煤矿井下基于虚拟Radio-map及Markov链的定位算法

为了在煤矿井下获得更高的定位精度,提出一种基于虚拟Radio-map及Markov链的定位方法。结合井下复杂环境,采用信道衰减模型及线性插值法实现了动态衰减因子,建立虚拟Radio-map的同时降低了工作量;考虑到每处采样点接收信号强度分布先验假设和统计特征,在线阶段采用基于贝叶斯准则框架的加权核函数算法,为每个样本数据赋予一个以自身为"核心"的函数,构建的概率密度分布避免了确定模型带来的误差,从而提高了定位精度;为进一步优化定位结果,考虑先验概率对贝叶斯后验概率的影响,提出了基于高斯模型的Markov链定位算法,抑制了运动目标位置的大幅度跳变,使目标定位更加精确。实验表明,所提算法可以通过较低数据采集工作量达到一定的定位精度,满足井下目标定位需求。

Positioning algorithms of underground coal mines based on virtual Radio-map and Markov chain

For the heavy workload of data sampling by fingerprint matching algorithm, a algorithm based on virtual Radio-map and Markov chain was presented to enhance the positioning accuracy in coal mine. Given the complex underground environment, dynamic attenuation factor was implemented by fading channel model and linear interpolation method,and the establishments of virtual Radio-map reduced the offline work load. Given the received signal strength indication(RSSI) priori distribution assumptions and statistical characteristics at each sampling point, weighted kernel function method based on Bayesian framework was utilized at online phase. To construct probability density distribution, each sample was given a " kernel" with itself by kernel function method and avoided errors caused by determining model,with the positioning precision improved. In order to optimize the positioning results, considering the influence of prior probability on posterior probability,Markov chain positioning algorithm based on Gaussian model was proposed and the positioning improved by inhibiting greatly the jump of moving target. Experiments show that the positioning accuracy meets the requirement of the underground localization.