基于半定规划的移动辅助定位算法

Semi-Definite Programming Based Mobility-Assisted Localization Algorithm

近期,基于移动单元的无线定位方案受到广泛关注。然而,现存的定位方案假定移动单元位置已知、无线传播模型的参数已知,这与事实并不相符。为此,提出基于半定规划的移动辅助定位(Semi-Definite Programming based Mobility-Assisted Localization,SMAL)算法。移动单元沿着任意轨迹移动,并周期向传感节点传输beacon包。锚节点从移动单元接收信号,并依据接收beacon的信号强度的相似性估计节点间距离。最后,依据距离值,并结合半定规划算法估计节点位置。仿真结果表明,相比于静态定位算法,SMAL算法能够获取高的定位精度。

In recent years,much attention has been paid to wireless localization schemes that exploit receptions of messages sent by a mobile unit.However,existing methods assume an accurate knowledge of the location of the mobile unit and a precise propagation model of the actual radio environment.This does not tally with the facts.Therefore,Semi-Definite Programming based mobility-assisted localization(SMAL)is proposed in this paper.In SMAL,a mobile unit moving along an arbitrary trajectory broadcasts beacons periodically to all sensor nodes.Receiving messages sent from a mobile unit,anchor nodes estimate an inter-node distance using a similarity between received signal strength indicators of beacons received from the mobile unit.Finally,according to the distance value and the semi-definite programming algorithm,the node location is estimated.Simulation results show that the proposed SMAL algorithm achieve a very high localization accuracy while static localization techniques fail.