基于LQI滤波与联合参数估计的井下人员定位算法

现有的基于RFID技术的井下人员定位系统存在定位准确度低、抗干扰性差等问题。为提高井下人员定位算法的准确性和环境适应性,提出了一种在ZigBee通信协议框架下,基于LQI滤波与RSSI联合参数估计的定位算法。首先使用LQI指标对通过ZigBee芯片读取的原始RSSI数据进行滤波,其次对RSSI的信道参数和环境噪声参数进行联合估计,并依据环境噪声参数对距离估计进行补偿,最后利用最小二乘法完成定位计算。算法挖掘了现有硬件设备的潜力,更多考虑了环境对定位的影响,有效的提高了定位的准确度。最后通过ZigBee硬件平台验证了算法的有效性,与原有RSSI定位算法相比,改进算法将定位均方误差降低了约10%。

Coal mine underground personnel localization algorithm based on LQI filter and joint parameters estimation

Existing RFID-based underground personnel localization systems have the problems of low positioning accuracy，low noise immunity and other issues.In order to enhance the accuracy and environmental adaptation of the coal mine underground personnel localization algorithm，this paper proposes a RSSI (received signal strength indication) joint parameters estimation and localization algorithm based on LQI (link quality indication) filter in the ZigBee communication protocol framework.The proposed algorithm first uses the LQI as the smoothing filter to process the original RSSI data collected from ZigBee chips，and then estimates the RSSI channel parameters combined with environmental noise parameter.Moreover，the environmental noise parameter is used to compensate the estimated distance.Finally，it gets the localization results via the least square method.Through taking the potential of existing hardware equipments and paying more consideration to the effects of environment，the proposed algorithm effectively improves the accuracy of localization.The effectiveness of the algorithm is confirmed by simulation based on the ZigBee hardware platform.Compared with the original RSSI location algorithm，the mean square error of the proposed algorithm is reduced by about 10%.