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Searching for objects is a common task in daily life and work. For augmented reality (AR) devices without spatial perception systems, the image of the object's last appearance serves as a common search assistance. Compared to using only images as visual cues, videos capturing the process of object placement can provide procedural guidance, potentially enhancing users' search efficiency. However, complete video playback capturing the entire object placement process as visual cues can be excessively lengthy, requiring users to invest significant viewing time. To explore whether segmented or accelerated video playback can still assist users in object retrieval tasks effectively, we conducted a user study. The results indicated that when video playback is covering the first appearance of the object's destination to the object's final appearance (referred to as the destination appearance, DA) and playing at normal speed, search time and cognitive load were significantly reduced. Subsequently, we designed a second user study to evaluate the performance of video playback compared to image cues in object retrieval tasks. The results showed that combining the DA playback starting point with images of the object's last appearance further reduced search time and cognitive load. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, people's demands on precise indoor positioning are increasing. Wireless sensor network, as the most commonly used indoor positioning sensor, performs a vital part for precise indoor positioning. However, in indoor positioning, obstacles and other uncontrollable factors make the localization precision not very accurate. Ultra-wide band (UWB) can achieve high precision centimeter-level positioning capability. Inertial navigation system (INS), which is a totally independent system of guidance, has high positioning accuracy. The combination of UWB and INS can not only decrease the impact of non-line-of-sight (NLOS) on localization, but also solve the accumulated error problem of inertial navigation system. In the paper, a fused UWB and INS positioning method is presented. The UWB data is firstly clustered using the Fuzzy C-means (FCM). And the Z hypothesis testing is proposed to determine whether there is a NLOS distance on a link where a beacon node is located. If there is, then the beacon node is removed, and conversely used to localize the mobile node using Least Squares localization. When the number of remaining beacon nodes is less than three, a robust extended Kalman filter with M-estimation would be utilized for localizing mobile nodes. The UWB is merged with the INS data by using the extended Kalman filter to acquire the final location estimate. Simulation and experimental results indicate that the proposed method has superior localization precision in comparison with the current algorithms. [ABSTRACT FROM AUTHOR]

An important capability of most smart, Internet-of-Things-enabled spaces (e.g., office, home, hospital, factory) is the ability to leverage context of use. Location is a key context element, particularly indoor location. Recent advances in radio ranging technologies, such as Wi-Fi RTT, promise the availability of low-cost, near-ubiquitous time-of-flight-based ranging estimates. In this paper, we build on prior work to enhance this ranging technology's ability to provide useful location estimates. For further improvements, we model user motion behavior to estimate the user motion state by taking the temporal measurements available from time-of-flight ranging. We select the velocity parameter of a particle-filter-based on this motion state. We demonstrate meaningful improvements in coordinate-based estimation accuracy and substantial increases in room-level estimation accuracy. Furthermore, insights gained in our real-world deployment provide important implications for future Internet-of-Things applications and their supporting technology deployments such as social interaction, workflow management, inventory control, or healthcare information tools. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Searching for objects is a common task in daily life and work. For augmented reality (AR) devices without spatial perception systems, the image of the object’s last appearance serves as a common search assistance. Compared to using only images as visual cues, videos capturing the process of object placement can provide procedural guidance, potentially enhancing users’ search efficiency. However, complete video playback capturing the entire object placement process as visual cues can be excessively lengthy, requiring users to invest significant viewing time. To explore whether segmented or accelerated video playback can still assist users in object retrieval tasks effectively, we conducted a user study. The results indicated that when video playback is covering the first appearance of the object’s destination to the object’s final appearance (referred to as the destination appearance, DA) and playing at normal speed, search time and cognitive load were significantly reduced. Subsequently, we designed a second user study to evaluate the performance of video playback compared to image cues in object retrieval tasks. The results showed that combining the DA playback starting point with images of the object’s last appearance further reduced search time and cognitive load.

