针对室内复杂环境中无线信号在非视距传播时造成的定位精度低的问题,提出一种门限比较加权法(TCW)-Taylor级数展开的联合定位算法。首先通过卡尔曼滤波器实时消除信号到达时间(TOA)测量值中的非视距误差,然后然后在平滑过的TOA值和含测量噪声的信号到达角度(AOA)确定的定位区域内利用TCW计算目标节点的位置,将计算结果作为Taylor级数展开的初值,最后通过迭代求解实现第二次精细定位。仿真实验结果表明,与传统的全质心-Taylor级数展开定位算法和基于最小二乘法的TOA/AOA混合定位算法相比,增加AOA约束条件和对不同的位置点赋予不同的动态权值定位,可以使初始定位结果更加准确,更加接近克拉默-拉奥下界。

A joint localization algorithm based on the threshold comparative weighted (TCW) Taylor series expansion is proposed to address the problem of low positioning accuracy caused by the non-line-of-sight propagation of wireless signals in complex indoor environments. First, the non-line-of-sight error in the measured value of the time of arrival (TOA) is eliminated in real-time by Kalman filter. Then, TCW is used to calculate the location of the target node in the location area determined by the smoothed TOA value and the angle of arrival (AOA) of the signal with measurement noise. The calculated result is taken as the initial value of Taylor series expansion. Finally, the iterative solution is carried out to achieve the second fine positioning. The simulation results demonstrate that compared with the traditional centroid-Taylor series expansion location algorithm and TOA/AOA hybrid location algorithm based on the least-squares method, the initial location results can be more accurate and closer to the Cramer-Rao lower bound by adding AOA constraints and assigning different dynamic weights to different locations.