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Wireless Localization Method Based on AHP-WKNN and Amendatory AKF

机译：基于AHP-WKNN的无线定位方法和算法AKF

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In this paper, we propose an AHP-WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted - nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP-WKNN method and the modified AKF can improve positioning accuracy effectively.
机译：在本文中，我们提出了一种用于室内定位的AHP-WKNN方法，其结合了分析层次处理（AHP）技术和加权 - 最近邻（WKNN）算法。 当使用WKNN选择用于室内定位的指纹时，AHP用于分配权重。 AHP技术可以合理地扩大参考点之间的接收信号强度（RSS）间隙对重量的影响，实现了更好的定位性能。 本文还通过基于RSS测量和预期输出之间的误差来校正输出来修改自适应卡尔曼滤波器（AKF）降噪方法。 修改的AKF可以更有效地跟踪RS的变化并实现更好的降噪性能。 仿真结果表明，所提出的AHP-WKNN方法和改进的AKF可以有效地提高定位精度。

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