Extended Gradient Predictor and Filter for smoothing RSSI

Subhan, Fazlı; Ahmed, S.; Ashraf, Khalid

doi:10.1109/icact.2014.6779148

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Therefore, in practical applications, the use of preprocessing techniques on raw collected data helps the RSSI to be a suitable estimator of distance [ 26 ]. Rough localization is performed using RSSI, which is the parameter used to estimate distance between mobile and anchor nodes or APs [ 27 ]. The position information of the robot and the MAC address and RSSI of different APs are recorded at the recording positions.…”

Section: System Overview and Methodologymentioning

confidence: 99%

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation

Shi

Zhang

Yao

et al. 2018

Sensors

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In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.

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Section: System Overview and Methodologymentioning

confidence: 99%

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation

Shi

Zhang

Yao

et al. 2018

Sensors

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“…The effectiveness of the proposed extended Gradient RSSI predictor and filter is presented by comparing it with the original Gradient filter using ideal and simulated RSSI measurements [14]. Ideal RSSI measurements reflect the real-time RSSI measurements obtained with respect to time, while simulated RSSI measurements reflect the presence of communication holes.…”

Section: Comparative Analysis Of the Proposed Extended Gradient Rssi mentioning

confidence: 99%

“…Plot of RSSI measurements which are filtered using the proposed and actual Gradient filter (case 3)[14] …”

mentioning

confidence: 99%

Extended Gradient RSSI Predictor and Filter for Signal Prediction and Filtering in Communication Holes

Subhan

Ahmed

Ashraf

et al. 2015

Wireless Pers Commun

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In wireless communication, received signal strength indicator (RSSI) is a measurement of power in the received signal. This parameter is mainly used in radio frequency (RF) based distance estimation, position estimation, and handover mechanisms. In real-time applications such as object tracking, a handover mechanism, distance estimations require continuous reception of RSSI measurements in order to accurately estimate the position of the object. However, there are some constraints such as signal attenuation, signal loss, multi-path effects. These parameters cause variations in RSSI, and hence it becomes weak, which produces communication holes. Communication holes refer to the phenomenon when the signal drops to zero, and there is a communication blockage, which is practically negligible and can be considered as disconnection. This communication hole occurs due to various factors such as multi-path effects, noise, moving out of the coverage zone, device manufacturing faults, and inquiry procedure as well. The main focus of this paper is to address the issue of communication holes in Bluetooth networks. In Bluetooth networks communication holes also occur due to the delay in the inquiry procedure. Therefore, an existing gradient filter is modified based on the most recent measurements received. The proposed extended gradient filter is tested using simulated and real time RSSI measurements and compared with Kalman Filter and Kalman Smoother. The results show better performance in case of communication holes occurrence.

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“…In [11], the authors conducted a survey regarding localization techniques, mentioned that GPS and cellular networks are outdoor localization sources, and they failed to localize anything indoor because of the deep shadowing effect. In [12], Subhan et al proposed an extended gradient predictor and filter to reduce variation in RSSI values. e RSSI values get variation due to various factors such as walls, obstacles, human crowd, and temperature.…”

Section: Related Workmentioning

confidence: 99%

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Khan

Bangash

et al. 2021

Security and Communication Networks

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Internet of Things (IoT), an emerging technology, is becoming an essential part of today’s world. Machine learning (ML) algorithms play an important role in various applications of IoT. For decades, the location information has been extremely useful for humans to navigate both in outdoor and indoor environments. Wi-Fi access point-based indoor positioning systems get more popularity, as it avoids extra calibration expenses. The fingerprinting technique is preferred in an indoor environment as it does not require a signal’s Line of Sight (LoS). It consists of two phases: offline and online phase. In the offline phase, the Wi-Fi RSSI radio map of the site is stored in a database, and in the online phase, the object is localized using the offline database. To avoid the radio map construction which is expensive in terms of labor, time, and cost, machine learning techniques may be used. In this research work, we proposed a hybrid technique using Cuckoo Search-based Support Vector Machine (CS-SVM) for real-time position estimation. Cuckoo search is a nature-inspired optimization algorithm, which solves the problem of slow convergence rate and local minima of other similar algorithms. Wi-Fi RSSI fingerprint dataset of UCI repository having seven classes is used for simulation purposes. The dataset is preprocessed by min-max normalization to increase accuracy and reduce computational speed. The proposed model is simulated using MATLAB and evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM). Moreover, the simulation results show that the proposed model achieves high accuracy of 99.87%.

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Extended Gradient Predictor and Filter for smoothing RSSI

Cited by 10 publications

References 12 publications

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation

Extended Gradient RSSI Predictor and Filter for Signal Prediction and Filtering in Communication Holes

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

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