Adaptive Neural Fuzzy Inference System for Accurate Localization of Wireless Sensor Network in Outdoor and Indoor Cycling Applications

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.

Section: Related Workmentioning

confidence: 99%

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

Jamil

Iqbal

Ahmad

et al. 2020

“…At present, there are two main research areas on IPS. One is based on radio frequency communication technology, such as radio frequency identification (RFID) [ 2 ], wireless sensor network (WSN) [ 3 ], ultra-wideband (UWB) [ 4 ], wireless fidelity (WiFi) [ 5 ], Bluetooth [ 6 ], etc. The other is based on visible light communication (VLC) [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication

Chen

et al. 2020

The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.

“…The majority of works use wireless measurements as the input, while some use data from camera [12], LiDAR [13], inertial [14], and sound [15] sensors. For wireless measurements, RSS (e.g., RSS from WiFi [16], BLE [17], ZigBee [18], RFID [19], cellular [20], and photodiode [21]), RSS features (e.g., two-dimensional RSS map [22], differential RSS [23], and RSS statistics [24]), channel information (e.g., the channel state information (CSI) [25] and channel impulse response (CIR) [26]), and angle-of-arrival (AoA) [27] have been used. These measurements are used for various purposes (i.e., outputs) through ANN.…”

Section: Introductionmentioning

confidence: 99%

“…These measurements are used for various purposes (i.e., outputs) through ANN. The majority of works directly output two-dimensional or three dimensional locations, while the other works also output information such as attitude angles [28], floor identifications [24], room identifications [16], region identifications [15], AoA [29], distances [18], step lengths [14], and moving status [30]. Additionally, data quality and status indexes such as non-line-of-sight (NLoS) [25], similarity of fingerprints [31] and images [32], localization errors [13] and localization success rate [33] may be generated from ANN.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning

Гао

et al. 2019

Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian distribution (GD)-based method. The predicted location uncertainty is evaluated and further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF). Indoor walking test results indicated the possibility of predicting the wireless fingerprinting uncertainty through ANN the effectiveness of setting measurement noises adaptively in the integrated localization EKF.