Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning

The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.

show abstract

“…In Ref. [54], the fingerprinting technique is combined with a Kalman filter to estimate trajectories in indoor environments.…”

mentioning

confidence: 99%

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

et al. 2019

View full text Add to dashboard Cite

show abstract

“…This system is used to localize and identify the car in the parking area. In another study, the two indoor localization techniques using machine learning algorithms are used to improve the indoor localization, that is, dead reckoning (DR) and data fusion [ 37 ]. In the first method, the DR technique uses an inertial sensor to improve the robustness and continuity of the indoor localization.…”

Section: Related Workmentioning

confidence: 99%

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Jamil

Iqbal

Ahmad

et al. 2020

Sensors

View full text Add to dashboard Cite

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

show abstract

“…In fingerprinting scheme, to estimate the position of the MU, various matching algorithms have been proposed [8]. There are probabilistic methods [40,41], deterministic methods [42,43], and neural networks [44,45]. Among them, the Nearest Neighbor (NN) method is the most basic deterministic matching algorithm [9].…”

Section: Wifi-based Navigationmentioning

confidence: 99%

Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF

et al. 2019

View full text Add to dashboard Cite

Indoor navigation has been developing rapidly over the last few years. However, it still faces a number of challenges and practical issues. This paper proposes a novel WiFi/MEMS integration structure for indoor navigation. The two-stage structure uses the extended Kalman filter (EKF) to fuse the information from WiFi/MEMS sensors and contains attitude-determination EKF and position-tracking EKF. In the WiFi part, a partition solution called “moving partition” is originally proposed in this paper. This solution significantly reduces the computation time and enhances the performance of the traditional Weighted K-Nearest Neighbors (WKNN) method. Furthermore, the direction measurement is generated utilizing WiFi positioning results, and a “turn detection” is implemented to guarantee the effectiveness. The navigation performance of the presented integration structure has been verified through indoor experiments. The test results indicate that the proposed WiFi/MEMS solution works well. The root mean square (RMS) position error of WiFi/MEMS is 0.7926 m, which is an improvement of 20.59% and 36.60% when compared to MEMS and WiFi alone. Besides, the proposed algorithm still performs well with very few access points (AP) available and its stability has been proven.

show abstract

Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning

Cited by 36 publications

References 61 publications

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF

Contact Info

Product

Resources

About