A Unified Analytical Framework for RSS-Based Localization Systems

He, Jiajun; Chun, Young Jin; So, Hing Cheung

doi:10.1109/jiot.2021.3114232

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…However, these models are only applicable to specific scenarios, which limit their adaptability. He et al [6] simulated the RSS in the presence of shadowing and NLOS propagation using κµ shadowed fading and examined their impacts on RSSbased localization, where the κ-µ shadowed fading can be utilized to simulate different types of wireless channels by adjusting the channel parameters κ, µ, and m. The work of [7] indicates that the background noise should also be considered in characterizing the RSS. This noise can be modeled in different ways, such as additive Gaussian noise or impulsive noise model based on the α-stable distribution [8].…”

Section: A Related Workmentioning

confidence: 99%

Received Signal Strength Characterization in Mixed LOS and NLOS Environments

Wang,

He,

Xiong

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

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The received signal strength (RSS) measurement is widely adopted in wireless positioning due to its simplicity and accessibility. This paper utilizes the Boolean Line Model (BLM) to characterize the RSS distribution considering both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, where the reflectors are modeled as line segments with random lengths and inclination angles. We begin by calculating the probability of establishing a specular reflection link for a single reflector with a given transmitter-receiver pair. By leveraging this probability, we compute the distribution of RSS for a single NLOS link. By convolving the RSS distributions from the LOS and multiple NLOS paths, the RSS distribution in mixed LOS and NLOS conditions is derived. Theoretical developments are validated by comparing simulations and the experimental data collected from real-world scenarios.

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Section: A Related Workmentioning

confidence: 99%

Received Signal Strength Characterization in Mixed LOS and NLOS Environments

Wang,

He,

Xiong

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

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“…Sun et al tackled the RSS-based localization problem in mixed line-of-sight and non-line-of-sight environments by developing a robust weighted least squares method with an iterative approach [43]. He et al proposed a unified analytical framework for RSS-based localization and developed a tractable expression for the localizability model [44].…”

Section: Related Workmentioning

confidence: 99%

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Mei,

Miao,

Wang

et al. 2024

JMSE

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Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

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“…One is to subtract a reference equation from the remaining equations [36,37], and the other is to define a new quantity to be estimated [38]. The WLLS estimator avoids the nonconvex issue in the ML estimation and has an analytical solution [39,40]. As analysed in refs.…”

Section: Introductionmentioning

confidence: 99%

Pseudo conditional distribution induced radio source localisation using received signal strength measurements

Zhang,

Duan

2023

IET Radar Sonar & Navi

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In received signal strength (RSS) based radio source localisation, RSS measurements can be converted to the squared distance estimates between the emission source and the sensors to construct a system of pseudolinear equations, allowing for the use of the weighted linear least squares (WLLS) estimators for location estimation. The WLLS estimators are widely applied in practice because of their simplicity and computational efficiency. Nevertheless, the major challenge of this approach lies in estimating the squared distance from RSS measurements governed by the log‐normal shadowing effect. A pseudo conditional distribution (PCD) of the squared distance between the emission source and the sensor is introduced first, given the RSS measurement at each sensor. Then, the authors propose a series of new WLLS location estimators, using three typical statistical characteristics, that is, mean, median, and mode, of the PCD. Analysis of their estimation performance are also provided through performance rankings in terms of their mean square errors and covariances. It is found that estimation performance of the PCD‐induced WLLS estimators heavily depends on the choice of the statistical characteristic of the PCD and different choices lead to estimators with better, worse, or equal performance. Numerical examples show that the proposed mode‐WLLS estimator always performs better than the existing WLLS estimators, and also better than the existing convex optimisation based algorithms in most cases but with much less computations.

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A Unified Analytical Framework for RSS-Based Localization Systems

Cited by 6 publications

References 43 publications

Received Signal Strength Characterization in Mixed LOS and NLOS Environments

Received Signal Strength Characterization in Mixed LOS and NLOS Environments

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Pseudo conditional distribution induced radio source localisation using received signal strength measurements

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