Indoor Location Tracking System Research Articles

Bias and Deviation Map-Based Weighted Graph Search for NLOS Indoor RTLS Calibration

Recently, UWB-based technology providing centimeter-level accuracy has been developed and widely utilized in indoor real-time location tracking systems. However, location accuracy varies due to factors such as frequency interference, collisions, reflected signals, and whether line-of-sight (LOS) conditions are met, and it can be challenging to ensure high accuracy in specific environments. Fortunately, when anchor positions are fixed, the locations of large obstacles such as columns or furniture remain relatively stable, leading to similar patterns of positioning bias at specific points. This study proposes an algorithm that corrects inaccurate positioning to more closely reflect the actual location based on bias and deviation maps generated using natural neighbor interpolation. Initially, positioning bias and deviations at specific points are sampled, and bias and deviation maps are created using natural neighbor interpolation. During location tracking, the algorithm detects candidate clusters and determines the centroid to estimate the actual location by applying the bias and deviation maps to the measured positions derived through trilateration. To validate the proposed algorithm, experiments were conducted in a non-LOS (NLOS) indoor environment. The results demonstrate that the proposed algorithm can reduce the positioning bias of a UWB-based RTLS by approximately 71.34% compared to an uncalibrated system.

Experimental Evaluation of Sensor Fusion of Low-Cost UWB and IMU for Localization under Indoor Dynamic Testing Conditions.

Autonomous systems usually require accurate localization methods for them to navigate safely in indoor environments. Most localization methods are expensive and difficult to set up. In this work, we built a low-cost and portable indoor location tracking system by using Raspberry Pi 4 computer, ultra-wideband (UWB) sensors, and inertial measurement unit(s) (IMU). We also developed the data logging software and the Kalman filter (KF) sensor fusion algorithm to process the data from a low-power UWB transceiver (Decawave, model DWM1001) module and IMU device (Bosch, model BNO055). Autonomous systems move with different velocities and accelerations, which requires its localization performance to be evaluated under diverse motion conditions. We built a dynamic testing platform to generate not only the ground truth trajectory but also the ground truth acceleration and velocity. In this way, our tracking system's localization performance can be evaluated under dynamic testing conditions. The novel contributions in this work are a low-cost, low-power, tracking system hardware-software design, and an experimental setup to observe the tracking system's localization performance under different dynamic testing conditions. The testing platform has a 1 m translation length and 80 μm of bidirectional repeatability. The tracking system's localization performance was evaluated under dynamic conditions with eight different combinations of acceleration and velocity. The ground truth accelerations varied from 0.6 to 1.6 m/s2 and the ground truth velocities varied from 0.6 to 0.8 m/s. Our experimental results show that the location error can reach up to 50 cm under dynamic testing conditions when only relying on the UWB sensor, with the KF sensor fusion of UWB and IMU, the location error decreases to 13.7 cm.

Automated workflow analysis in vegetable grafting using an Ultra-Wide Band based real-time indoor location tracking system

• Automatically extracting information for workflow analysis from raw sensory data. • Developing a framework for workflow analysis using Ultra-Wide Band technology. • Identifying worker and task associations based on the location data. • Automatically calculating flow time, processing time, and waiting time from Gantt charts. • Achieving 90.54% accuracy in validating the proposed algorithms against the video data. Traditional workflow analysis requires intensive manual processes to extract meaningful information from raw sensory data. A novel framework for workflow analysis is developed based on a real-time indoor location tracking system utilizing Ultra-Wide Band technology. The proposed framework is applied to identify worker and task associations in a labor-intensive horticultural plant production process for grafted vegetable seedlings, based on location data. It involves binary segmentation of different distance measures calculated from location data to detect the plant tray’s movements and worker association in the vegetable grafting operation. An iterative overlap time removal algorithm is then implemented to ensure that no worker is assigned to overlapping processes, and no task is processed by multiple workers simultaneously. Key workflow metrices such as flow time, processing time, and waiting times are automatically calculated. Automatically generated Gantt charts and bar plots provide valuable insights into the workflow for management and help them identify improvement opportunities. Validation of results with video data show that the proposed workflow analysis is 90.5% accurate.

