Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting

In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floor-level two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and AI (GCA'18

RSS-based wireless LAN indoor localization and tracking using deep architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA

Cooperative Localization in Mines Using Fingerprinting and Neural Networks

This work is a special investigation in the localization of users in underground and confined areas such as gold mines. It sheds light on the basic approaches that are used nowadays to estimate the position and track users using wireless technology. Localization or Geo-location in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery suffer severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting the channel impulse response fingerprints with reference to one wireless receiver and using an artificial neural network as the matching algorithm. In this work we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints allows us to create artificial neural networks that work separately or cooperatively using the same localization technique. In this approach, the received signals by the mobile at various distances are analysed and several components of each signal are extracted accordingly. The channel impulse response found at each position is unique to the position of the receiver. The parameters extracted from the CIR are the received signal strength, mean excess delay, root mean square, maximum excess delay, the number of multipath components, the total power of the received signal, the power of the first arrival and the delay of the first arrival. The use of multiple fingerprints from multiple references not only adds diversity to the set of inputs fed to the neural network but it also enhances the overall concept and makes it applicable in a multi-access environment. Localization is analyzed in the presence of two receivers using several position estimation procedures. The results showed that using two CIRs in a cooperative localization technique gives a position accuracy less than or equal to 1m for 90% of both trained and untrained neural networks. Another way of using cooperative intelligence is by using the time domain including tracking, probabilities and previous positions to the localization system. Estimating new positions based on previous positions recorded in history has a great improvement factor on the accuracy of the localization system where it showed an estimation error of less than 50cm for 90% of training data and 65cm for testing data. The details of those techniques and the estimation errors and graphs are fully presented and they show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points as well as using tracking improves significantly the accuracy, precision, scalability and the overall performance of the localization system

Experimental study on RSS based indoor positioning algorithms

This work compares the performance of indoor positioning systems suitable for low power wireless sensor networks. The research goal is to study positioning techniques that are compatible with real-time positioning in wireless sensor networks, having low-power and low complexity as requirements. Map matching, approximate positioning (weighted centroid) and exact positioning algorithms (least squares) were tested and compared in a small predefined indoor environment. We found that, for our test scenario, weighted centroid algorithms provide better results than map matching. Least squares proved to be completely unreliable when using distances obtained by the one-slope propagation model. Major improvements in the positioning error were found when body influence was removed from the test scenario. The results show that the positioning error can be improved if the body effect in received signal strength is accounted for in the algorithms.Helder D. Silva is supported by the Portuguese Foundation for Science and Technology under the grant SFRBD/78018/2011.info:eu-repo/semantics/publishedVersio

Penentuan Lokasi Wireless Device Berbasis 3d Access Point Location Based

Perkembangan penggunaan wireless saat ini telah mengubah cara hidup manusia. Dengan menganalisa gelombang yang diterima dari pemancar menuju sebuah perangkat tersebut. Untuk itu dibutuhkan sebuah model yang mampu memprediksi lokasi dari sebuah perangkat penerima. Pada penelitian ini, dikembangkan suatu metode untuk penentuan lokasi terhadap sebuah perangkat di dalam ruangan. Penelitian ini menerapkan konsep neural network dengan mendeteksi sinyal wireless yang ada di sekitar perangkat penerima. Sinyal-sinyal tersebut kemudian dikirimkan menuju server untuk kemudian diproses lebih lanjut. Proses terbagi menjadi dua, yaitu learning dan production. Pada tahap learning, sistem akan membentuk sebuah model yang akan mampu beradaptasi dengan kombinasi input dan output yang telah diberikan sebelumnya. Dengan memanfaatkan konsep Parallel Resilient Back Propagation, hasil akurasi yang diberikan pada penelitian ini mencapai 89%

Penentuan Lokasi Wireless Device Berbasis 3d Access Point Location Based

Perkembangan penggunaan wireless saat ini telah mengubah cara hidup manusia. Dengan menganalisa gelombang yang diterima dari pemancar menuju sebuah perangkat tersebut. Untuk itu dibutuhkan sebuah model yang mampu memprediksi lokasi dari sebuah perangkat penerima. Pada penelitian ini, dikembangkan suatu metode untuk penentuan lokasi terhadap sebuah perangkat di dalam ruangan. Penelitian ini menerapkan konsep neural network dengan mendeteksi sinyal wireless yang ada di sekitar perangkat penerima. Sinyal-sinyal tersebut kemudian dikirimkan menuju server untuk kemudian diproses lebih lanjut. Proses terbagi menjadi dua, yaitu learning dan production. Pada tahap learning, sistem akan membentuk sebuah model yang akan mampu beradaptasi dengan kombinasi input dan output yang telah diberikan sebelumnya. Dengan memanfaatkan konsep Parallel Resilient Back Propagation, hasil akurasi yang diberikan pada penelitian ini mencapai 89%

Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m