Maximum likelihood estimators for rigid body localization using DOA measurement
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摘要: 针对刚体定位同时估计目标的位置和姿态信息的问题，研究了在三维空间中利用单基站进行刚体定位（RBL）的框架.该框架采用单个基站对安装在刚性目标表面的小规模无线传感器信号的波达方向进行测量，并将其与传感器拓扑信息融合，提出了两种用于RBL的最大似然估计算子.利用改进的高斯-牛顿算法对旋转矩阵和平移向量的最大似然估计算子进行优化估计，解算出了目标物体的三维位置和姿态.仿真结果表明，文中提出的最大似然算子可以接近理论克拉美罗下限，并且在收敛成功率和运算成本方面具有较为出色的性能.Abstract: Rigid body localization (RBL) not only estimates the position of the target, but also obtains the attitude information of the target. The RBL framework of single base station is studied in three-dimensional space. This framework uses a single base station to measure the direction of arrival (DOA) of signal from small-scale wireless sensor network signal installed on the rigid target surface, and then fuses the DOA measurement with the network topology information, and finally proposes two maximum likelihood estimators (MLE) for RBL purpose. The improved Gauss Newton algorithm is adopted to optimize the MLEs of rotation matrix and translation vector and the three-dimensional position and attitude of the object are estimated. The simulation results show that the proposed MLEs can approach the theoretical Cramer Rao Lower Bound, and have better performance with respect to convergence success rate and computation cost.
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