Comparing centralized Kalman filter schemes for indoor positioning in wireless sensor network

Sensor devices suffer severe interference due to multi path effect, non line of sight (NLOS) and variations of the wireless propagation environment in indoor positioning systems. These interferences lead to measurement error during sensor measurement. Kalman filter (KF) and extended Kalman filter (EKF) have been widely used in tracking systems to reduce measurement noise. However, KF and EKF assume the measurement noise follows normal distribution, and the real noise distribution should be based on experimental statistical results. Besides fluctuating wireless condition makes the system unstable in indoor environments. We analyze the time of flight (TOF) measurement statistic model in experiments and design KF and EKF models for indoor positioning system according to the statistic model. We introduce our system architecture for wireless sensor networks (WSN) to overcome KF's drawbacks, which divides the positioning system into three components: measurement, pre-processing and data-processing. Measurement component measures the range based on TOF method. We developed a voting filter (VF) and an averaging filter (AF) in preprocessing to reduce measurement noise for later processing. During data-processing, Kalman filter and extended Kalman filter are used to track the positions. We also implement another scheme, low pass filter (LPF) with KF or EKF, to estimate the positions with the knowledge of geographic information. Three realistic experiments are set up using the sensor equipment nanoPAN 5375 to evaluate these methods. Comparing the experimental results, low pass filter with EKF is most suitable for indoor positioning.