Maximum Likelihood Estimation for Multiple Camera Target Tracking on Grassmann Tangent Subspace
提出一种基于流形切子空间的似然模型，将传统欧氏估计转化为流形上的估计，并给出两种最大似然估计方法，用于多视角系统中的目标定位。
In this paper, we introduce a likelihood model for tracking the location of object in multiple view systems. Our proposed model transforms conventional nonlinear Euclidean estimation model to an estimation model based on the manifold tangent subspace. In this paper, we show that by decomposition of input noise into two parts and description of model by exponential map, real observations in the Euclidean geometry can be transformed to the manifold tangent subspace. Moreover, by obtained tangent subspace likelihood function, we propose two iterative and noniterative maximum likelihood estimation approaches which numerical results show their good performance.