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Direct position determination based on unitary space-time subspace data fusion

机译:基于酉空时子空间数据融合的直接位置确定

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Direct position determination (DPD) is a single-step method which directly localizes transmitters from sensor outputs without computing intermediate parameters. Compared with conventional two-step localization methods, DPD achieves higher accuracy especially under low signal to noise ratio (SNR) conditions. This paper proposes an improved subspace data fusion (SDF)-based DPD algorithm with a moving array. Different from the existing SDF-based DPD which uses only the arrival angle information, the proposed algorithm exploits the location information embedded in both arrival angles and Doppler shifts. It relies on a unitary space-time SDF, where multiple stationary transmitters are directly localized by fusing all the unitary (real-valued) space-time subspaces at all positions of the moving array. Therefore, our algorithm realizes the localization by real-valued computations. The simulation results show the superior localization performance of the proposed DPD compared to the existing SDF-based DPD and two-step localization algorithms.
机译:直接位置确定(DPD)是一种单步方法,它直接从传感器输出中直接定位发射器而不计算中间参数。与传统的两步定位方法相比,DPD尤其在低信噪比(SNR)条件下实现更高的精度。本文提出了一种具有移动阵列的改进的子空间数据融合(SDF)的DPD算法。与仅使用到达角信息的基于SDF的DPD不同,所提出的算法利用嵌入到达角度和多普勒班次中的位置信息。它依赖于酉空时SDF,其中多个静止发射器通过熔化移动阵列的所有位置处的所有单一(实值)时分子空间来直接定位。因此,我们的算法通过实际计算实现了本地化。与现有的基于SDF的DPD和两步定位算法相比,仿真结果显示了所提出的DPD的卓越定位性能。

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