A Dynamic HMM-Based Real-Time Location Tracking System Utilizing UHF Passive RFID

In this paper, an indoor real-time location tracking system is proposed using ultra-high frequency passive radio frequency identification technology. A dynamic k nearest neighbor driven hidden Markov model is developed to track subsequent positions of moving objects by combining static localization with dynamic tracking. Statistically validated regression models are developed to relate the tags’ received signal strength indicator and distance, which are iteratively updated over time to accommodate environmental changes. These models are utilized to generate a fingerprint database, which is used by the dynamic k nearest neighbor to estimate the set of potential locations for targeted tagged objects. The probabilistic likelihoods of tags’ potential locations based on the current location and the received signal strength indicator(s) are used to construct the hidden Markov model’s transition and emission matrixes, respectively. Finally, the performance of the proposed framework is evaluated by a set of experiments given different factors, such as movement pattern and speed. Results reveal that our proposed framework can track objects with an average tracking error as low as 0.36m.

Improved indoor location tracking system for mobile nodes

Improved indoor location tracking system for mobile nodes

Improved Indoor Location Tracking System for Mobile Nodes

The solutions to the problem of the tracking a wireless node is approached conventionally by: 1) proximity detection; 2) triangulation; 3) scene analysis methods. In these, scene analysis method is simple, accurate and less expensive. Indoor localisation technologies need to address the existing inaccuracy and inadequacy of global positioning-based systems (GPS) in indoor environments (such as urban canyons, inside large buildings, etc.). This paper presents a novel indoor Wi-Fi tracking system with minimal error in the presence of barrier using Bayesian inference method. The system integrates an android app and python scripts (that run on server) to identify the position of the mobile node within an indoor environment. The received signal strength indicator (RSSI) method is used for tracking. Experimental results presented to illustrate the performance of the system comparing with other methods. From the tracked nodes, a theoretical solution is proposed for finding shortest path using Steiner nodes.

Design and Analysis of a Quasi-Yagi Antenna for an Indoor Location Tracking System

In this paper, a quasi-Yagi antenna for an indoor location tracking system is proposed. The performance of the proposed antenna was verified by testing it using an indoor location tracking system. To improve the bandwidth and gain, two parasitic directors were added near the dipole. The performance verification of the proposed antenna is explained, along with a performance comparison of the VSWR (voltage standing wave ratio) radiation pattern and the realized gain. The proposed antenna was connected to an NVA-R661 module of Xethru Inc. for indoor location tracking. The proposed antenna exhibited a wide bandwidth of 4.36 GHz by satisfying a VSWR ≤ 2 from 5.03 to 9.39 GHz, the maximum gain was 6.46 dBi in the 8 GHz band. The radiation pattern exhibited a good directivity characteristic within the proposed band. The location tracking result of a moving target clearly describes the route of the target along a moving line.

Ephemeral ID Beacon-Based Improved Indoor Positioning System

Recently, the rapid development of mobile devices and communication technologies has dramatically increased the demand for location-based services that provide users with location-oriented information and services. User location in outdoor spaces is measured with high accuracy using GPS. However, because the indoor reception of GPS signals is not smooth, this solution is not viable in indoor spaces. Many on-going studies are exploring new approaches for indoor location measurement. One popular technique involves using the received signal strength indicator (RSSI) values from the Bluetooth Low Energy (BLE) beacons to measure the distance between a mobile device and the beacons and then determining the position of the user in an indoor space by applying a positioning algorithm such as the trilateration method. However, it remains difficult to obtain accurate data because RSSI values are unstable owing to the influence of elements in the surrounding environment such as weather, humidity, physical barriers, and interference from other signals. In this paper, we propose an indoor location tracking system that improves performance by correcting unstable RSSI signals received from BLE beacons. We apply a filter algorithm based on the average filter and the Kalman filter to reduce the error range of results calculated using the RSSI values.

