Department of Electronics and Telecommunication, Amrutvahini College of Engineering, Sangamner 422608, Maharashtra, India.
Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.
Sensors (Basel). 2022 Jan 4;22(1):358. doi: 10.3390/s22010358.
Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.
基于三边测量的目标定位在无线传感器网络（WSN）中使用接收信号强度（RSS）通常会由于室内环境中 RSS 测量的高度波动而导致不准确的位置估计。提高 RSS 系统中的定位精度一直是大量研究的重点。本文提出了两种基于 RSS 测量的无距离算法，即支持向量回归（SVR）和 SVR+卡尔曼滤波器（KF）。与三边测量不同，所提出的基于 SVR 的定位方案可以直接使用现场测量值估计目标位置，而无需依赖距离的计算。与广义回归神经网络（GRNN）等其他最先进的定位和跟踪（L&T）方案不同，SVR 定位架构仅需三个 RSS 测量值即可定位移动目标。此外，基于 SVR 的定位方案与 KF 融合，以进一步细化目标位置估计。进行了严格的模拟测试，以测试所提出算法在噪声射频（RF）信道和动态目标运动模型下的定位效果。受益于 SVR 的良好泛化能力，模拟结果表明，所提出的基于 SVR 的定位算法在室内定位性能方面优于基于三边测量和 GRNN 的定位方案。
