An Improved Particle Filter for UAV Passive Tracking Based on RSS
Jun Gao, Hui Zhao · 2020
The increasing demand for unmanned aerial vehicle (UAV) supervision urgently requires accurate tracking algorithms as technical support. Particle filter (PF) is widely used for solving tracking problems for its fewer restrictions than Kalman filter (KF), but it needs to be optimized in received signal strength (RSS) based passive positioning scenario where the power measurement influenced by noise and antenna pattern. This paper proposes an improved PF algorithm. Firstly, probabilistic data association (PDA) is used to preprocess the measurement data to determine and eliminate outlier data. Secondly, the preliminary measurement data from offline experiments are used to modify the observation likelihood function to improve the weight distribution of the particles, so as to weaken the influence from non-ideal antenna pattern. In actual experiments, compared with the traditional PF algorithm, the results measured by USRP platform show that the proposed algorithm reduces the position RMSE from 5.55 m to 2.87 m when the UAV is moving at low speed. Owing to positioning accuracy improving, the velocity RMSE is also reduced by 5.1%.