CN105813193A - 一种智能电网无线传感器网络节点定位方法 - Google Patents
一种智能电网无线传感器网络节点定位方法 Download PDFInfo
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- H—ELECTRICITY
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Abstract
本发明提供一种智能电网无线传感器网络节点定位方法,包括设置多个固定节点作为信标节点,无线传感器网络移动节点向其周围的多个信标节点发送定位信息,通过RSSI算法根据发送场强和修正后的接收场强的关系得到移动传感器网络节点与多个固定节点间的距离;利用三边测量法求得移动传感器网络节点的坐标信息;利用极大似然估计法对求得的移动传感器网络节点的坐标信息进行修正。本发明提供的一种智能电网无线传感器网络节点定位方法采用了有线和无线混合的通信方式用于井下电力节点通信,可提高井下智能电网异构网络混合通信中的节点定位精度。
Description
技术领域
本发明涉及无线通信技术领域,特别涉及一种智能电网无线传感器网络节点定位方法。
背景技术
在智能电网的大背景下,用电信息采集系统肩负着用电信息自动采集、高效共享和实时监控的重要任务。用电信息采集系统通信网络具有节点多,应用场景复杂的特点。传感器网络具有低功耗、低成本、抗干扰性强、高灵活性、建设周期短等优势,已经广泛应用于用电信息采集系统。无线传感器网络是由一些具备感知环境参数能力的传感器节点组成的网络,并装备有无线传输设备进行无线通信。这种无线传感器网络被广泛使用于一些环境感知应用中,诸如水质监控、室内空气质量监控、精密农业管理等。在上述的这些应用中,如果无法获取节点的具体位置信息是没有意义的,因而无线传感器网络中节点的定位问题成为目前炙手可热的研究之一。
目前已有的基于场强的RSSI定位技术是一种粗糙的测距定位技术,有时定位误差甚至会达到50%。因为该方法利用的是测量发送节点和接收节点的场强,两者作差估算出场强的衰减值,然后根据电磁波衰减模型,计算出对应两节点的距离。这种方法的优点是所需成本和功率都较低,因为节点本身就具有无线传输的功能。但是该方法利用的是信号场强,信号在井下传输过程中的反射和非视距效应都会造成传输损耗的不确定性,因此测距精度较低。在已有的研究中,有关研究人员提出了一种基于无线传感器网络的改进的RSSI井下定位算法,该系统通过迭代计算和解决在数据传输过程中的潜在冲突提高了节点定位精度。有人提出了一个地下矿井定位方法,使用安装在信标节点上的传感器进行准确定位。这些传感器使用超宽带信号进行测距,并使用统计推断的方法校正由于多径和非视距传播造成的误差。
但是,由于井下无线环境复杂,受到各种因素的影响,目前的RSSI定位系统还很不成熟,系统定位精度还不能满足实际的需求。
发明内容
针对以上问题,本发明专利目的在于设计了一种智能电网无线传感器网络节点定位方法,采用了有线和无线混合的通信方式用于井下电力节点通信,可提高井下智能电网异构网络混合通信中的节点定位精度。本发明是通过以下技术方案实现的:
一种智能电网无线传感器网络节点定位方法,包括如下步骤:
采用电力线通信设置多个固定节点,并根据固定节点的位置确定各个固定节点的坐标信息;
移动传感器网络节点向其周围的多个固定节点发送场强,固定节点接收并测量场强的大小;
通过RSSI算法根据发送场强和修正后的接收场强的关系得到移动传感器网络节点与多个固定节点间的距离;
根据固定节点的坐标信息和移动传感器网络节点与多个固定节点间的距离利用三边测量法求得移动传感器网络节点的坐标信息;
利用极大似然估计法对求得的移动传感器网络节点的坐标信息进行修正。
进一步,本发明所述固定节点结合井下巷道狭窄狭长的特点部署在巷道两侧,所述移动传感器网络节点在巷道中间自由移动,所有固定节点的数据都传输到汇聚节点。
进一步,本发明所述移动传感器网络节点(xi,yi)与固定节点(x,y)间的距离根据以下公式确定:
其中
其中,d0是参考距离,p0i表示距离d0处的功率,β是路径损耗指数,nij是一个零均值且标准差为σ的高斯随机变量。
进一步,本发明所述修正后的接收场强为根据电磁波影响造成的功率衰减求得,所述电磁波衰减表达式为:
矩形井下隧道截面:
拱顶井下隧道截面:
其中,α单位为dB/m,a为巷道最宽距离,b为巷道最高距离,巷道内为介电常数为ε0、磁导率为μ0的理想介质;εr是巷道两侧壁的相对介电常数,λ为衰减前电磁波波长。
进一步,本发明所述节点(xi,yi)和(xj,yj)之间的最大似然估计距离为:
