Abstract The problem of direct position determination (DPD) using a single moving array in the presence of deterministic sensor gain and phase errors is considered. To eliminate the localization bias caused by these errors, an eigenstructure based self-calibrating DPD method is first introduced, in which the sensor gain and phase errors and the emitter positions are jointly estimated by an iterative process. Considering the performance deterioration of eigenstructure methods when the signal to noise ratio or the number of samples is not sufficiently large, a maximum likelihood (ML) based two-step self-calibration approach for DPD is subsequently proposed. The sensor gain errors are provided using the diagonal of the covariance matrix of the array output by a closed form solution at the first step. Then, the phase errors and the emitter positions are jointly estimated by an iterative scheme based on ML, in which the phase errors are also determined by a closed form solution in each iteration. Besides, detailed analyses and discussions about the differences between the introduced eigenstructure based and the proposed ML based self-calibration DPD methods are also provided. At last, numerical simulations are involved to examine their performance.

中文翻译：

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使用具有传感器增益和相位误差的单个移动阵列进行自校准直接位置确定

摘要 考虑在存在确定性传感器增益和相位误差的情况下使用单个移动阵列的直接位置确定 (DPD) 问题。为了消除这些误差引起的定位偏差，首先引入了一种基于特征结构的自校准 DPD 方法，其中传感器增益和相位误差以及发射器位置通过迭代过程联合估计。考虑到信噪比或样本数量不够大时特征结构方法的性能恶化，随后提出了一种基于最大似然（ML）的DPD两步自校准方法。传感器增益误差是使用阵列输出的协方差矩阵的对角线通过第一步的封闭形式解提供的。然后，相位误差和发射器位置由基于 ML 的迭代方案联合估计，其中相位误差也由每次迭代中的闭合形式解确定。此外，还提供了关于引入的基于特征结构的自校准 DPD 方法与提出的基于 ML 的自校准 DPD 方法之间差异的详细分析和讨论。最后，通过数值模拟来检验它们的性能。