In this article, we undertake a thorough investigation into the theory of indoor location, adopt an effective and quick positioning algorithm, and make use of a network of lowpower iBeacons. An iBeacon-based indoor positioning system (IPS) is presented to investigate how to utilize iBeacon for accurate location and whether it can successfully replace the current dominant positioning technology based on the analysis that was conducted. However, the first things that should be taken into account at this point in the design of house robots for indoor environments are how to quickly and precisely gather target node location information as well as how to regulate and plan a course. This article examined the more popular and often used indoor positioning techniques, provided a succinct summary of current indoor positioning technologies and regulators, and examined ultrasonic locating technology in depth. Based on this, a system for mobile home service robots was developed, and simulation tests were performed to assess the accuracy of node locating, node reception and arrival times, the best level of route planning, and navigation and path estimation errors in both absolute and comparative terms. Additionally, we go into detail about the difficulties in developing a practical IPS, the solutions that are already available, a thorough performance comparison, and some potential future IPS development trends. [ABSTRACT FROM AUTHOR]

The technological diversity applied in practical examples entails the identification and the analysis of different implementation options, thus opening the way to a world of technology. The goal of this paper is to spot novel prospects for incorporating sensors and Big Data techniques for better property administration strategies, through an indoor localization and navigation architecture. The hereby analysis uses an approach that combines Radio Frequency Identification technology, performance efficient algorithms for calculating routes in very large datasets, methodologies for optimizing spatial representations and Internet of Things specific hardware distribution techniques to properly address challenging logistics issues and complex facilities management. The proposed solution is tailored for a large underground parking area in a commercial center, enabling easy car location identification, automatic parking time calculation and dynamically determined occupancy levels, along with descriptive statistics about busy time intervals or visiting patterns. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The weighted $K$ -nearest neighbors (WKNN) algorithm is a widely adopted lightweight methodology for indoor WiFi positioning based on location fingerprinting. Nonetheless, it suffers from the disadvantage of a fixed $K$ value and susceptibility to incorrect reference point matching. To address this issue, we present a novel algorithm in this paper, referred to as static continuous statistical characteristics-soft range limited-self-adaptive WKNN (SCSC-SRL-SAWKNN). Our algorithm not only takes into account location tracking in the motion state but also exploits the continuous statistical features of extended periods of inactivity to enhance localization. In the motion state, we initially employ the self-adaptive WKNN (SAWKNN) algorithm to determine the optimal $K$ value, followed by the employment of the soft range limited KNN (SRL-KNN) algorithm to identify the correct reference point and ultimately estimate the position. When a prolonged stationary state is detected, we first utilize the moving window method to obtain a more stable position fingerprint, and then proceed with the positioning process in the same motion state. Ultimately, we use Kalman filter to generate the location trajectory. Our experimental findings demonstrate that the proposed SCSC-SRL-SAWKNN algorithm outperforms traditional WKNN, SAWKNN, and SRL-KNN techniques in terms of localization accuracy and location trajectory. Specifically, the localization accuracy of our algorithm is 56.7% and 36.6% higher than that of traditional WKNN in the static state and overall situation, respectively.

In view of the increasing number of power grid equipment and a large number of indoor deployments, the existing positioning technology cannot meet the requirements. In this paper, a DOA estimation method based on deep learning is proposed to improve the indoor positioning accuracy. Firstly, by studying the propagation characteristics of wireless signals and the transmission characteristics of indoor wireless channels, a statistical channel model with multi subcarrier characteristics is established to generate channel impulse response information. Furthermore, the transformation matrix required for virtual antenna expansion is designed by using the idea of interpolation transformation. This matrix is combined with the channel impulse response to obtain the virtual antenna array data for DOA estimation. The DOA estimation model based on CNN is optimized to obtain high-precision DOA. Finally, the simulation results show that the positioning accuracy can be doubled by using the virtual antenna array technology. The performance of the two-stage DOA estimation algorithm proposed in this paper is slightly better than the accuracy of a large difference step. Even when there are fewer APS, the positioning accuracy is better than the traditional MUSIC algorithm, which is more suitable for indoor positioning of a large number of power grid equipment in the future.

