A Constrained Filter for Indoor Localization Based on Multi-filter Fusion and NLOS Identification

Abstract

Wireless sensor networks (WSN) are increasingly important in various fields. One of the main technologies of WSN is mobile node localization. And how to achieve precise node positioning in non-line-of-sight (NLOS) environments is a main challenge. In this paper, we propose a constrained filter NI-HCF based on NLOS identification and multi-filter fusion. We first process the measurements using Extended H-infinity filter (EHF) to obtain the predicted state vector. Subsequently, NLOS identification is conducted on measurements via residual analysis between predicted state vectors and measurements, categorizing them into LOS and NLOS groups. We send the classified measured values into Constrained Square-Root Unscented Kalman Filter (CSRUKF). This means that the measured value of LOS is processed with Square Root Unscented Kalman Filter (SRUKF). And NLOS measurements are utilized to determine a feasible domain. The sigma points outside the feasible domain can be projected into the feasible domain by solving the Semi-Definite Programming (SDP) problem. The final position estimate is obtained by the projected sigma points. Simulations and experimental tests indicate that the proposed algorithm achieves improved positioning precision in NLOS conditions and has better results compared with MPDA, IMM-EKF, RIMM, EKF and REKF.