当前以自主驾驶车为代表的无人自主运动体的定位能力是实现其导航建图、环境理解和决策控制的关键。在大雪大雾、非结构化道路、电磁干扰等不确定环境下，传感器观测易受干扰甚至丢失，从而恶化无人自主运动体的定位能力。利用V2X通讯技术采集多传感器信息加以融合，实现自适应噪声抑制是当前一种有效方法。本项目针对动态不确定环境协作定位存在的系统和观测噪声特性未知、定位对异常值敏感以及通讯负担重的问题，分别重点研究分布式噪声自适应估计、异常值检测以及能效优先的传感器调度方法加以解决，从而提高协作定位的精度、鲁棒性和通讯能力。本项目进一步将所提方法在无人驾驶车上进行量化验证和测试。

Currently the localization ability of unmanned autonomous autonomous driving vehicles represented by autonomous car is the key technology of navigation and mapping, environment understanding and decision-making and control. The measurements of sensors are easy to interfere and even lost under uncertain environment, such as big snow and big frog, unstructured road and electromagnetic interference, and jeopardize the localization performance of unmanned autonomous vehicles. Multi-sensor fusion by utlizing V2X communication techonology is one effective approach. Aiming at unknown system and measurement noise statistics, sensitiveness to outliers and high communication burden problems, this project study the distributed noise adaptive estimation algorithm, outlier detection and energy-efficient sensor scheduling methods, which increases the accuracy, robustness and communication ability. The proposed algorithms are planned to be tested and verified in autonomous car.