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
针对基于载噪比(carrier to noise ratio,CNR)的GNSS干扰源定位,在存在多个干扰源、多径传输且接收机间距较远时定位难度大、精度低的问题,提出了一种加权K均值(K-Means)聚类算法与基于差分接收信号强度(differential received signal s...针对基于载噪比(carrier to noise ratio,CNR)的GNSS干扰源定位,在存在多个干扰源、多径传输且接收机间距较远时定位难度大、精度低的问题,提出了一种加权K均值(K-Means)聚类算法与基于差分接收信号强度(differential received signal strength,DRSS)的方程解算定位相结合的多干扰源定位方法.在假设干扰源个数确定以及单个接收机只受到一个干扰源影响的前提下,设计了改进的加权K-Means聚类算法实现对多个干扰源位置的初步估计.为了进一步降低在观测接收机相距较远时加权K-Means方法的定位误差,在聚类后选取各簇内受干扰影响显著的接收CNR构建基于DRSS的定位方程组,通过方程解算得到更加精细的定位结果.仿真结果表明,所提出的定位方案可以实现对多干扰源的定位,结合DRSS参数定位后,单干扰源场景下定位误差可降低19%以上,存在两个单音干扰源的场景下定位误差可降低38%以上.展开更多
Beam scheduling is one of the most important issues regarding data relay satellite systems,which can improve the utilization efficiency of limited system resources by programming beam allocation for relay missions.The...Beam scheduling is one of the most important issues regarding data relay satellite systems,which can improve the utilization efficiency of limited system resources by programming beam allocation for relay missions.The ever-increasing relay missions create a substantial challenge for beam scheduling due to an increase in different mission demands.The cooperative usage of different beams further increases the complexity of this problem.Therefore,we develop a novel optimization method to solve the beam scheduling problem for the scenario of various mission demands in the data relay satellite system(DRSS).Based on the analysis of mission demands and resource features,we first construct a heterogeneous parallel machines scheduling model to formulate the beam scheduling problem in the DRSS.To solve this complicated model,we investigate the matching method between mission demands and beam resources,and introduce two concepts,the loose duration and the number of available beams,to make the matching process more effective.Then,the following three algorithms are proposed.Our first approach,the maximized completion probability algorithm(MCPA),applies a greedy strategy based on the new concepts to allocate beams for missions;and two improved versions of this algorithm are also presented,which employ the strategies of mission insertion optimization and mission sequence optimization,respectively.Our simulation results show that the proposed algorithms are superior to the existing algorithms in terms of the scheduled missions,the weight of scheduled missions,and the processing time,which significantly improves the performance of beam scheduling in the DRSS.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.
文摘针对基于载噪比(carrier to noise ratio,CNR)的GNSS干扰源定位,在存在多个干扰源、多径传输且接收机间距较远时定位难度大、精度低的问题,提出了一种加权K均值(K-Means)聚类算法与基于差分接收信号强度(differential received signal strength,DRSS)的方程解算定位相结合的多干扰源定位方法.在假设干扰源个数确定以及单个接收机只受到一个干扰源影响的前提下,设计了改进的加权K-Means聚类算法实现对多个干扰源位置的初步估计.为了进一步降低在观测接收机相距较远时加权K-Means方法的定位误差,在聚类后选取各簇内受干扰影响显著的接收CNR构建基于DRSS的定位方程组,通过方程解算得到更加精细的定位结果.仿真结果表明,所提出的定位方案可以实现对多干扰源的定位,结合DRSS参数定位后,单干扰源场景下定位误差可降低19%以上,存在两个单音干扰源的场景下定位误差可降低38%以上.
基金This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800in part by the National Natural Science Foundation of China under Grant 61922050.
文摘Beam scheduling is one of the most important issues regarding data relay satellite systems,which can improve the utilization efficiency of limited system resources by programming beam allocation for relay missions.The ever-increasing relay missions create a substantial challenge for beam scheduling due to an increase in different mission demands.The cooperative usage of different beams further increases the complexity of this problem.Therefore,we develop a novel optimization method to solve the beam scheduling problem for the scenario of various mission demands in the data relay satellite system(DRSS).Based on the analysis of mission demands and resource features,we first construct a heterogeneous parallel machines scheduling model to formulate the beam scheduling problem in the DRSS.To solve this complicated model,we investigate the matching method between mission demands and beam resources,and introduce two concepts,the loose duration and the number of available beams,to make the matching process more effective.Then,the following three algorithms are proposed.Our first approach,the maximized completion probability algorithm(MCPA),applies a greedy strategy based on the new concepts to allocate beams for missions;and two improved versions of this algorithm are also presented,which employ the strategies of mission insertion optimization and mission sequence optimization,respectively.Our simulation results show that the proposed algorithms are superior to the existing algorithms in terms of the scheduled missions,the weight of scheduled missions,and the processing time,which significantly improves the performance of beam scheduling in the DRSS.
