An Particle Filter Algorithm for the Low Measuring Noise System

Aiming at the problem that conventional particles filter algorithm uses a prior density function to sample particles,thereby the particles distribution should rely on the dynamic model to reduce the estimation precision.A improved particles correlated pre-sampling likelihood sampling particles filter algorithm is proposed,which based on the likelihood sampling particles filter of observation likelihood function sampling.Under the condition of low measurement noise,the likelihood sampling can obtain particles which are closer to the true posterior distribution,so the estimation precision is expected to be improved.The correlated pre-sampling procedure calculates the transition-probability of adjacent time and abandons the particles with lower probability to improve particles efficiency.By this way,estimation accuracy is ensured and the amount of required particles is decreased significantly.The importance density function is designed and the weight-value recursive formula is deduced.The proposed algorithm is analysised by the Monte Carlo simulation,and it is also applied to the problem of target-tracking in the hybrid coordination.