中文摘要：

针对传统方法难以准确估计扩展目标形状的问题,提出一种新的基于高斯曲面拟合的量测模型和基于高斯曲面特征矩阵的形状估计算法。首先,建立能反映目标真实形状的结构点,并产生多个高斯曲面,通过曲面叠加形成任意形状的量测空间分布模型;然后,根据高斯曲面拟合原理,用矩阵表示该拟合曲面主要区域的分布特征,并通过映射方程建立矩阵坐标与笛卡尔坐标的映射关系;最后,通过贝叶斯滤波体系更新拟合矩阵。与现有算法相比,本文算法不需要准确预设目标形状,在量测噪声较大的环境下,可以自适应的拟合目标真实形状。并且,在不需要预设目标形状方程的情况下,可以估计包括空心形状在内的任意不规则目标形状。实验结果表明,在目标初始形状参数不准确的情况下,本文算法正确估计了飞机形状、空心形状和集群目标形状,并且具有较好的扩展目标形状估计性能和较强的鲁棒性。

英文摘要：

Taking account of the difficulty of shape estimation for the extended targets,a new measurement model and a shape estimation approach based on Gaussian surface feature matrix are proposed in this paper. First,the structural points are establiched,which are able to reflect the true shape of the extend target,and these points are used to construct some Gaussian surfaces. Then these Gaussian surfaces are fitted to yield the suitable measurement spatial distribution models. Furthermore, the feature of measurement spatial distribution is described by using a matrix with Gaussian surface fitting approach, and a mapping relationship between the matrix coordinates and the Cartesian coordinates is established by a suitable mapping function. Finally, the Gaussian surface feature matrix is updated by Bayesian filtering method. Compared with the conventional algorithms, the proposed algorithm can estimate the true shape of extended target in the high-noisy environment, even the preset shape is inaccurate. Moreover,it can be used to estimate any irregular shape, even the hollow shapes, without knowing the preset shape information. Simulation results show that the plane shapes, hollow shapes and group targets are estimated accurately in case of the preset shape parameters are inaccurate, which demonstrates that the proposed algorithm has good performance for shape estimation of any extended target with a strong robustness.