基于动态Radio Map的粒子滤波室内无线定位算法

针对目前大多数基于射频信号强度匹配定位算法在定位精度及鲁棒性方面不足，提出了一种基于动态Radio Map的粒子滤波室内无线定位算法.该算法利用参考节点构建基于空间关联性的动态Radio Map模型，以反映信号环境的实时变化，并将移动目标定位由分类问题转化为回归问题，打破了传统网格式Radio Map模型的限制，降低了算法的时空复杂度.实验结果表明，相对于静态Radio Map模型，动态Radio Map模型将定位精度平均提高了约20%，表现出良好的环境动态自适应能力.

Dynamic Radio Map Based Particle Filter for Indoor Wireless Localization

Indoor wireless localization is the basis of various location-based applications. So far, the majority of Radio Map based wireless indoor localization algorithms adopted the static radio map in which signal environment was regarded as stable over time, and didn’t make good use of continuous motion information of the target. In this paper a dynamic Radio Map based particle Filter for wireless indoor localization (DRMPF) algorithm is proposed, which combines the particle filter with Radio Map based positioning technology. By constructing spatial correlation model (SCM) based dynamic Radio Map, DRMPF uses some reference nodes to capture the real-time signal changes in the environment. Contiguous spatial correlation model breaks the limitation of traditional grid-shaped Radio Map, and converts the wireless indoor localization from classification problem into regression problem. It also reduces the training cost and algorithm complexity of the online localization stage. Extensive experiments demonstrates that the proposed SCM based Radio Map model has good time generalization ability. Compared with the static Radio Map, the DRMPF algorithm improves the positioning accuracy by about 20%, which demonstrates a good ability to adapt to the environment changes.