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
为了解决基于蓝牙射频RSS的室内定位算法精度低、实时性差等问题,提出一种融合到达角(Angle of Arrival,AOA)与射频RSS的K近邻指纹定位算法,通过采集NRF51822传感器的射频RSS聚类信号形成定位指纹库,采用KNN欧式最优算法与指纹库进行匹...为了解决基于蓝牙射频RSS的室内定位算法精度低、实时性差等问题,提出一种融合到达角(Angle of Arrival,AOA)与射频RSS的K近邻指纹定位算法,通过采集NRF51822传感器的射频RSS聚类信号形成定位指纹库,采用KNN欧式最优算法与指纹库进行匹配得出近似坐标位置,设计一款小型PCB八木天线模拟定位基站,补偿射频RSS随距离、遮挡等造成的信号接收强度指示(Received Signal Strength Indicator,RSSI)跳变、衰减,通过信标与定位节点的AOA到达角与最优指纹数据的权值归一化换算,得出最终定位坐标。实验结果表明,该算法具有定位精度高、实时性好等优点,具有较高的推广价值。展开更多
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。
文摘为了解决基于蓝牙射频RSS的室内定位算法精度低、实时性差等问题,提出一种融合到达角(Angle of Arrival,AOA)与射频RSS的K近邻指纹定位算法,通过采集NRF51822传感器的射频RSS聚类信号形成定位指纹库,采用KNN欧式最优算法与指纹库进行匹配得出近似坐标位置,设计一款小型PCB八木天线模拟定位基站,补偿射频RSS随距离、遮挡等造成的信号接收强度指示(Received Signal Strength Indicator,RSSI)跳变、衰减,通过信标与定位节点的AOA到达角与最优指纹数据的权值归一化换算,得出最终定位坐标。实验结果表明,该算法具有定位精度高、实时性好等优点,具有较高的推广价值。