To achieve low-cost and robustness, an indoor location system using simple visual tags is designed by comprehensively considering accuracy and computation complexity. Only the color and shape features are used for tag detection, by which both algorithm complexity and data storage requirement are reduced. To manage the nonunique problem caused by the simple tag features, a fast query and matching method is further presented by using the view field of the camera and the tag azimuth. Then, based on the relationship analysis between the spatial distribution of tags and location error, a pose and position estimation method using the weighted least square algorithm is designed and works together with the interactive algorithm by the designed switching strategy. By using the techniques presented, a favorable balance is achieved between the algorithm complexity and the location accuracy. The simulation and experiment results show that the proposed method can manage the singular problem of the overdetermined equations effectively and attenuate the negative effect of unfavorable label groups. Compared with the ultrawide band technology, the location error is reduced by more than 62%. [ABSTRACT FROM AUTHOR]

As an indispensable type of information, location data are used in various industries. Ultrawideband (UWB) technology has been used for indoor location estimation due to its excellent ranging performance. However, the accuracy of the location estimation results is heavily affected by the deployment of base stations; in particular, the base station deployment space is limited in certain scenarios. In underground mines, base stations must be placed on the roof to ensure signal coverage, which is almost coplanar in nature. Existing indoor positioning solutions suffer from both difficulties in the correct convergence of results and poor positioning accuracy under coplanar base-station conditions. To correctly estimate position in coplanar base-station scenarios, this paper proposes a novel iterative method. Based on the Newton iteration method, a selection range for the initial value and iterative convergence control conditions were derived to improve the convergence performance of the algorithm. In this paper, we mathematically analyze the impact of the localization solution for coplanar base stations and derive the expression for the localization accuracy performance. The proposed method demonstrated a positioning accuracy of 5 cm in the experimental campaign for the comparative analysis, with the multi-epoch observation results being stable within 10 cm. Furthermore, it was found that, when base stations are coplanar, the test point accuracy can be improved by an average of 63.54% compared to the conventional positioning algorithm. In the base-station coplanar deployment scenario, the upper bound of the CDF convergence in the proposed method outperformed the conventional positioning algorithm by about 30%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The positioning of indoor electronic devices is an essential part of human–computer interaction, and the accuracy of positioning affects the level of user experience. Most existing methods for RF-based device localization choose to ignore or remove the impact of multipath effects. However, exploiting the multipath effect caused by the complex indoor environment helps to improve the model's localization accuracy. In response to this question, this paper proposes a multipath-assisted localization (MAL) model based on millimeter-wave radar to achieve the localization of indoor electronic devices. The model fully considers the help of the multipath effect when describing the characteristics of the reflected signal and precisely locates the target position by using the MAL area formed by the reflected signal. At the same time, for the situation where the radar in the traditional Single-Input Single-Output (SISO) mode cannot obtain the 3D spatial position information of the target, the advantage of the MAL model is that the 3D information of the target can be obtained after the mining process of the multipath effect. Furthermore, based on the original hardware, it can achieve a breakthrough in angular resolution. Experiments show that our proposed MAL model enables the millimeter-wave multipath positioning model to achieve a 3D positioning error within 15 cm. [ABSTRACT FROM AUTHOR]

Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE 802.11-2016 (also known as IEEE 802.11mc) Wi-Fi standard ("two-sided" RTT). Unfortunately, the penetration of this Wi-Fi protocol has been slower than anticipated, perhaps because APs tend not to be upgraded as often as other kinds of electronics, in particular in large institutions—where they would be most useful. Recently, Google released Android 12, which also supports an alternative "one-sided" RTT method that will work with legacy APs as well. This method cannot subtract out the "turn-around" time of the signal, and so, produces distance estimates that have much larger offsets than those seen with two-sided RTT—and the results are somewhat less accurate. At the same time, this method makes possible distance measurements for many APs that previously could not be used. This increased accessibility can compensate for the decreased accuracy of individual measurements. We demonstrate here indoor localization using one-sided RTT with respect to legacy APs that do not support IEEE 802.11-2016. The accuracy achieved is 3–4 m in cluttered environments with few line-of-sight readings (and using only 20 MHz bandwidths). This is not as good as for two-sided RTT, where 1–2 m accuracy has been achieved (using 80 MHz bandwidths), but adequate for many applications A wider Wi-Fi channel bandwidth would increase the accuracy further. As before, Bayesian grid update is the preferred method for determining position and positional accuracy, but the observation model now is different from that for two-sided RTT. As with two-sided RTT, the probability of an RTT measurement below the true distance is very low, but, in the other direction, the range of measurements for a given distance can be much wider (up to well over twice the actual distance). We describe methods for formulating useful observation models. As with two-sided RTT, the offset or bias in distance measurements has to be subtracted from the reported measurements. One difference is that here, the offsets are large (typically in the 2400–2700 m range) because of the "turn-around time" of roughly 16 μs (i.e., about two orders of magnitude larger than the time of flight one is attempting to measure). We describe methods for estimating these offsets and for minimizing the effort required to do so when setting up an installation with many APs. [ABSTRACT FROM AUTHOR]

