基于动态环境衰减的粒子滤波室内定位算法

针对精确的室内定位中节点受复杂环境的干扰带来因距离相同而位置不同的环境差异,造成定位精度不足和定位稳定性较差的问题,提出了一种新的动态环境衰减因子(DEAF)模型的算法。算法构造DEAF模型,且重新定义了其取值方式。在算法中,首先利用粒子滤波算法对接收到的信号强度(RSSI)进行平滑处理;然后利用DEAF模型计算目标节点的估计距离;最后用三边测量法求出目标节点的坐标。通过与几种常用的滤波模型进行对比实验,得出这种动态环境衰减模型结合粒子滤波的算法能很好地调和不同位置带来的环境噪声差异,算法使定位平均误差降到0.68 m左右,且在室内定位中有较高的定位精度和较好的稳定性。

Indoor positioning algorithm with dynamic environment attenuation based on particle filtering

Due to the problem that the nodes having the same distance but different position in the complex environment, brings shortage to accuracy and stability of indoor positioning, a new indoor positioning algorithm with Dynamic Environment Attenuation Factor (DEAF) was proposed. This algorithm built a DEAF model and redefined the way to assume the value. In this algorithm, particle filtering method was firstly used to smooth the Received Signal Strength Indication (RSSI); then, the DEAF model was used to calculate the estimation distance of the node; finally, the trilateration was used to get the position of the target node. Comparative experiments had been done using several filtering models, and the results show that this dynamic environment attenuation factor model combined with particle filtering can resolve the problem of the environment difference very well. This algorithm reduces the mean error to about 0.68 m, and the result has higher positioning accuracy and good stability.