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
基于接收信号强度差(Difference of Received Signal Strength, DRSS)的定位模型具有节省能量、带宽和时间的优点,并且在定位过程中隐藏了发射机的传输方式,非常有益于机密监视或军事应用。然而DRSS模型具有较高的非凸性,在定位求解时...基于接收信号强度差(Difference of Received Signal Strength, DRSS)的定位模型具有节省能量、带宽和时间的优点,并且在定位过程中隐藏了发射机的传输方式,非常有益于机密监视或军事应用。然而DRSS模型具有较高的非凸性,在定位求解时比较困难,本文提出了一种改进的定位方法——相对误差及凸优化混合定位方法。首先借助相对误差方法构建最小化问题,然后借助半正定规划和二阶锥规划对模型进行近似求解。为了验证所提方法的有效性,引入均方根误差(Root Mean Square Error, RMSE)作为估计方法精度的评判标准,通过对比本文所提方法以及现有四种方法(A-BLUE、U-BLUE、LARE-SDP、SOCP)的RMSE,研究结果发现本文提出方法的RMSE值最低,并且更加贴近理论误差的CRLB下界。The positioning model based on Difference of Received Signal Strength (DRSS) has the advantages of saving energy, bandwidth, and time, and hides the transmission mode of the transmitter during the positioning process, which is very beneficial for confidential monitoring or military applications. However, the DRSS model has high nonconvexity and is difficult to solve in localization. This paper proposes an improved localization method—a hybrid localization method of relative error and convex optimization. Firstly, the minimization problem is constructed using the relative error method, and then the model is approximately solved using semi positive definite programming and second-order cone programming. In order to verify the effectiveness of the proposed method, Root Mean Square Error (RMSE) was introduced as the evaluation criterion for the accuracy of the estimation method. By comparing the RMSE of the proposed method with four existing methods (A-BLUE, U-BLUE, LARE-SDP, SOCP), the research results showed that the RMSE value of the proposed method was the lowest and closer to the CRLB lower bound of the theoretical error.展开更多
提出一种在低空场景下基于接收信号强度(Rcecived Signal Strength,RSS)与到达角度(Angle of Arrival,AOA)信息融合的单站无源定位算法。该算法采用单架无人机设备虚拟多站设备接收无线电辐射源信号,融合RSS估计的距离信息与AOA方向角信...提出一种在低空场景下基于接收信号强度(Rcecived Signal Strength,RSS)与到达角度(Angle of Arrival,AOA)信息融合的单站无源定位算法。该算法采用单架无人机设备虚拟多站设备接收无线电辐射源信号,融合RSS估计的距离信息与AOA方向角信息,依据最小二乘准则(LS)构造算法的优化目标函数,采用凸松弛技术将目标函数等价为二阶锥规划(SOCP)问题并通过内点法求解。实验结果表明,该算法的定位精度在2 km范围内可达20 m,其定位性能优于单站无源定位算法,且由于采用单架无人机采集信号,其设备复杂度相较于多站无源定位较低。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.
文摘基于接收信号强度差(Difference of Received Signal Strength, DRSS)的定位模型具有节省能量、带宽和时间的优点,并且在定位过程中隐藏了发射机的传输方式,非常有益于机密监视或军事应用。然而DRSS模型具有较高的非凸性,在定位求解时比较困难,本文提出了一种改进的定位方法——相对误差及凸优化混合定位方法。首先借助相对误差方法构建最小化问题,然后借助半正定规划和二阶锥规划对模型进行近似求解。为了验证所提方法的有效性,引入均方根误差(Root Mean Square Error, RMSE)作为估计方法精度的评判标准,通过对比本文所提方法以及现有四种方法(A-BLUE、U-BLUE、LARE-SDP、SOCP)的RMSE,研究结果发现本文提出方法的RMSE值最低,并且更加贴近理论误差的CRLB下界。The positioning model based on Difference of Received Signal Strength (DRSS) has the advantages of saving energy, bandwidth, and time, and hides the transmission mode of the transmitter during the positioning process, which is very beneficial for confidential monitoring or military applications. However, the DRSS model has high nonconvexity and is difficult to solve in localization. This paper proposes an improved localization method—a hybrid localization method of relative error and convex optimization. Firstly, the minimization problem is constructed using the relative error method, and then the model is approximately solved using semi positive definite programming and second-order cone programming. In order to verify the effectiveness of the proposed method, Root Mean Square Error (RMSE) was introduced as the evaluation criterion for the accuracy of the estimation method. By comparing the RMSE of the proposed method with four existing methods (A-BLUE, U-BLUE, LARE-SDP, SOCP), the research results showed that the RMSE value of the proposed method was the lowest and closer to the CRLB lower bound of the theoretical error.
文摘提出一种在低空场景下基于接收信号强度(Rcecived Signal Strength,RSS)与到达角度(Angle of Arrival,AOA)信息融合的单站无源定位算法。该算法采用单架无人机设备虚拟多站设备接收无线电辐射源信号,融合RSS估计的距离信息与AOA方向角信息,依据最小二乘准则(LS)构造算法的优化目标函数,采用凸松弛技术将目标函数等价为二阶锥规划(SOCP)问题并通过内点法求解。实验结果表明,该算法的定位精度在2 km范围内可达20 m,其定位性能优于单站无源定位算法,且由于采用单架无人机采集信号,其设备复杂度相较于多站无源定位较低。