동작 중 채널오차 보상 기능을 갖는 UWB 기반 무선위치추적 필터

In this paper, a filter that can simultaneously estimate location of a mobile node and channel-specific errors (CSE) is proposed for ultra-wideband (UWB)-based indoor location tracking system. This filter can be used to correct the errors caused by not performing the calibration process prior to the location tracking service and to track the location of the mobile node. For this, we first define the channel common error (CCE) and CSE, and design location tracking filters that include each error as state variable. As a result of analyzing the observability of each filter, CCE model-based filter (CCE-F) shows faster estimation performance than CSE model-based filter (CSE-F), and CSE-F has more accurate error estimation performance than CCE-F. Taking this into consideration, in this paper, a sequential model change (SMC) filter that can compensate the errors fast and accurately is proposed by using the CCE-F and CSE-F sequentially. The superior performance of the proposed filter is analyzed through some simulation and confirmed through experiments.

Accurate Location Tracking From CSI-Based Passive Device-Free Probabilistic Fingerprinting

The research on indoor localization has received great interest in recent years. This has been fueled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. In this paper, we present an accurate device-free passive (DfP) indoor location tracking system that adopts channel state information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for multiple input multiple output orthogonal frequency-division multiplexing PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-the-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of nonequipped individuals.

Implementation of Sex Crime Prevention Systems Using the Indoor Location Tracking System

Implementation of Sex Crime Prevention Systems Using the Indoor Location Tracking System

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and mobile node, has been discussed in this work, interested to the indoor positioning applications. Developing an indoor location tracking system based on received signal strength indicator (RSSI) of WSN is considered cost effective and the simplest method. The suitable technique for estimating position out of RSSI measurements is the extended Kalman filter (EKF) which is especially used for non linear data as RSSI. In order to reduce the estimated errors from EKF algorithm, this work adopted forward data processing of the EKF algorithm to improve the accuracy of the filtering output, its called iterated extended Kalman filter (IEKF). However, using IEKF algorithm should know the stopping criterion value that is influenced to the maximum number iterations of this system. The number of iterations performed will be affected to the computation time although it can improve the estimation position. In this paper, we propose modified IEKF for mobile cooperative tracking system within only 4 iterations number. The ilustrated results using RSSI measurements and simulation in MATLAB show that our propose method have capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with conventional IEKF algorithm with MSE 1.09 m. The time computation perfomance of our propose method achived in 3.55 seconds which is better than adding more iteration process.

Implementing Campus Indoor Location Tracking System

Digital electronic maps are used to track the location in outdoor and indoor environment. Most of electronic maps are useful for outdoor environment. There is no efficiency technology for search the indoor location. Many smart-phones are used to track the location by electronic map. The electronic maps may be the Google map, GPS navigation, waze and offline GPS maps. These are only useful for outdoor environment. Indoor based location tracking system can be implemented by using the Indoor Atlas Android SDK. It provides an API for developers to create application for inside building navigation. This app also useful for vision it have impaired people because the speech recognition for searched location and also include the event details in indoor based location tracking application. Using this app to find the shortest path for desired location.

Smartphone-Based Real-Time Indoor Location Tracking With 1-m Precision.

Monitoring the activities of daily living of the elderly at home is widely recognized as useful for the detection of new or deteriorating health conditions. However, the accuracy of existing indoor location tracking systems remains unsatisfactory. The aim of this study was, therefore, to develop a localization system that can identify a patient's real-time location in a home environment with maximum estimation error of 2m at a 95% confidence level. A proof-of-concept system based on a sensor fusion approach was built with considerations for lower cost, reduced intrusiveness, and higher mobility, deployability, and portability. This involved the development of both a step detector using the accelerometer and compass of an iPhone 5, and a radio-based localization subsystem using a Kalman filter and received signal strength indication to tackle issues that had been identified as limiting accuracy. The results of our experiments were promising with an average estimation error of 0.47m. We are confident that with the proposed future work, our design can be adapted to a home-like environment with a more robust localization solution.

무선 환경의 위치 정보 오차 개선을 위한 스마트폰 센서 기반 실내 위치 추적 시스템

무선 환경의 위치 정보 오차 개선을 위한 스마트폰 센서 기반 실내 위치 추적 시스템

An indoor locationtracking system for smart parking

There are always frustrations for drivers in finding parking space and parking is costly in almost every major city in the world. In this paper, we propose a crowdsourcing solution by exploiting sensors in the smartphone to collect real-time parking availability information. In particular, we utilise a pedestrian dead reckoning (PDR) system to track the driver's trajectory to detect when he is about to leave his parking space. However, the effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we implement a waist-mounted-based PDR method on a smartphone that can measure the user's moving distance with a high accuracy. Furthermore, we design a map-matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.

