目前应用广泛的方向定位算法往往忽略观测点误差。Vaghefi等人将基于极大似然法的多点方向定位方法进行了扩展,将观测点位置误差引入模型,有效提高了优化模型的精度;但是该模型没有考虑观测点和目标之间的几何关系约束。文中将等式约束条件引入优化模型,并利用SQP优化方法进行优化计算,简化了Hessian矩阵,推导出其他参数的解析解,由于考虑了几何约束关系,仿真结果表明新模型相对极大似然模型能更加准确的修正目标和观测点位置。

Most bearing-only localization methods neglect uncertainty in observer position. Recently, Vaghefi extends the maximum likelihood bearing-only localization method based on uncertainties in observer position, it raises model's accuracy effectively, but Vaghefi's new model doesn't consider the geometrical constrain between observers and object. In this paper, a geometrically constrained optimization model was proposed based on Vaghefi's model with considering the observer position error, sequential propagation (SQP) optimization method was used to correlate the object's position, Hessian matrix of SQP was simplified, and the parameters' analytical equations were shown. Because of consideration of geometrical constrain and observer position's error at the same time, simulation results show the new model is more accurate than the maximum likelihood method and Vaghefi's model.