Improved selective hybrid RSS/AOA weighting schemes for NLOS localization

Gazzah, Leïla; Najjar, Leila; Besbes, Hichem

doi:10.1109/icmcs.2014.6911298

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…The noisy angle measurement,

{\hat{θ}}_{i j},

is given by

{\hat{θ}}_{i j} = \arctan (][, \frac{()(, {\overset{false¯}{y}}_{j} - y_{i})}{()(, {\overset{false¯}{x}}_{j} - x_{i})}) + m_{i j} + ϕ_{i j},

where

m_{i j}

represents the Gaussian noise in angle estimate with mean zero and variance

α_{i j}^{2}

, i.e.

m_{j i} \sim N ()(, 0, α_{i j}^{2})

and

ϕ_{i j}

represents the angular deviation introduces by the NLoS component of the signal [26]. The term

δ_{i j} = 0.5 α_{i j}^{2}

is called the unbiasing constant.…”

Section: Measurement and Linear Least Square Modelmentioning

confidence: 99%

Localisation of wireless nodes with partial connectivity in wireless sensor systems

Khan

Hafeez

2020

IET wirel. sens. syst.

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“…The noisy angle measurement,

{\hat{θ}}_{i j},

is given by

{\hat{θ}}_{i j} = \arctan (][, \frac{()(, {\overset{false¯}{y}}_{j} - y_{i})}{()(, {\overset{false¯}{x}}_{j} - x_{i})}) + m_{i j} + ϕ_{i j},

where

m_{i j}

represents the Gaussian noise in angle estimate with mean zero and variance

α_{i j}^{2}

, i.e.

m_{j i} \sim N ()(, 0, α_{i j}^{2})

and

ϕ_{i j}

represents the angular deviation introduces by the NLoS component of the signal [26]. The term

δ_{i j} = 0.5 α_{i j}^{2}

is called the unbiasing constant.…”

Section: Measurement and Linear Least Square Modelmentioning

confidence: 99%

Localisation of wireless nodes with partial connectivity in wireless sensor systems

Khan

Hafeez

2020

IET wirel. sens. syst.

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“…A more effective solution is to choose only those UAVs for which LOS/OLOS conditions exist. For the urban area, a similar approach is also used in other location techniques, e.g., [35,36].…”

Section: Modification Of Location Algorithmmentioning

confidence: 99%

Cooperative system of emission source localization based on SDF

Kelner

2018

2018 International Conference on Military Communications and Information Systems (ICMCIS)

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Efficient and precise location of emission sources in an urbanized environment is very important in electronic warfare. Therefore, unmanned aerial vehicles (UAVs) are increasingly used for such tasks. In this paper, we present the cooperation of several UAVs creating a wireless sensor network (WSN) that locates the emission source. In the proposed WSN, the location is based on spectrum sensing and the signal Doppler frequency method. The paper presents the concept of the system. Simulation studies are used to assess the efficiency of the cooperative WSN. In this case, the location effectiveness for the WSN is compared to the single UAV.

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“…However, these schemes suffer from poor localization accuracy due to additional signal attenuation resulting from transmission through walls and other big obstacles and severe RSS fluctuation due to multipath fading and indoor noise [14,15,16]. Moreover, the NLOS propagation property may seriously degrade the performance of AOA-based and TOA-based indoor localization due to the difficulty of detecting the direct signal path and measuring the time of flight of the signal, respectively [17]. Thus, the metric arrived at using the AOA, RSS or TOA solely is vulnerable to environmental changes [13].…”

Section: Introductionmentioning

confidence: 99%

Advanced Heterogeneous Feature Fusion Machine Learning Models and Algorithms for Improving Indoor Localization

Zhang

Xiao

Yang

et al. 2019

Sensors

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In the era of the Internet of Things and Artificial Intelligence, the Wi-Fi fingerprinting-based indoor positioning system (IPS) has been recognized as the most promising IPS for various applications. Fingerprinting-based algorithms critically rely on a fingerprint database built from machine learning methods. However, currently methods are based on single-feature Received Signal Strength (RSS), which is extremely unstable in performance in terms of precision and robustness. The reason for this is that single feature machines cannot capture the complete channel characteristics and are susceptible to interference. The objective of this paper is to exploit the Time of Arrival (TOA) feature and propose a heterogeneous features fusion model to enhance the precision and robustness of indoor positioning. Several challenges are addressed: (1) machine learning models based on heterogeneous features, (2) the optimization of algorithms for high precision and robustness, and (3) computational complexity. This paper provides several heterogeneous features fusion-based localization models. Their effectiveness and efficiency are thoroughly compared with state-of-the-art methods.

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Improved selective hybrid RSS/AOA weighting schemes for NLOS localization

Cited by 8 publications

References 7 publications

Localisation of wireless nodes with partial connectivity in wireless sensor systems

Localisation of wireless nodes with partial connectivity in wireless sensor systems

Cooperative system of emission source localization based on SDF

Advanced Heterogeneous Feature Fusion Machine Learning Models and Algorithms for Improving Indoor Localization

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