To adapt the character of evolving over time and real-time of sensor data in location tracing service based on RFID

we present an adaptive evolving particle filtering algorithm-AMUR(an adaptive measuring algorithm of underlying uncertainty for RFID data).AMUR adaptively changes the number of samples on the basis of K-L distance

introduces an improved PSO (particle swarm optimization) method to enhance the efficiency of resampling phase of conventional particle filter(SIRPF).Meanwhile

to detect the most optimal samples among candidate sample set

AMUR defines a fitness function based on CWA(conventional weighted aggregation) for PSO which balances the importance between priori density and likelihood densitys.It provides a reliable measure of confidence for initial tuple in the probability RFID database.Experimental comparison of current algorithms shows

AMUR outpreforms current methods in terms of measurement of underlying uncertainties over RFID data

particle degradation and particle depletion.