This paper presents new pseudolinear estimation algorithms for angle-of-arrival (AOA) target motion analysis when the sensor locations are not precisely known. Sensor location errors can cause significant performance degradation for the existing pseudolinear estimator (PLE) and its variants. In particular, we analytically show that, in the presence of sensor location errors, the well-known weighted instrumental variable estimator (WIVE) is no longer asymptotically unbiased due to the non-vanishing correlation between the instrumental variable matrix and the pseudolinear noise vector. To ameliorate this bias problem, a novel bias compensation method is developed for the WIVE, which is proven to be approximately efficient for a large number of measurements under the small noise assumption. In addition, a selective-angle-measurement (SAM) strategy is integrated into the bias-compensated WIVE (BCWIVE) to strengthen the correlation between the instrumental variable matrix and the measurement matrix, alleviating the performance degradation in large AOA and sensor location noises, and unfavourable geometries. The performance advantages of the proposed BCWIVE and SAM-BCWIVE over the PLE, its WIVE variants and the maximum likelihood estimator are demonstrated by way of extensive simulation examples.

中文翻译：

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存在传感器定位误差的 AOA 伪线性目标运动分析

当传感器位置不准确时，本文提出了用于到达角 (AOA) 目标运动分析的新伪线性估计算法。传感器位置错误会导致现有伪线性估计器 (PLE) 及其变体的性能显着下降。特别是，我们通过分析表明，在存在传感器位置误差的情况下，众所周知的加权工具变量估计器 (WIVE) 不再是渐近无偏的，因为工具变量矩阵和伪线性噪声向量之间存在非零相关性。为了改善这个偏差问题，为 WIVE 开发了一种新颖的偏差补偿方法，该方法被证明在小噪声假设下对大量测量近似有效。此外，选择性角度测量 (SAM) 策略被集成到偏置补偿 WIVE (BCWIVE) 中，以加强仪器变量矩阵和测量矩阵之间的相关性，缓解大迎角和传感器位置噪声以及不利几何形状下的性能下降. 所提出的 BCWIVE 和 SAM-BCWIVE 相对于 PLE、其 WIVE 变体和最大似然估计器的性能优势通过广泛的仿真示例得到了证明。