CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment

Time-of-arrival (TOA)-based indoor geolocation suffer from huge distance measurement error caused by multipath and nonline-of-sight (NLOS) conditions. In this paper, we presented a new distance mitigation algorithm based on channel classification and Kalman filter to enhanced TOA performance in multipath and NLOS indoor extreme environment. This algorithm could significantly reduce the ranging error caused by the extreme channel condition in indoor area. We compared the performance of our algorithm with the traditional TOA distance mitigation algorithms, such as Kalman filter, biased Kalman filter, binary hypothesis testing, and ANN, using a commercially available TOA-based geolocation system in typical indoor and underground environments. Results show the performance of our algorithm is much superior to the others.