An Indoor Localization Algorithm Based on RBF Neural Network Optimized by the Improved PSO

Gong, Yang; Yu, Jian; Sun, Congyi

doi:10.12783/dtcse/iceiti2016/6173

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Empirical models perform well in terms of processing time and memory efficiency, although they are less compatible with sudden changes in the propagation environment [40,41]. Flexible neural network solutions can be used to model the relationship between the predicted and measured RSSI values and have been demonstrated in [42][43][44]. Their advantage over threshold-based detection methods is in their ability to adapt based on the observed data rather than on analytical and theoretical models of a system [45,46].…”

Section: Introductionmentioning

confidence: 99%

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

Naghdi

O’Keefe

2022

Sensors

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The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.

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Section: Introductionmentioning

confidence: 99%

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

Naghdi

O’Keefe

2022

Sensors

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A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning

Niu

2021

IJGI

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As the demand for location services increases, research on location technology has aroused great interest. In particular, signal-based fingerprint location positioning technology has become a research hotspot owing to its high positioning performance. In general, the received signal strength indicator (RSSI) will be used as a location feature to build a fingerprint database. However, at different locations, this feature distinction may not be obvious, resulting in low positioning accuracy. Considering the wavelet transform can get valuable features from the signals, the long-term evolution (LTE) signals were converted into wavelet feature images to construct the fingerprint database. To fully extract the signal features, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. A deep residual network (ResNet) rough locator is used to learn useful features from the wavelet feature fingerprint image database. Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization performance. Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system. Experimental results indicate that the proposed system leads to improved positioning performance in outdoor environments.

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An Indoor Localization Algorithm Based on RBF Neural Network Optimized by the Improved PSO

Cited by 2 publications

References 2 publications

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning

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