Real-time locating system (RTLS) based on UWB radio technology can be used to track people performing every-day activities. However, the quality of obtained data is relatively low and, therefore it is difficult to perform a reliable advanced analysis of human motion based on it. The paper analyses the noise of RTLS measurements and suggests filtering methods that reduce the impact of the noise on the accuracy of activity recognition. The methods are based on the statistical properties of the noise and human anatomy and motion limitations. First, a rule based method for inserting missing measurement values is suggested and compared with simple insertion of the last known value. Second, an adaptive low-pass filter that reduces impulsive noise is suggested and compared with median filter. Third, a filter that ensures human motion constraints are meet is suggested. In addition, an implementation of Kalman filter that can be used to estimate the missing values, estimate the velocity of movement from recorded locations, and for smooth the signal is described. The advantages and limitations of the suggested filtering approach are demonstrated on synthetic and real data. Finally, influence of each phase of the suggested filtering chain on the accuracy of activity recognition is analysed.