A Passive Localization Algorithm That Integrates RSSI and PDOA Location Observations in a Mixed Scene

Abstract

Passive radio frequency identification (RFID) localization is attractive for low-cost indoor positioning, but mixed line-of-sight (LOS) and non-line-of-sight (NLOS) propagation causes both RSSI fluctuations and carrier-phase ambiguity. This study proposes a coarse-to-fine PRTFC–SD-PDOA framework in which an RSSI fingerprint likelihood defines the admissible decision domain, candidate-wise phase ambiguity is resolved inside that domain, and normalized RSSI and phase costs are jointly minimized. Dominant-reflection compensation is used when the reflecting surface is known, and temporal filtering is applied to repeated observations. In independently repeated simulations, the joint estimator achieved RMSEs of 0.098 m in LOS and 0.250 m in the mixed scene. In a nine-position R420 experiment, the particle-filtered estimator achieved mean point-wise RMSEs of 0.122 m in LOS and 0.457 m in the mixed scene; the corresponding Kalman-filtered results were 0.118 and 0.424 m. Thus, both filters reduced the variability of the unfiltered joint estimates, while the Kalman filter was slightly more accurate for the stationary, approximately Gaussian measurement sequences considered here.