Group Targets Tracking Algorithm Based on Box Particle Filter
Zhen Li · Acta Automatica Sinica · 2015
Particle filter(PF) algorithm is often used to solve the nonlinear filtering problem for point measurements in the existing group targets tracking algorithms. However, the traditional PF algorithm cannot be directly applied to the case where the point measurements should be converted to interval measurements when the measurements are affected by biases or bounds errors of unknown distributions. Therefore, this work presents an improved PF algorithm based on the generalized likelihood(GL) weighting method. The GL-PF algorithm uses the definite integral solution of generalized likelihood function to calculate the weighting of particles under interval measurements. For the sake of reducing computational burden, this work presents another group tracking algorithm based on box particle filter(BoxPF). Firstly, the rectangular box particles are sampled in the target state space. Then, the ratio between the contracted and the predicted box particle volumes is used to calculate the weighting of particles based on the interval analysis and constraints propagation method. Lastly, the group structure is estimated based on the estimation results of group target state and the evolving network model. Computer simulations show that compared with the GL-PF algorithm, the Box-PF algorithm can achieve a greater computational efficiency and reduce the peak error of the estimation results.