基于混合子空间去噪的免携带设备目标定位
Device Free Localization Based on Mixed Subspace Denoising

免携带设备目标定位是利用目标人物对线通信链路产生的阴影衰落来确定目标的位置，然而当环境发生变化时也会引起线链路信号强度的变化，会影响定位精度。针对传统子空间去噪方法的不足，本文与指纹法相结合探讨了基于子空间分解的小波去噪方法。通过研究静态环境下的噪声特征，选取最大特征值作为信号分量的阈值，自适应地提取目标信号，有效地消除环境变化产生的影响。在线阶段通过计算实时接收信号强度和射频地图中数据信息之间的核距离进行匹配估计出目标的位置。最后通过实验对本文算法进行了仿真，结果表明本文提出的算法相比传统已有算法能达到更好的定位精度。
Device free localization (DFL) utilizing the received signal strength (RSS) variations on the wireless link caused by an object is the estimation of the object such as a person without carrying any electronic device. However, environment influences, such as temperature or swaying of sensor nodes, also alter RSS variations, thus degrading the positioning accuracy. A novel wavelet denoising algorithm based on subspace decomposition, combined with the fingerprint method, is proposed to reduce the environment impact. The noise characteristics of static environment are researched, then the maximum characteristic value is extracted as threshold, the signal features are adaptively decomposed into different orthogonal subspaces and the object signal is reconstructed in subspace. The feather extraction method is discussed after the mixed denoising analysis. Gaussian radial basis function is utilized to calculate the kernel distance between online measurement received signal strength and the fingerprint data to estimate the target location coordinate. Simulation results indicate that the proposed algorithm can achieve better positioning accuracy than the traditional location algorithm.