The direct position determination (DPD) method can provide high localization performance than conventional two-step localization methods. However, the existing DPD methods only consider the scenario of parameters of the receiving arrays, and the localization performance decreases dramatically when the array model is inaccurate in practice. This paper studies the problem for positioning a stationary emitter in the presence of sensor gain and phase errors (SGPEs) aided by calibration sources. To remove these negative effects caused by SGPEs, calibration sources with known positions are introduced. The extended relationship between parameters of calibration sources and errors is used to establish a structural objective function based on the maximum likelihood estimate. The calibration parameters are jointly optimized with target-related parameters and an alternating iterative algorithm is then developed to decouple the multidimensional search into several low-dimensional optimizations. We also derive the Cramér–Rao bound (CRB) to evaluate the performance of the proposed method. Simulation results demonstrate that the proposed method outperforms the existing DPD methods and two-step methods, which incorporates the error information, and the accuracy attains the associated CRB.

中文翻译：

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使用校准源针对传感器增益和相位误差进行可靠的直接位置确定

与传统的两步定位方法相比，直接位置确定 (DPD) 方法可以提供更高的定位性能。然而，现有的DPD方法只考虑了接收阵列参数的场景，当阵列模型在实践中不准确时，定位性能会急剧下降。本文研究了在校准源辅助的传感器增益和相位误差 (SGPE) 中定位固定发射器的问题。为了消除 SGPE 造成的这些负面影响，引入了具有已知位置的校准源。校准源参数与误差之间的扩展关系用于建立基于最大似然估计的结构目标函数。校准参数与目标相关参数联合优化，然后开发交替迭代算法以将多维搜索解耦为几个低维优化。我们还推导出 Cramér-Rao 界（CRB）来评估所提出方法的性能。仿真结果表明，所提出的方法优于现有的 DPD 方法和两步法，其中包含了误差信息，并且精度达到了相关的 CRB。