Based On The Lunar Rover Positioning Its Filtering Algorithm For Wireless Sensor Networks

The unknown environment of the moon in outer space requires that moon rover localize itself accurately, and the localization method should be robust. This is the key problem in developing the moon rover.A distributed moon rover localization system framework is introduced in this paper. The framework is composed of central computer, moon rover, and wireless sensor network. This system can not only provide robust localization service regardless of the weather, but it can also minimize the computing load when rover moves. We also analyze the ranging principles used in wireless sensor network. The localization system will localize sensors and moon rover in the network according to these principles.On basis of this framework, we propose PAKF (Particle - Extended Kalman Filter), a novel localization algorithm. PAKF has two modules: it first uses particle filter to acquire estimates of rover's position; then it randomly samples estimates as the initial estimate of extended Kalman filtering. When sensor silence and other emergency events happen, it will switch between the two filtering modules. The experiment results illustrate that PAKF is a robust algorithm with high accuracy and computing efficiency.