Robust Localization System Using Vector Combination in Wireless Sensor Networks

Kang, Inhye; Nam, Haewoon

doi:10.1109/access.2022.3190409

Cited by 3 publications

(5 citation statements)

References 31 publications

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“…If all nodes have a routing path to each other, the coordinate transformation of all AOAs can be conducted using mathematical techniques [21]. After the coordinate transformation of all AOAs, the vectors between all nodes can be recalculated in a single global coordinate system, thereby facilitating the estimation of the position of all nodes [9]. The method of recalculating vectors in this process is briefly introduced below.…”

Section: Vector-based Methodsmentioning

confidence: 99%

“…The previously discussed conventional vector-based localization method [9] lacks the ability to ensure precise positioning accuracy in large networks. This limitation arises from the direct conversion of vector information into location data, disregarding the reliability of the information, which consequently results in cumulative errors during the vector synthesis process.…”

Section: Proposed Localization Methodsmentioning

confidence: 99%

“…To evaluate the positioning performance, we compared the proposed positioning method with existing positioning methods such as CMDS, MDS-MAP(C,R), and EHLS [9], while varying various environmental factors (e.g., distance error, angle error, the proportion of vector nodes, size of the network, the number of nodes and communication range). In addition, the positioning accuracy of the proposed localization method was analyzed by changing the computational complexity (e.g., the number of iterations and refinement range), with the aim of evaluating performance based on computational complexity.…”

Section: A Simulation Environmentsmentioning

confidence: 99%

“…The angle-based method (e.g. RAST) has a very high positioning error due to the node with the 1-edge connectivity [9]. The angle-based method needs each node to connect with at least two neighboring nodes to calculate the position.…”

Section: A Simulation Environmentsmentioning

confidence: 99%

“…In the case of large-scale WSNs, it is challenging to obtain angle information between nodes that do not have direct communication links. To address this issue, a vector combination-based positioning method was proposed in [9], which is capable of operating in such environments. But, the process of simply combining vectors leads to the accumulation of vector errors that have an adverse impact on the positioning performance.…”

Section: Introductionmentioning

confidence: 99%

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Iterative Vector-Based Localization in a Large Heterogeneous Sensor Network

Kang,

Nam

2024

IEEE J. Sel. Areas Sensors

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This paper proposes a novel iterative vectorbased localization method in a large heterogeneous sensor network, where a subset of nodes possesses the capability to measure both distance and angle information, while the others are only limited to distance measurements. Unlike conventional vector-based positioning methods that assume all nodes can measure both distance and angle, our approach tackles a more realistic scenario where some nodes are limited to distance-only measurements. To address the challenges of the node localization in a heterogeneous sensor network, the proposed positioning method calculates vector information between the nodes that are not directly communicated and aligns it with a reference coordinate. In addition, the proposed method employs an iterative calculation, such as least-squares minimization, thereby achieving high positioning accuracy. Simulation results demonstrate that the proposed positioning method outperforms the conventional distance-based positioning method in environments with low angle measurement errors, exhibiting up to 44% higher positioning accuracy. Furthermore, the proposed positioning method shows 24% higher positioning accuracy compared to the conventional vector-based positioning method.

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Section: Vector-based Methodsmentioning

confidence: 99%

Section: Proposed Localization Methodsmentioning

confidence: 99%

Section: A Simulation Environmentsmentioning

confidence: 99%

Section: A Simulation Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iterative Vector-Based Localization in a Large Heterogeneous Sensor Network

Kang,

Nam

2024

IEEE J. Sel. Areas Sensors

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Minimum-Spanning-Tree-Based Time Delay Estimation Robust to Outliers

Yamaoka,

Nakashima,

Wakabayashi

et al. 2023

IEEE Access

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In this paper, we present a novel approach to estimating multiple time delays (TDs) in sensor arrays that is robust to outliers of TD measurements. These measurements are typically obtained from the peak of the cross correlation of two sensor signals but may contain outliers due to noise, significantly degrading the performance of downstream applications. To address this issue, we propose an approach to leverage only the best minimum-necessary TD measurements. First, we describe the general properties of TDs and show that the degree of freedom of TDs is the number of sensors minus one for a signal source, which indicates that the full set of TDs is redundant in this case. We then consider selecting nonredundant TDs from all measurements given the necessary and sufficient condition to reconstruct all TDs uniquely. We represent this condition by utilizing the graph theory, and then, formulate an optimization problem to select the optimal nonredundant TDs while satisfying the condition above. We reduce this problem to the problem of finding a minimum spanning tree and propose an efficient algorithm for TD estimation. Experimental evaluation shows that our method successfully eliminates outliers while ensuring that all TDs can be restored.

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Hybrid PDA/FIR Filtering for Indoor Localization Using Wireless Sensor Networks

Pak

2022

Electronics

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Indoor localization systems using wireless sensor networks (WSNs) are widely used to track the positions of workers, robots, and equipment. In indoor spaces, the occasional obstruction of radio propagation by physical objects such as furniture, appliances, and humans is referred to as the non-line-of-sight (NLOS) problem and has been a challenge for indoor localization. In this study, a new indoor localization algorithm to overcome the NLOS problem is proposed. We propose a new method to use redundant fixed nodes and nearest neighbor (NN) measurements, which increases the probability of avoiding NLOS-contaminated measurements. In addition, we propose a novel localization algorithm that can handle the contaminated measurements as clutters. The proposed algorithm is based on the hybrid filtering structure in which probabilistic data association (PDA) filter and a finite impulse response (FIR) filter are used as main and assisting filters, respectively. We adopt the extended minimum variance FIR (EMVF) filter as an assisting FIR filter, which recovers the main PDA filter from failures. Thus, the resulting filter is referred to as hybrid PDA/FIR filter (HPFF). Extensive simulations using an indoor localization scenario in a long corridor were performed for evaluation of the proposed localization algorithm. The EKF using NN measurements improves localization accuracy under temporary NLOS conditions, and the PDA filter further enhances the localization accuracy of EKF. However, EKF and PDA filter cannot completely overcome NLOS problem and exhibit significant increase in errors under certain conditions. The HPFF produced localization accuracy with the root time-averaged mean square (RTAMS) position error under 0.4 m and did not fail under NLOS conditions. The accurate and reliable localization performance of HPFF was demonstrated in comparison with the EKF and PDA filter through extensive WSN-based indoor localization simulations.

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Robust Localization System Using Vector Combination in Wireless Sensor Networks

Cited by 3 publications

References 31 publications

Iterative Vector-Based Localization in a Large Heterogeneous Sensor Network

Iterative Vector-Based Localization in a Large Heterogeneous Sensor Network

Minimum-Spanning-Tree-Based Time Delay Estimation Robust to Outliers

Hybrid PDA/FIR Filtering for Indoor Localization Using Wireless Sensor Networks

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