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一种基于RSS-AOA的异构锚节点定位的简单高效方法。

A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes.

作者信息

Ding Weizhong, Li Lincan, Chang Shengming

机构信息

School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China.

出版信息

Sensors (Basel). 2025 Mar 24;25(7):2028. doi: 10.3390/s25072028.

Abstract

Accurate and reliable localization is crucial for various wireless communication applications. A multitude of studies have presented accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different anchor nodes, which may not accurately reflect real-world scenarios with varying noise distributions. In this paper, we propose a simple and efficient localization method based on hybrid RSS-AOA measurements that accounts for the varying measurement noises of different anchor nodes. We develop a closed-form estimator for the target location employing the linear-weighted least squares (LWLS) algorithm, where the weight of each LWLS equation is the inverse of its residual variance. Due to the unknown variances of LWLS equation residuals, we employ a two-stage LWLS method for estimation. The proposed method is computationally efficient, adaptable to different types of wireless communication systems and environments, and provides more accurate and reliable localization results compared to existing RSS-AOA localization techniques. Additionally, we derive the Cramer-Rao lower bound (CRLB) for the RSS-AOA signal sequences used in the proposed method. Simulation results demonstrate the superiority of the proposed method.

摘要

准确可靠的定位对于各种无线通信应用至关重要。众多研究提出了使用混合接收信号强度(RSS)和到达角(AOA)测量的精确定位方法。然而,这些研究通常假设不同锚节点的测量噪声分布相同,而这可能无法准确反映具有不同噪声分布的实际场景。在本文中,我们提出了一种基于混合RSS - AOA测量的简单高效定位方法,该方法考虑了不同锚节点测量噪声的变化。我们采用线性加权最小二乘法(LWLS)算法为目标位置开发了一种闭式估计器,其中每个LWLS方程的权重是其残差方差的倒数。由于LWLS方程残差的方差未知,我们采用两阶段LWLS方法进行估计。所提方法计算效率高,适用于不同类型的无线通信系统和环境,并且与现有的RSS - AOA定位技术相比,提供了更准确可靠的定位结果。此外,我们推导了所提方法中使用的RSS - AOA信号序列的克拉美 - 罗下界(CRLB)。仿真结果证明了所提方法的优越性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d25/11991503/fec81be7397e/sensors-25-02028-g001.jpg

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