Chang Bo, Zhang Xinrong, Sun Na, Ni Hao
Faculty of Electronic Information Engineering, HuaiYin Institute of Technology, Huaian 223003, China.
Faculty of Automation, HuaiYin Institute of Technology, Huaian 223003, China.
Sensors (Basel). 2025 May 2;25(9):2883. doi: 10.3390/s25092883.
A two-stage fusion filtering positioning algorithm based on prediction residuals and gain adaptation is proposed to address the problems of disturbance and modeling errors in the application of distance-based positioning algorithms in wireless sensor networks, as well as inaccurate initial filtering values leading to large estimation errors or even divergence. Firstly, based on parameterization methods, a pseudo linear equation is constructed from the time of arrival (TOA) and multipath delay. The weighted least squares (WLS) method is applied to obtain the initial value of target position resolution, and its performance approaches the Cramér-Rao lower bound (CRLB). Secondly, the exact position of the target is obtained using the reconstructed Gaussian white noise statistics and the Kalman filtering algorithm. The simulation results show that compared with other initial positioning algorithms, the average positioning accuracy of the proposed algorithm is improved by 28.57%, and it has a better filtering performance.
针对无线传感器网络中基于距离的定位算法应用时存在的干扰、建模误差以及初始滤波值不准确导致估计误差大甚至发散等问题，提出了一种基于预测残差和增益自适应的两阶段融合滤波定位算法。首先，基于参数化方法，从到达时间（TOA）和多径延迟构建伪线性方程，应用加权最小二乘法（WLS）获得目标位置分辨率的初始值，其性能接近克拉美罗下界（CRLB）。其次，利用重构的高斯白噪声统计量和卡尔曼滤波算法获得目标的精确位置。仿真结果表明，与其他初始定位算法相比，该算法的平均定位精度提高了28.57%，具有更好的滤波性能。
