【摘要】 粒子概率假设密度滤波(SMC-PHDF)在进行粒子更新时需要知道测量噪声的概率分布以计算似然函数,这使得SMC-PHDF依赖于测量噪声的概率模型。针对这一点不足,提出一种未知测量噪声分布下的多目标跟踪算法——基于风险评估的概率假设密度滤波(RE-PHDF)。该算法在SMC-PHDF进行概率假设密度(PHD)粒子更新时采用风险函数计算每个PHD粒子的风险值,并通过一个风险评估函数评估每个PHD粒子,然后用评估后的结果更新粒子的权值。由于粒子更新时避免了在多维测量空间中计算似然函数,算法不仅不依赖于测量噪声的概率分布,还可以节省大量计算时间。仿真结果表明:和SMC-PHDF相比,RE-PHDF在未知的复杂测量噪声环境下具有更高的鲁棒性和稳定性;同时,在两种算法跟踪精度接近的情况下,所提算法节省了50%的运行时间。更多还原

【Abstract】 When updating particles,aparticle probability hypothesis density filter(SMC-PHDF)requires the probabilistic distribution of measurement noise to calculate the likelihood function,which makes it rely excessively on the probabilistic model of measurement noise.To overcome this drawback,a new multiple target tracking algorithm under unknown probabilistic distribution of measurement noise is proposed,namely,a risk evaluation-based probability hypothesis density filter(RE-PHDF).When SMC-PHDF updates probability hypothesis density(PHD)particles,the algorithm computes the risk of each particle using a risk function,and evaluates each particle by a risk evaluation function,then updates the particle weights by means of the evaluated results.Avoiding thus the likelihood function calculation in multi-dimensional measurement space,the algorithm does not depend on the probabilistic distribution of measurement noise and can save much computing time.The simulation results show that RE-PHDF possesses higher robustness and stability under unknown and complicated measurement noise environment in comparison with SMC-PHDF.In addition,the new algorithm can save up to 50%execution time while possessing similar accuracy as SMC-PHDF.更多还原