UniSA STEM, University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA 5095, Australia.
Defence Science & Technology Group, Cyber and Electronic Warfare Division, Edinburgh, SA 5111, Australia.
Sensors (Basel). 2021 Oct 28;21(21):7159. doi: 10.3390/s21217159.
This paper investigates the hybrid source localization problem using differential received signal strength (DRSS) and angle of arrival (AOA) measurements. The main advantage of hybrid measurements is to improve the localization accuracy with respect to a single sensor modality. For sufficiently short wavelengths, AOA sensors can be constructed with size, weight, power and cost (SWAP-C) requirements in mind, making the proposed hybrid DRSS-AOA sensing feasible at a low cost. Firstly the maximum likelihood estimation solution is derived, which is computationally expensive and likely to become unstable for large noise levels. Then a novel closed-form pseudolinear estimation method is developed by incorporating the AOA measurements into a linearized form of DRSS equations. This method eliminates the nuisance parameter associated with linearized DRSS equations, hence improving the estimation performance. The estimation bias arising from the injection of measurement noise into the pseudolinear data matrix is examined. The method of instrumental variables is employed to reduce this bias. As the performance of the resulting weighted instrumental variable (WIV) estimator depends on the correlation between the IV matrix and data matrix, a selected-hybrid-measurement WIV (SHM-WIV) estimator is proposed to maintain a strong correlation. The superior bias and mean-squared error performance of the new SHM-WIV estimator is illustrated with simulation examples.
本文研究了使用差分接收信号强度（DRSS）和到达角（AOA）测量的混合源定位问题。混合测量的主要优势在于相对于单个传感器模态提高了定位精度。对于足够短的波长，AOA 传感器可以根据尺寸、重量、功率和成本（SWAP-C）要求进行构建，使得提出的混合 DRSS-AOA 传感能够以低成本实现。首先推导出了最大似然估计解，该解计算成本高，并且对于较大的噪声水平可能变得不稳定。然后，通过将 AOA 测量值纳入 DRSS 方程的线性化形式，开发了一种新颖的闭式伪线性估计方法。该方法消除了与线性化 DRSS 方程相关的讨厌参数，从而提高了估计性能。检查了由于将测量噪声注入伪线性数据矩阵而引起的估计偏差。采用工具变量法来减小这种偏差。由于所得加权工具变量（WIV）估计器的性能取决于 IV 矩阵和数据矩阵之间的相关性，因此提出了一种选择混合测量 WIV（SHM-WIV）估计器来保持较强的相关性。通过仿真示例说明了新的 SHM-WIV 估计器在偏差和均方误差性能方面的优越性。
