Indoor positioning technology is one of the cornerstones for many services in Internet-of-Things (IoT) systems. However, the existing indoor positioning systems are still suffering from several issues, such as unstable positioning accuracy, a high system complexity, and a high deployment cost. To tackle these issues, this article presents a Particle Filter-based Indoor Positioning System (PFIPS) that can localize and track a tag that broadcasts Bluetooth Low Energy (BLE) beacon messages to BLE receivers. The proposed PFIPS uses a Kalman Filter to preprocess collected Received Signal Strength Indication (RSSI) information in order to smooth the fluctuated RSSI data. It also designs an effective Particle Filter (PF) to approximate the location of a tag, which gradually reduces the location uncertainties in a Gaussian belief space. To show the applicability of our PFIPS, we have developed PFIPS in a testbed based on commercial off-the-shelf (COTS) devices. Through intensive simulations and experiments, our experiment results show that our PFIPS outperforms the legacy indoor positioning systems in terms of location accuracy by 24.1% and achieves median accuracy of 1.16 m.

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Particle Filtering-Based Indoor Positioning System for Beacon Tag Tracking

Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing these processes; however, due to the human factor involved, information on products, vehicles and workers may be missing, corrupt, or misleading. In this paper, a cost-effective Indoor Positioning System (IPS) based on Bluetooth Low Energy (BLE) technology is presented for use in Intralogistics that works automatically, and therefore minimizes the possibility of acquiring incorrect data. The proposed IPS solution is intended to be used for supervising order-picker movements, movement of packages between workstations, and tracking other mobile devices in a manually operated warehouse. Only data that are accurate, reliable and represent the actual state of the system, are useful for detailed material flow analysis and optimization in Intralogistics. Using the developed solution, IPS technology is leveraged to enhance the manually operated warehouse operational efficiency in Intralogistics. Due to the hardware independence, the developed software solution can be used with virtually any BLE supported beacons and receivers. The results of IPS testing in laboratory/office settings show that up to 98% of passings are detected successfully with time delays between approach and detection of less than 0.5 s.

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Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

Smartphone-based indoor navigation systems are becoming increasingly popular in a variety of applications. However, localization accuracy has always been a challenge. The Kalman filter (KF) is a well-known estimation in the Bayesian framework, but can only deal with linear problems and Gaussian models. A particle filter (PF) is another essential estimation tool in a Bayesian system. However, a critical challenge with PF is the problem of particle degradation after resampling. To mitigate the particle degradation problem in PF, unsupervised learning based on k-means clustering is proposed in this paper. It forms clusters of similar particles based on the sum of weights. Also, we present enhancing the PF by utilizing a map constraint and k-means clustering (PFMK) and integrating Bluetooth low energy (BLE) along with pedestrian dead reckoning (PDR) for positioning. BLE and PDR-based positioning with a map constraint lead to an increase in accuracy of at least 20% compared with a traditional PF. Moreover, the proposed unsupervised k-means approach increases the accuracy by an additional 20%, whereas the overall performance of PFMK achieves a mean error of <1.5 m in the test environments.

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Floor Map-Aware Particle Filtering Based Indoor Navigation System

In this work, a novel algorithm based on machine learning to tackle the navigation problem of Unmanned Aerial Vehicle (UAV) in satellite-less surroundings is presented. The proposed network is trained by exploiting the outputs of a Generalized Trust Region Sub-problem (GTRS) method, after which a deep Long Short-Term Memory (LSTM) model is applied to predict the drone’s position. We present here our preliminary findings which indicate high potential and utility of machine learning tools for the problem of interest. More precisely, the results reveal improved accuracy, while matching the execution time of the GTRS method. Moreover, the proposed method also reveals lower susceptibility to noise; in practice, these two factors combined can be the difference between the UAV colliding with an obstacle or not. 

Applying Deep Neural Networks to Improve UAV Navigation in Satellite-less Environments

In this work, a novel algorithm based on machine learning to tackle the navigation problem of Unmanned Aerial Vehicle (UAV) in satellite-less surroundings is presented. The proposed network is trained by exploiting the outputs of a Generalized Trust Region Sub-problem (GTRS) method, after which a deep Long Short-Term Memory (LSTM) model is applied to predict the drone’s position. We present here our preliminary findings which indicate high potential and utility of machine learning tools for the problem of interest. More precisely, the results reveal improved accuracy, while matching the execution time of the GTRS method. Moreover, the proposed method also reveals lower susceptibility to noise; in practice, these two factors combined can be the difference between the UAV colliding with an obstacle or not.

Beom Hwang

Papers

Particle Filtering-Based Indoor Positioning System for Beacon Tag Tracking

Design and Implementation of Indoor Positioning System Using Particle Filter Based on Wireless Signal Intensity