Direct Self-trajectory Determination Based on Array Sensing and Evolutionary Particle Filter

The self-trajectory determination is an effective method to continuously track the target’s motion position. However, the traditional methods are relied on auxiliary parameters, which cause the problems of information loss and error accumulation. In order to handle these problems, we propose a direct self-trajectory determination algorithm based on evolutionary particle filter (EPF) for unmanned aerial vehicle (UAV) mounted with an antenna array. Firstly, the array sensing data are eigenvalue decomposed to obtain the observation function and the state transition function is constructed with the process control parameters. Then, particles are distributed randomly around the position of UAV and their weighted values are estimated using the likelihood function derived from the observation function. The resampling algorithm is adopted to select particles with larger values and the position of UAV is determined from these reserved particles. To overcome the decrease in particle diversity, the reserved particles get more dense after mutation and the new particle group for next moment is obtained with the state transition function. In this way, the self-trajectory is iteratively refined with EPF. Finally, the simulation test and the practical experiment based on UAV are conducted to verify that the proposed algorithm is more accurate and more stable when tracking real-time positions of UAV.