In this letter, received signal strength (RSS) based target localization is studied. First we start with the case of known emitted power of target. The least squares (LS) approach is adopted, and the object function is shown to be decomposed as a convex quadratic plus a concave term. Consequently, a Majorization-Minimization (MM) based approach from range measurements termed R-MM is proposed, which iteratively finds the local optimum through simple operations. To further improve the performance, unconstrained squared-range (USR)-based LS estimation is used as an initial point for R-MM, named R-MM-USR. Then R-MM-USR is extended to solve the target localization under unknown emitted power through alternating minimization. For both cases Cramèr Rao bound (CRB) is derived. In addition, the computation complexity of R-MM-USR is lowest among the high accuracy approaches. Besides, numerical simulations are conducted to demonstrate the near optimal performance, compared with CRB and other state-of-art methods.

中文翻译：

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距离测量中基于主最小化的目标定位问题

在这封信中，研究了基于接收信号强度 (RSS) 的目标定位。首先，我们从已知目标发射功率的情况开始。采用最小二乘法（LS），将目标函数分解为凸二次加凹项。因此，提出了一种称为 R-MM 的基于距离测量的主要化-最小化 (MM) 方法，该方法通过简单的操作迭代地找到局部最优。为了进一步提高性能，基于无约束平方范围 (USR) 的 LS 估计被用作 R-MM 的初始点，称为 R-MM-USR。然后将 R-MM-USR 扩展为通过交替最小化来解决未知发射功率下的目标定位。对于这两种情况，推导出 Cramèr Rao 界 (CRB)。此外，在高精度方法中，R-MM-USR 的计算复杂度最低。此外，与 CRB 和其他最先进的方法相比，进行了数值模拟以证明接近最佳的性能。