Several computing techniques and sensor technologies have been proposed in the last two decades to provide indoor localization systems for personal in-home staying. This field known as personal localization system (PLS) is quite challenging due to some faulty sensor measurements as well as people random movements. This paper describes the ongoing work of in-home PLS using data fusion between an inertial measurement unit (IMU) and a load pressure sensing floor. In addition, a fault-tolerant fusion method is proposed using a purely informational formalism: information filter on the one hand, and information theory tools on the other hand. Residues based on the Kullback-Leibler divergence are used. Using an appropriate threshold, these residues lead to the detection and exclusion of sensors faults. The experimental results show that the proposed approach is very promising, a fault-tolerant PLS that localizes and tracks people in their home with a high accuracy.