Object location in indoor environments is challenging when there is no physical contact, a field of view, reflective materials, and an excess of obstacles. Several works propose using Radio Frequency Identification technology (RFID) and machine learning methods to develop location systems in those situations. However, using an object as a target class slows learning and prediction in large-scale environments. To circumvent such problems, we proposed a location system that uses hierarchical classification. We divided the environment into regions to reduce the classifier’s training and the number of predicted classes. To define the regions, we used clustering techniques, indicating which clustering technique achieves the best performance in the proposed scenario. The main contribution of this work is a high-precision location system for large-scale environments. The results showed the proposed system’s implantation in a real environment with 400 target objects with 5 cm of location precision. The accuracy for region detection is 99.36%, while for identifying the object is 99.94%. Additionally, with the proposed hierarchical approach, we showed a reduction of 38.16% and 58.39% in processing time and classifier model size. [ABSTRACT FROM AUTHOR]

In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, with the recent COVID-19 pandemic, indoor location systems have gained considerable attention for detecting violations in physical distancing requirements and monitoring restrictions on occupant capacity. However, existing systems that rely on wearable devices, cameras, or sound signal analysis are intrusive and often violate privacy. In this research, we propose a new framework for indoor location. We present an innovative, non-intrusive implementation of indoor location based on wireless sensor networks. Further, we introduce a new protocol for querying and performing computations in wireless sensor networks (WSNs) that preserves sensor network anonymity and obfuscates computation by using onion routing. We also consider the single point of failure (SPOF) of sink nodes in WSNs and substitute them with a blockchain-based application through smart contracts. Our set of smart contracts is able to build the onion data structure and store the results of computation. Finally, a role-based access control contract is used to secure access to the system. [ABSTRACT FROM AUTHOR]

The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users' smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field and Wi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users' trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building's floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.

In fire emergencies, timely communication with on-site coordinators and accurate localization of first responders facilitates effective task assignment and resource allocation in harsh, low-visibility environments. Building information modeling (BIM) is widely accepted in the architecture, engineering, and construction industries as a central repository of building information. It could provide both the geometric building data and semantic information; however, the convenient linkage and integration with indoor location technologies for emergency purposes have not been addressed according to the authors’ knowledge. A stand-alone BIM-based indoor location (BIMIL) framework and portal were designed and tested to enable the automatic extraction, transformation, and visualization of BIM-related data for public safety purposes in this study. Based on current information technology, this research reduces the gap in cross-application by supporting indoor location to overcome the primary shortcoming of existing indoor building models. Eliminating the need for specific software and skill in data processing, this portal will support on-site coordinators’ importation of BIM files, allowing them to convert those files into processed and visualized indoor information containing key yet simplified geometric building data and essential emergency-related information such as fire rating hours, egresses, and hazardous materials. Additionally, the indoor location data can be integrated into a generalized 3D building model to support decision-making activities and management tasks in the field.

