Chen Zhang, Wang Jinlong
College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China.
The Sixty-third Institute, National University of Defense Technology, Nanjing 210007, China.
Sensors (Basel). 2019 May 30;19(11):2482. doi: 10.3390/s19112482.
In this paper, we propose a novel indoor passive localization approach called eigenspace-based DOA with direct-path recognition (ES-DPR), based on a DOA estimation algorithm with multiple omnidirectional antennas deployed in a uniform linear array (ULA). To address the multipath propagation interference problem in the indoor environments, we utilize the azimuth and RSS estimation results, which are calculated by using the eigenspace-based DOA (ES-DOA) algorithm, in a novel style. A direct-path bearing recognition algorithm is introduced to identify the real DOA of the signal source in different indoor environments, by combining the azimuth and RSS estimation with ensemble learning methods. Numerical simulations are conducted to verify the validity and superiority of the proposed method. The results show that the proposed ES-DPR method can achieve high resolution and has strong anti-noise capability in dealing with the multipath signals, and the direct-path recognition algorithm is reliable and robust in different indoor environments, even in undetectable direct-path conditions.
在本文中，我们基于一种在均匀线性阵列（ULA）中部署多个全向天线的波达方向（DOA）估计算法，提出了一种名为基于特征空间的带直线路径识别的波达方向（ES-DPR）的新型室内无源定位方法。为了解决室内环境中的多径传播干扰问题，我们以一种新颖的方式利用基于特征空间的波达方向（ES-DOA）算法计算出的方位角和接收信号强度（RSS）估计结果。引入了一种直线路径方位识别算法，通过将方位角和RSS估计与集成学习方法相结合，来识别不同室内环境中信号源的真实波达方向。进行了数值模拟以验证所提方法的有效性和优越性。结果表明，所提的ES-DPR方法能够实现高分辨率，并且在处理多径信号时具有很强的抗噪声能力，而且直线路径识别算法在不同室内环境中，甚至在无法检测到直线路径的情况下都是可靠且稳健的。
