基于PDR和RSSI的室内定位算法研究

构建了基于无线传感网络的行人航迹推算(PDR)系统,通过RSSI定位为其提供绝对定位信息。在RSSI定位中,提出基于PDR方位信息的自适应Flip-Flop RSSI信息预处理机制和RSSI指纹信息融合动态路径衰减指数的定位算法,以改善RSSI定位算法的抗噪声干扰能力。在多信息融合粒子滤波环节中,针对传统算法中滤波精度与滤波实时性很难同时得到改善的问题,提出基于PDR信息与RSSI定位信息的动态区间粒子滤波算法,通过PDR方位信息自适应控制区间衍生粒子数量以提高滤波实时性,并将建筑地图信息、RSSI定位信息及其可信度因子融入粒子权值计算中以提高定位精度。经实验验证,提出的算法在RSSI定位抗噪声能力方面,以及融合定位精度和滤波实时性方面都取得了良好的效果,与传统算法相比最大定位误差由3.16 m降低到1.81 m,滤波时间也由7.21 s降至7.01 s。

Study on PDR and RSSI based indoor localization algorithm

In this paper a pedestrian dead reckoning (PDR) system based on wireless sensor network is proposed, and the RSSI positioning system is used to provide absolute position information. Meanwhile, aiming at the problem that traditional RSSI positioning algorithm is vulnerable to environmental noise, we propose an adaptive Flip Flop RSSI preprocessing algorithm based on PDR azimuth information and a positioning algorithm of RSSI fingerprint information fusion and dynamic path attenuation index to improve the anti noise interference capability of the RSSI positioning algorithm. In multi information fusion particle filtering, aiming at the problem of poor filtering precision and real time ability, we proposed a dynamic interval particle filter algorithm based on PDR information and RSSI localization information. With PDR azimuth information, the algorithm can adaptively control the interval derivative particle number to enhance the filtering real time ability; meanwhile, the building map information, RSSI positioning information and certainty factor are integrated into the particle weight calculation to improve the positioning accuracy. The verification experiments show that the proposed algorithm has achieved good results in the terms of resisting noise ability of RSSI positioning, combining positioning precision and real time performance; compared with the traditional algorithm the maximum positioning error is reduced from 3.16 m to 1.81 m, and the filtering time is reduced from 7.21 s to 7.01 s.