针对无线传感器网络中的联合接收信号强度(received signal strength,RSS)和到达角度(angle of arrival,AOA)定位问题,提出一种全新的凸组合定位方法。该方法使用某些特定点(称为虚拟点)的凸组合来估计目标点的位置。提出了基于二次约...针对无线传感器网络中的联合接收信号强度(received signal strength,RSS)和到达角度(angle of arrival,AOA)定位问题,提出一种全新的凸组合定位方法。该方法使用某些特定点(称为虚拟点)的凸组合来估计目标点的位置。提出了基于二次约束二次规划(quadratically constrained quadratic programming,QCQP)和半正定规划(semidefinite programming,SDP)两种虚拟点构造方法。在此基础上,将目标定位的极大似然(maximum likelihood,ML)估计问题进行凸化,得到组合系数,进一步得到目标定位结果。数值实验表明,所提出的凸组合方法比文献中的几种定位方法具有更高的精度,特别是相对于线性最小二乘(linear least squares,LLS)方法,精度最高提升约40%。此外,其定位结果可以作为ML估计方法的初始化,进一步提升定位性能。展开更多
In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in ...In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.展开更多
文摘针对无线传感器网络中的联合接收信号强度(received signal strength,RSS)和到达角度(angle of arrival,AOA)定位问题,提出一种全新的凸组合定位方法。该方法使用某些特定点(称为虚拟点)的凸组合来估计目标点的位置。提出了基于二次约束二次规划(quadratically constrained quadratic programming,QCQP)和半正定规划(semidefinite programming,SDP)两种虚拟点构造方法。在此基础上,将目标定位的极大似然(maximum likelihood,ML)估计问题进行凸化,得到组合系数,进一步得到目标定位结果。数值实验表明,所提出的凸组合方法比文献中的几种定位方法具有更高的精度,特别是相对于线性最小二乘(linear least squares,LLS)方法,精度最高提升约40%。此外,其定位结果可以作为ML估计方法的初始化,进一步提升定位性能。
基金National Natural Science Foundation of China,grant number 62205120,funded this research.
文摘In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.
