can basically meet the requirements of human-computer interaction.For non-contact liquid identification,this thesis designs a non-invasive liquid identification system based on RFID.Firstly,the corresponding phase data is obtained by putting the liquid into the experimental platform,and the noise and abnormal data are removed by moving average filtering.On this basis,different weight indices are given to the tags based on the identification performance of the tag array for different liquids.Then we extract the corresponding liquid characteristics from consequences of different liquids’ absorption towards signals.Finally,the extracted features are sent to the support vector machine to better realize the liquid identification.The experimental results show that the average recognition accuracy of the designed system corresponding to the liquid reaches 96%,which verifies the feasibility of the system.