基于证据理论的群指纹融合室内定位方法

A New Indoor Localization Algorithm by Fusing Group of Fingerprints via Dampster-Shafer Evidence Theory

室内定位的主要挑战是室内的多径传播及非平稳信道环境,传统基于信号强度指纹的单指纹室内定位方法由于受环境变化影响较大,稳健性较差且精度较低。针对此问题,提出一种基于D-S证据理论的群指纹融合高精度室内定位方法。在建库阶段,利用室内阵列信号接收模型,首先通过计算阵列接收信号的不同统计特性构建包括信号强度、协方差矩阵、信号子空间及四阶累积量组成的群指纹库,再对群指纹进行神经网络训练获取针对每种指纹的神经网络分类器;在实测阶段,把实测数据的上述4种变换输入到训练好的神经网络分类器中,最后利用D-S证据理论对神经网络分类器的分类结果进行融合,给出最终的定位结果。仿真结果证明了算法的有效性及可行性。该算法可充分发挥指纹信息的集群效应,对噪声、多径传播等具有较好的稳健性,是一种高精度的室内定位新方法。

The main challenges of indoor localization come from multi-path propagation and non-stationary channel environment. Some classical localization approaches based on single received signal strength （RSS） fingerprint show low accuracy and bad robustness due to some environment changes. In this paper, we propose an accurate indoor localization algorithm by fusing group of fingerprints via Dampster-Shafer （D-S） evidence theory. The main idea can be summarized as follows： in off-line phase, first, based on the received data from a receiving array deployed in indoor environment, we calculate four fingerprints, namely, RSS, covariance matrix, signal subspace, and fourth-order cumulant. Secondly, these fingerprints are input to train four different classifiers by using back-propagation （BP） neural networks. In on-line phase, by calculating the corresponding transformations of the received signals of the array, we can obtain the predictions of these classifiers; then, we use D-S evidence theory to fuse the final localization results. The proposed algorithm can deal with different environment noise adaptively and show higher accuracy compared with some existing fingerprint-based algorithms. The performance of our proposed algorithm is verified by simulation results.