节点之间距离的无偏估计为:
基于RSSI定位的无偏距离估计方差的Cramer-Rao下界由下式获得:
其中,d0是参考距离,p0i表示距离d0处的功率,pij表示节点距离处的功率,β是路径损耗指数,σij为节点距离处的高斯随机变量。
本发明提供的一种智能电网无线传感器网络节点定位方法与现有技术相比具有以下优势:
1、从场景角度,考虑了井下异构混合智能电网中的节点定位精度问题。结合用电信息采集系统的特点,采用灵活性较强的无线传感器网络加上固有的PLC通信网络组成本地网络,在Sink节点部署集中器,在源节点处部署采集器。无线采集器是异构的微功率无线设备。
2、从定位方法角度,采用了基于场强的RSSI定位技术。虽然是一种粗糙的定位技术,但这种方法的优点是所需成本和功率都较低,对于微功率无线传感器来说有很大的优势。该方法利用的是测量信标节点和目标节点的场强,两者作差估算出场强的衰减值。
3、从优化的角度,考虑了实际环境的复杂性。实际中抄表系统工作在复杂的电磁环境中,所以传输过程中会有无线功率的损耗。如果不能很好的考虑这些衰减,定位精度必然受到影响。本发明考虑了实际电磁环境并对基本RSSI定位算法进行了电磁波修正。
附图说明
以下参照附图对本发明实施例作进一步说明,其中:
图1是本发明实施例提供的井下混合网络模型图;
图2是本发明实施例提供的三边测量法示意图;
图3是本发明实施例提供的极大似然估计法示意图;
图4是本发明实施例提供的矩形巷道示意图;
图5是本发明实施例提供的隧道形状示意图;
图6是本发明实施例提供的不同距离下定位坐标与实际坐标比较图;
图7是本发明实施例提供的不同距离下定位算法均方根误差比较图。
具体实施方式
下面结合附图和具体实施例对本发明作进一步的详细说明。
本发明结合井下用电信息采集系统的特点,首先考虑基本的RSSI定位算法,在节点接收端,考虑通信过程中存在的电磁波衰减,给接收场强加上电磁波修正,从而使节点的定位精度更高。下面结合附图对本发明的应用原理作详细的描述。
本发明提供一种智能电网无线传感器网络节点定位方法,包括如下步骤:
采用电力线通信设置多个固定节点,并根据固定节点的位置确定各个固定节点的坐标信息;
移动传感器网络节点向其周围的多个固定节点发送场强,固定节点接收并测量场强的大小;
通过RSSI算法根据发送场强和修正后的接收场强的关系得到移动传感器网络节点与多个固定节点间的距离;
根据固定节点的坐标信息和移动传感器网络节点与多个固定节点间的距离利用三边测量法求得移动传感器网络节点的坐标信息;
利用极大似然估计法对求得的移动传感器网络节点的坐标信息进行修正。
如图1所示,是一个电力信息采集混合通信的场景。作业面上分布着一些固定节点和移动节点。这些移动节点是一些无线传感器,它们的位置在不断变化,其使用无线通信的方式传输电力信息数据;固定节点固定不动,采用电力线通信的方式传输数据。最终所有节点的数据都经过转发,传输到汇聚节点。考虑到移动节点的位置不断变化,在这样一个混合网络中,要想实现两种通信方式的互联互通,首先要考虑的应该是如何精确定位移动节点。定位技术对于包含无线传感器的混合网络至关重要。在用电信息采集传输系统中常用的定位算法是基于场强的定位技术。基于场强的定位技术虽然是一种粗糙的定位技术,但这种方法的优点是所需成本和功率都较低,对于微功率无线传感器来说有很大的优势。该方法利用的是测量信标节点和目标节点的场强,两者作差估算出场强的衰减值,然后根据电磁波衰减模型,计算出对应两节点的距离,进而求出目标节点坐标。在我们的混合通信模型中,固定节点位置已知,RSSI正好可以利用固定节点作为信标节点,确定移动节点的位置。
RSSI算法是基于场强的定位算法,它的基本原理是:已知发送节点的信号发射场强,和接收节点接收到的信号强度,就可以估算出传输过程中的场强衰减,根据电磁波场强衰减与传输距离的关系,将场强衰减差值代入就可以计算出发送和接收节点间的距离。
如图2和图3所示,已知未知节点到信标节点的距离,就可以利用三边测量法和极大似然估计法得到定位坐标。如果已知未知节点距离三个信标节点的距离,以三个信标节点为圆心,距离为半径画三个圆,三个圆必定交于一点。写出三个圆的方程,求解之后就可以得到交点的坐标。
极大似然估计用于提高三边测量法的精度,它选取三个以上节点,其通过增加定位信息以提高计算精度。
假设有m个信标节点,以信标节点为圆心,距离为半径画圆,则这些圆应该都交于一点,该点即为未知节点。
