Abstract: According to the communication instability and poor localization accuracy in indoor wireless sensor networks, a dynamic localization of mobile robot was proposed which dynamically chooses neighbor beacon nodes and establish grid space based on boundary of beacon node location in real time. This method applies the receive signal strength index for distance measurement. Furthermore, the range-based improved approximate point in triangle test (APIT) was used to realize the localization. Finally, the localization error-correct is implemented by the classical Kalman filter. The method is adaptable for the communication instability of indoor wireless sensor networks, the Kalman filter provides optimal data. Experiments show that the accuracy and adaptability of the dynamic localization of the mobile robot are better than Kalman filter grid-based improved maximum likelihood estimation algorithm.

Key words: wireless sensor networks, dynamic localization, Kalman filter algorithm, receive signal strength index