基于目标出生强度在线估计的多目标跟踪算法

针对多目标跟踪中未知的目标出生强度, 提出了基于Dirichlet分布的目标出生强度在线估计算法, 来改进概率假设密度滤波器在多目标跟踪中的性能. 算法采用有限混合模型来描述未知目标出生强度, 使用仅依赖于混合权重的负指数Dirichlet分布作为混合模型参数的先验分布. 利用拉格朗日乘子法推导了混合权重在极大后验意义下的在线估计公式; 混合权重在线估计过程利用了负指数Dirichlet分布的不稳定性, 驱使与目标出生数据不相关分量的消亡. 以随机近似过程为分量均值和方差的在线估计策略, 推导了基于缺失数据的分量均值与方差的在线估计公式. 在无法获得初始步出生目标先验分布的约束下, 提出了在混合模型上增加均匀分量的初始化方法. 以当前时刻的多目标状态估计值为出发点, 提出了利用概率假设密度滤波器消弱杂波影响的出生目标数据获取方法. 仿真结果表明, 提出的目标出生强度在线估计算法改进了概率假设密度滤波器在多目标跟踪中的性能.

Multiple Target Tracking Algorithm Based on Online Estimation of Target Birth Intensity

As far as the unknown target birth intensity in multiple target tracking is concerned, an online estimation algorithm of target birth intensity is proposed to improve the performance of probability hypothesis density filter in multiple target tracking. The finite mixture model is adopted to model the unknown intensity of target birth. Dirichlet distribution with negative exponent parameters, which only depends on the mixing weights, is used as the prior distribution of parameters in the mixture model. The online estimation formulation of mixing weight is derived by Lagrange multiplier in the sense of maximum a posterior. The instability of Dirichlet distribution with negative exponent parameters is applied in driving the components irrelevant with birth targets to extinction during the online estimation procedure of mixing weights. Stochastic approximation procedure is regarded as the strategy of online estimation of component mean and covariance. The online estimation formulations of component mean and covariance are derived based on missing data. An initialization method is developed by adding a uniform distribution into the mixing model, under the constraint that no prior distribution of target birth is obtained in initialization. From the standpoint of current estimates of multiple target states, the method of achieving the data of birth targets, which makes full use of the ability of probability hypothesis density filter to reduce the effect of clutter, is presented. Simulation results show that the proposed online estimation algorithm of target birth intensity can improve the performance of probability hypothesis density filter in multiple target tracking.