A New Regional Localization Method for Indoor Sound Source Based on Convolutional Neural Networks

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

“…In fact, the other aforementioned communication solutions require specialized infrastructure (wireless radio beacons) to be installed in the indoor environment and extra equipment in the devices. Because the signal characteristics are strongly related to the distance between the transmitter and the receiver, they can be used to perform localization [13,14,15]. Usually, the easy to obtain parameters are the Received Signal Strength Indicator (RSSI) [16,17,18], Channel State Information (CSI) [19,20], Angle Of Arrival (AOA) [14,21], Time Of Arrival (TOA) [22], and time difference of arrival [23].…”

Section: Introductionmentioning

confidence: 99%

“…This is motivated by the highly-efficient Deep Learning (DL) algorithms, which have been demonstrated to show very good performance in different contexts and applications related to the indoor localization field: LOS/NLOS identification [19,27], activity recognition [28], uncertainty prediction [29], denoising autoencoders [30], and localization [31,32]. These DL-based methods have been widely introduced into indoor localization, estimating either the location coordinates or other localization information such as room identification [31], floor identification [17], region identification [13,14,33], etc. Sound-based localization systems have been proposed in [13,14], ensuring a region identification prediction.…”

Section: Introductionmentioning

confidence: 99%

Deep CNN for Indoor Localization in IoT-Sensor Systems

Njima

Ahriz

Zayani

et al. 2019

Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.

“…Table 1 illustrates part of papers that use ANN in localization within the year of 2018. 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.…”

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%

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.