Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recognition
Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recognition
复制标题
多目标跟踪/识别中通过跳跃扩散过程的条件均值估计
DOI：
10.1109/78.482117
复制
发表时间：
1995
影响因子：
5.4
通讯作者：
U. Grenander
中科院分区：
文献类型：
--
作者：
M. Miller;Anuj Srivastava;U. Grenander
提出了一种新的算法，用于目标位置、方向和类型函数的条件均值估计的生成，以及未知数量目标和未知类型目标的跟踪。采用贝叶斯方法，将窄带传感器阵列流形模型与高分辨率成像模型相结合，结合基于飞机动力学的先验，定义了跟踪/目标参数空间的后验测度。利用控制刚体动力学的牛顿力方程，形成飞机运动的先验密度。使用基于跳跃扩散过程的随机抽样算法生成条件均值估计，在后验测量下经验地生成这些随机目标位置、方向和类型函数的MMSE估计。最后给出了该算法在网络化Silicon Graphics工作站和DECmpp/MasPar并行机上的目标跟踪和识别结果。
A new algorithm is presented for generating the conditional mean estimates of functions of target positions, orientations and type in recognition, and tracking of an unknown number of targets and target types. Taking a Bayesian approach, a posterior measure is defined on the tracking/target parameter space by combining a narrowband sensor array manifold model with a high resolution imaging model, and a prior based on airplane dynamics. The Newtonian force equations governing rigid body dynamics are utilized to form the prior density on airplane motion. The conditional mean estimates are generated using a random sampling algorithm based on jump-diffusion processes for empirically generating MMSE estimates of functions of these random target positions, orientations, and type under the posterior measure. Results are presented on target tracking and identification from an implementation of the algorithm on a networked Silicon Graphics workstation and DECmpp/MasPar parallel machine.