NLOS Mitigation Algorithm Implementing In Cellular Network And RFID Indoor Location System

The proliferation of mobile computing devices, wireless technologies and the Internet has fostered a growing interest in location-aware systems and services. In many applications there is a need to know the location of objects, for example: wireless navigation, management in cities, transportation, government mobile work and so on. Non-line-of-sight (NLOS) error is the most common and also a major source of errors in wireless location system. NLOS mitigation algorithms in outdoor and indoor location systems have been presented. Under different NLOS scenarios, simulations result in high accuracy.Celluar network localization technology of outdoor location systems has been analyzed in our paper. It presents an approach to ameliorate the effect of the NLOS using Time Difference of Arrival (TDOA) with more than minimum number of base stations (BSs). This algorithm makes use of the redundant TDOA measurements to formulate a problem as a test of hypothesis in order to get a hard decision to discard the NLOS-BSs. Simulations show that the proposed algorithm can completely discard the NLOS-BSs when NLOS error is higher than the measurement noise.For the positioning of objects located inside buildings, a low cost Radio Frequency Identification (RFID) indoor navigation scheme is proposed in our paper by deploying RFID tags and implementing a new localization algorithm which is improved from Monte Carlo algorithm to make person holding RFID reader know where he is in real time. In this algorithm, the person's state space is represented by maintaining a set of random samples. And a localization method that can represent arbitrary distributions is proposed by using a sampling-based representation. Comparison between proposed algorithm and Least Square (LS) algorithm in TOA indicated that the positioning errors of the proposed algorithm are lower than LS under the NLOS scenarios. For the case that LS is not available when less than three tags deployed, however, the proposed algorithm can keep track of person.