Multi-Target Localization Based on Unidentified Multiple RSS/AOA Measurements in Wireless Sensor Networks

Kang, Seyoung; Kim, Tae Hyun; Chung, Wonzoo

doi:10.3390/s21134455

Cited by 7 publications

(5 citation statements)

References 36 publications

(59 reference statements)

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“…It was basically updated from the Okumura-Hata model to permit space for adjusting factors regarding the environment type. The pathloss for this type is in (11) [23]:…”

Section: Ericsson Modelmentioning

confidence: 99%

“…Typically, the specifics of the RF signal environment are not identified. Thus, the most efficient model that is taken into account all the considerations [11], [12]. Various propagation models have been used and classified into: firstly, the empirical models, which depend on the measurement for a specific region, such as log-distance, Okumura models, and Hata models.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis for a suitable propagation model in outdoor with 2.5 GHz band

et al. 2023

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As demand for mobile wireless network services continues to rise, network planning and optimization significantly affect development. One of the critical elements in network planning is predicting pathloss. Thus, propagation models predict pathloss in indoor and outdoor environments. Choosing the appropriate propagation model for the area out of existing models is essential for network planning. Selected propagation models suitable with 2.5GHz, such as Friis Free Space Propagation Model (FSPL), Sandford University Interim (SUI), Ericsson, Okumura, and COST-231 HATA models, are utilized for evaluation and compared with empirical data collected from long-term evolution (LTE) networks in urban areas. The best acceptable model is chosen based on statistical results such as mean, standard deviation, and root mean square errors (RMSE). The analytical results show Cost-231 Hata model fits the empirical pathloss with a minimum RMSE of 5.27 dB.

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“…It was basically updated from the Okumura-Hata model to permit space for adjusting factors regarding the environment type. The pathloss for this type is in (11) [23]:…”

Section: Ericsson Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis for a suitable propagation model in outdoor with 2.5 GHz band

et al. 2023

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“…Node localization algorithms in WSNs are mainly divided into range-based localization algorithm and range-free localization algorithm [15]. The range-based localization algorithm mainly included RSSI [16], TOA [17], TDOA [18], and angle of arrival (AOA) [19]. The range-free localization algorithm included centroid [20], weighted centroid, DV-Hop, amorphous [21], and APIT localization algorithm [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Node Localization Algorithm Based on Modified Archimedes Optimization Algorithm in Wireless Sensor Networks

2022

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Node localization information plays an important role in wireless sensor networks (WSNs). To solve the problem of low localization accuracy of distance vector hop (DV-Hop) localization algorithm in wireless sensor networks, an improved localization algorithm called MAOADV-Hop based on the modified Archimedes optimization algorithm (MAOA) and DV-Hop is proposed, which can achieve the balance between the localization speed and the localization precision. Firstly, tent chaotic mapping and particle swarm optimization (PSO) algorithm are introduced into Archimedes optimization algorithm to improve the initial population diversity and change the update rules of density and volume, which improve the global convergence ability and convergence speed of the algorithm. Secondly, the MAOA is used to replace the least square part of the DV-Hop localization algorithm to improve the localization accuracy of the algorithm. Finally, MAOADV-Hop is verified through four different network environments and compared with DE_DV-Hop, BOA_DV-Hop, and DV-Hop. The simulation results show that the localization speed of the proposed approach is faster than that of DE_DV-Hop and BOA_DV-Hop, and the localization error is less than that of DV-Hop, DE_DV-Hop, and BOA_DV-Hop.

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“…In a passive sensor network, there may be multiple signal sources, and before accurate localization, one should first correctly associate observations regarding the same sources [9]. The multi-dimension assignment model is a classical method for data association in passive sensor networks [10][11][12], but it needs the locations of signal sources, which is unavailable before correct data association.…”

Section: Introductionmentioning

confidence: 99%

Signal Source Positioning Based on Angle-Only Measurements in Passive Sensor Networks

Chen

Wang

Zhou

et al. 2022

Sensors

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Some passive sensors can measure only directions of arrival of signals, but the real positions of signal sources are often desirable, which can be estimated by combining distributed passive sensors as a network. However, passive observations should be correctly associated first. This paper studies the multi-target data association and signal localization problem in distributed passive sensor networks. With angle-only measurements from distributed passive sensors, multiple lines in a 3-dimensional (3D) scenario can be built and then those that will intersect in a small volume in 3D are classified into the same source. The center of the small volume is taken as an estimate of the signal source position, whose statistical distributions are formulated. If the minimum distance is less than an association threshold, then two lines are considered to be from the same signal source. In numerical results, the impacts of angle measurement accuracy and platform self-positioning accuracy are analyzed, indicating that this method can achieve a prescribed data association rate and a high positioning performance with a low computation cost.

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Multi-Target Localization Based on Unidentified Multiple RSS/AOA Measurements in Wireless Sensor Networks

Cited by 7 publications

References 36 publications

Performance analysis for a suitable propagation model in outdoor with 2.5 GHz band

Performance analysis for a suitable propagation model in outdoor with 2.5 GHz band

Node Localization Algorithm Based on Modified Archimedes Optimization Algorithm in Wireless Sensor Networks

Signal Source Positioning Based on Angle-Only Measurements in Passive Sensor Networks

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