如图4所示为矩形巷道横截面图,其中α单位为dB/m,a为巷道最宽距离,b为巷道最高距离,巷道内为介电常数为ε0、磁导率为μ0的理想介质;εr是巷道两侧壁的相对介电常数,λ为衰减前电磁波波长。如图5所示为拱顶巷道横截面图。根据矩形波导理论求解波导场分量方程的方法得到巷道内电磁波的各场分量,进而求解波动方程,将结果带入麦克斯韦方程,即可求出由于电磁波影响造成的功率衰减表达式。为了通信的可靠性,最好额外安装一些天线,这样在相邻节点之间可以尽可能地保持为视距传输。平均接收功率和信号衰减功率可以分别由下列式子表示:
其中d=|d(xi,xj)-d0|。
常用于评价定位准确率的度量是定位坐标与实际坐标的均方误差MSE与均方根误差RMSE。在二维定位估计中计算MSE的方法为:
MSE=E[(X-x)2+(Y-y)2]
其中(X,Y)为MS的实际位置,(x,y)为MS的估计位置。此外,均方根误差也常用于评价定位准确率:
本发明利用了归一化的均方根误差作为定位准确率评价指标。
下面结合仿真对本发明的应用效果做进一步的说明:
1、仿真条件:
本发明的仿真环境和参数的配置如下:
仿真中假定固定节点分布在三角形的三个顶点作为信标节点,移动节点随机分布在三角形内部,每次都往三角形内部随机部署m个节点,对m个节点进行定位,最后取测量误差的平均值。为了便于仿真,使传输距离增大采用的方法是成倍增大三角形的面积。
对于电磁波修正我们假定井下为拱形巷道,路径损耗n=2.75,参考距离d0=20m,介电常数εr为10,巷道横截面长宽分别为a=2.5m,b=2m。
2、仿真内容与仿真结果:
考虑不同的传输距离,分别对定位坐标和实际坐标的实际位置和均方根误差进行仿真,结果如下:
图6为本发明实施例提供的不同传输距离下,定位坐标和实际坐标的实际位置比较。我们可以看出,在距离很近的时候,定位精度相差不多,这表示电磁波修正带来的收益比较少。但随着距离的增加,RSSI定位算法的性能开始变差,得到的定位坐标与实际相差较大,此时电磁波修正变得很有必要,其有效减小了由于距离增加而造成的定位误差,我们看到这种修正效果发挥了一定的作用,提高了RSSI的定位精度。
图7为本发明实施例提供的不同传输距离下归一化的定位坐标和实际坐标均方根误差比较,可以看到RSSI算法的性能虽然随距离增加变差,但是这种变坏并没有急剧增大。也能清楚看到电磁波修正是与距离成比例的,我们的修正是线性修正,这一点可以由电磁波修正的公式验证。
以上所述本发明的具体实施方式,并不构成对本发明保护范围的限定。任何根据本发明的技术构思所做出的各种其他相应的改变与变形,均应包含在本发明权利要求的保护范围内。
Claims (5)
1.一种智能电网无线传感器网络节点定位方法,其特征在于,包括如下步骤:
采用电力线通信设置多个固定节点作为信标节点,并根据固定节点的位置确定各个固定节点的坐标信息;
移动传感器网络节点向其周围的多个固定节点发送定位信息,固定节点接收并确定场强的大小;
通过RSSI算法根据发送场强和修正后的接收场强的关系得到移动传感器网络节点与多个固定节点间的距离;
根据固定节点的坐标信息和移动传感器网络节点与多个固定节点间的距离利用三边测量法求得移动传感器网络节点的坐标信息;
利用极大似然估计法对求得的移动传感器网络节点的坐标信息进行修正。
2.根据权利要求1所述的智能电网无线传感器网络节点定位方法,其特征在于,所述固定节点结合井下巷道狭窄狭长的特点部署在巷道两侧,所述移动传感器网络节点在巷道中间自由移动,所有固定节点的数据都传输到汇聚节点。
3.根据权利要求1所述的智能电网无线传感器网络节点定位方法,其特征在于,所述移动传感器网络节点(xi,yi)与固定节点(x,y)间的距离根据以下公式确定:
其中
其中,d0是参考距离,p0i表示距离d0处的功率,β是路径损耗指数,nij是一个零均值且标准差为σ的高斯随机变量。
4.根据权利要求1所述的智能电网无线传感器网络节点定位方法,其特征在于,所述修正后的接收场强为根据信道环境对电磁波传输影响造成的功率衰减求得,所述电磁波衰减表达式为:
矩形井下隧道截面:
拱顶井下隧道截面:
其中,α单位为dB/m,a为巷道最宽距离,b为巷道最高距离,巷道内为介电常数为ε0、磁导率为μ0的理想介质;εr是巷道两侧壁的相对介电常数,λ为衰减前电磁波波长。
5.根据权利要求1所述的智能电网无线传感器网络节点定位方法,其特征在于,所述节点(xi,yi)和(xj,yj)之间的最大似然估计距离为:
节点之间距离的无偏估计为:
基于RSSI定位的无偏距离估计方差的Cramer-Rao下界由下式获得:
其中,d0是参考距离,p0i表示距离d0处的功率,pij表示节点距离处的功率,β是路径损耗指数,σij为节点所在位置的高斯随机变量。
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