非线性无偏最优估计器及高精度跟踪算法研究

项目名称： 非线性无偏最优估计器及高精度跟踪算法研究

项目编号： No.61271317

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 雷明

作者单位： 上海交通大学

项目金额： 75万元

中文摘要： 在利用最小方差线性无偏估计（BLUE）准则下的Kalman类非线性滤波器，进行运动目标跟踪研究中，由于传感器量测的非线性，以及目标运动模式的高机动性，高精度非线性滤波器一直是机动跟踪领域重点研究之问题。首先，从BLUE准则的线性假设着手，本课题将建立非线性无偏最优估计准则，并在此准则下给出高精度非线性最优滤波的通用理论框架，进而给出针对高维/非高斯情况的处理技术。其次，将探索非线性最优滤波框架下，实现传感器位置/Doppler量测联合优化利用机制，并给出针对该联合利用问题的高精度专有滤波算法；最后，为了进一步对已有的不同非线性滤波算法在典型参数范围内进行精度和可靠性评价，有必要给出在线计算Crammer-Rao下界的理论框架，并探索不同滤波算法的Crammer-Rao理论下界，以指导算法性能分析和实际应用。

中文关键词： 非线性滤波；无偏最优估计；非线性量测；高维/非线性/非高斯系统；可靠性分析评价

英文摘要： In the investigation of maneuvering target tracking that based on the state-space-described Kalman-like nonlinear filters, due to some negative issues, such as the linear regression presented by the criterion of linear minimum variance unbias estimation (BLUE), and the nonlinearity of the sensor-obtained observations, as well as the high flexibility of maneuvering mode, certain improvement of the nonlinear estimator wih high accuracy is frequently explored and the involved topic is regarded as an important chellenge by the tracking comnunity. Firstly, to start from the point of the linear assumption in the BLUE, in this planed project we will explore the criterion of nonlinear & unbiased optimality estimation with a general viewpoint, then accordingly a general theoretical framework of high accuracy nonlinear & optimal estimation will be formulated, moreover, we are also obliged to investigate the distinguished strategies to deal the high dimensional/nonlinear/non-Gaussian problems, so as to efficiently enlarge the application scope of the new estimator. Secondly, equiped with the proposed nonlinear & optimal estimation theory, we will focus on the optimal utilization of both the position observation and the Doppler observation in a jointed manner, meanwhile, to suggest a well-designed filtering algorith

英文关键词： Nonlinear Filtering；Unbiased Optimality Estimation；Nonlinear Observations；High Dimensional/Nonlinear/Non-Gaussian System；Reliability Analysis and Evalution