Convergence Analysis of the Gaussian Mixture Extended-target Probability Hypothesis Density Filter
高斯混合扩展目标概率假设密度滤波器的收敛性分析

The convergence of the Gaussian mixture extended-target probability hypothesis density (GM-EPHD) filter is studied. Under the assumptions that the clutter intensity is known a priori and the expected number of measurements arising from an extended target is continuous and bounded, this paper proves that the GM-EPHD filter converges uniformly to the true EPHD filter as the number of GM terms tends to infinity. In addition, this paper also proves that the extended Kalman (EK) filter approximation of the algorithm in weak nonlinear condition, which is called EK-GM-EPHD filter, converges uniformly to the true EPHD filter as the covariance of each GM term tends to zero. The purpose of this paper is to theoretically present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which they satisfy uniform convergence.