The proposed System for Indoor Location Tracking

Indoor location tracking systems are used to locate people or certain objects in buildings and in closed areas. For example, finding co-workers in a large office building, locating customers within a shopping mall and locating patients in the hospital are a few applications of indoor location tracking systems. Indoor tracking capability opens up multiple possibilities. To address this need, this paper describes the implementation of a Bluetooth-based indoor location tracking system that utilizes the integrated Bluetooth modules in any today's mobile phones to specify and display the location of the individuals in a certain building. The proposed system aims for location tracking/monitoring and marketing applications for whom want to locate individuals carrying mobile phones and advertise products and services.

Speech Interaction Analysis on Collaborative Work at an Elderly Care Facility

Nursing and care are central aspects of healthcare services, and improvements in the quality and efficiency of healthcare processes are important issues. Since these tasks involve both physical actions and information processing, they can be described as what the authors call “action-oriented intellectual services.” There are striking mismatches between such services and current information and communication technology systems, which are generally designed as tools for deskwork. In addition, almost all nursing and care services are realized by the collaborative work of multiple staff members in distributed locations in the field, a situation with which conventional communication media are of limited utility. A smart voice tweet system for nursing and care is proposed to overcome these problems. To realize this technology, one needs to understand how staff communicates to realize collaborative work in healthcare domains. The authors therefore observed bathing assistance, night shift operations, and handover tasks at a private elderly care home for 8 days. The authors collected approximately 400 h of recorded speech, 42,000 transcribed utterances, data from an indoor location-tracking system, and handwritten notes by human observers. The authors also analyzed speech interactions in the bathing assistance task. The authors found that (1) staff members were almost always speaking during tasks, (2) remote communication was rare, (3) about 75% of utterances were spoken to the residents, (4) the intended recipient of utterances was frequently switched, and (5) about 17% of utterances contained personal names. The authors also attempted clustering utterances into what the authors call “passages”, and about 33% of passages contained only one personal name. These results should be applicable in semi-automatic long-term care record taking.

Smartphone-based indoor pedestrian tracking using geo-magnetic observations

With the widespread use of smartphones, the use of location-based services LBS with smartphones has become an active research issue. The accurate measurement of user location is necessary to provide LBS. While outdoor locations are easily obtained with GPS, indoor location information is difficult to acquire. Previous work on indoor location tracking systems often relied on infrastructures that are influenced by environmental changes and temporal differences. Several studies have proposed infrastructure-less systems that are independent of the surroundings, but these works generally required non-trivial computation time or energy costs. In this paper, we propose an infrastructure-less pedestrian tracking system in indoor environments. The system uses accelerometers and magnetic sensors in smartphones without pre-installed infrastructure. We reduced the cumulative error of location tracking by geo-magnetic observations at corners and spots with magnetic fluctuations. In addition, we developed a robust estimation model that is tolerant to false positives, as well as a mobility model that reflects the characteristics of multiple sensors. Extensive evaluation in a real environment indicates that our system is accurate and cost-effective.

Smartphone-Based Indoor Pedestrian Tracking Using Geo-Magnetic Observations

With the widespread use of smartphones, the use of location-based services (LBS) with smartphones has become an active research issue. The accurate measurement of user location is necessary to provide LBS. While outdoor locations are easily obtained with GPS, indoor location information is difficult to acquire. Previous work on indoor location tracking systems often relied on infrastructures that are influenced by environmental changes and temporal differences. Several studies have proposed infrastructure-less systems that are independent of the surroundings, but these works generally required non-trivial computation time or energy costs. In this paper, we propose an infrastructure-less pedestrian tracking system in indoor environments. The system uses accelerometers and magnetic sensors in smartphones without pre-installed infrastructure. We reduced the cumulative error of location tracking by geo-magnetic observations at corners and spots with magnetic fluctuations. In addition, we developed a robust estimation model that is tolerant to false positives, as well as a mobility model that reflects the characteristics of multiple sensors. Extensive evaluation in a real environment indicates that our system is accurate and cost-effective.