To solve the problem of poor accuracy of the current GPS technology of indoor positioning, 5G technology is combined with convolutional neural network algorithm to proposes an indoor positioning scheme based on 5G new radio（NR）parameters. By collecting 5G NR data, the fingerprint data is formed with the reference point number and stored in the fingerprint database. Taking the precision, recall and micro-score value as the evaluation index, the convolutional neural network algorithm was used to train the fingerprint database to obtain the location model, and the Adam method was used to optimize the model. The total data set used in this scheme is 2 400, of which the training set size is 2 160 and the test set size is 240. The positioning model was used for 1 000 training sessions, with 512 training data for each batch, and the indoor positioning effect with an average error of 1.33 m was finally achieved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Indoor positioning is important for applications within Internet of Things, such as equipment tracking and indoor maps. Inexpensive Bluetooth-beacons have become common for such applications, where the distance is estimated using the Received Signal Strength. Large installations require substantial efforts, either in determining the exact location of all beacons to facilitate lateration, or collecting signal strength data from a grid over all locations to facilitate fingerprinting. To reduce this initial setup cost, one may infer the positions using Simultaneous Location and Mapping. In this paper, we use a mobile phone equipped with an Inertial Measurement Unit, a Bluetooth receiver, and an Unscented Kalman Filter to infer beacon positions. Further, we apply adaptive noise modeling in the filter based on the estimated distance of the beacons, in contrast to using a fixed noise estimate which is the common approach. This gives us more granular control of how much impact each signal strength reading has on the position estimates. The adaptive model decreases the beacon positioning errors by 27% and the user positioning errors by 21%. The positioning accuracy is 0.3 m better compared to using known beacon positions with fixed noise, while the effort to setup and maintain the position of each beacon is also substantially reduced. Therefore, adaptive noise modeling of Received Signal Strength is a significant improvement over static noise modeling for indoor positioning.

To achieve low-cost and robustness, an indoor location system using simple visual tags is designed by comprehensively considering accuracy and computation complexity. Only the color and shape features are used for tag detection, by which both algorithm complexity and data storage requirement are reduced. To manage the nonunique problem caused by the simple tag features, a fast query and matching method is further presented by using the view field of the camera and the tag azimuth. Then, based on the relationship analysis between the spatial distribution of tags and location error, a pose and position estimation method using the weighted least square algorithm is designed and works together with the interactive algorithm by the designed switching strategy. By using the techniques presented, a favorable balance is achieved between the algorithm complexity and the location accuracy. The simulation and experiment results show that the proposed method can manage the singular problem of the overdetermined equations effectively and attenuate the negative effect of unfavorable label groups. Compared with the ultrawide band technology, the location error is reduced by more than 62%.

As an indispensable type of information, location data are used in various industries. Ultrawideband (UWB) technology has been used for indoor location estimation due to its excellent ranging performance. However, the accuracy of the location estimation results is heavily affected by the deployment of base stations; in particular, the base station deployment space is limited in certain scenarios. In underground mines, base stations must be placed on the roof to ensure signal coverage, which is almost coplanar in nature. Existing indoor positioning solutions suffer from both difficulties in the correct convergence of results and poor positioning accuracy under coplanar base-station conditions. To correctly estimate position in coplanar base-station scenarios, this paper proposes a novel iterative method. Based on the Newton iteration method, a selection range for the initial value and iterative convergence control conditions were derived to improve the convergence performance of the algorithm. In this paper, we mathematically analyze the impact of the localization solution for coplanar base stations and derive the expression for the localization accuracy performance. The proposed method demonstrated a positioning accuracy of 5 cm in the experimental campaign for the comparative analysis, with the multi-epoch observation results being stable within 10 cm. Furthermore, it was found that, when base stations are coplanar, the test point accuracy can be improved by an average of 63.54% compared to the conventional positioning algorithm. In the base-station coplanar deployment scenario, the upper bound of the CDF convergence in the proposed method outperformed the conventional positioning algorithm by about 30%.

With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1–0.3 m less than that of the traditional method.

The benefits of the fourth industrial revolution are realised through accurate capture and processing of data relating to product, process, asset and supply chain activities. Although services such as Global Positioning Services (GPS) can be relied on outdoors, indoor positioning remains a challenge due to the characteristics of indoor environments (including metal structures, changing environments and personnel). An accurate Indoor Positioning System (IPS) is required to provide end-to-end asset tracking within a manufacturing supply chain to improve security and process monitoring. Inertial measurement units (IMU) are commonly used for indoor positioning and routing services due to their low cost and ease of implementation. However, IMU accuracy (including heading and orientation detection) is reduced by the effects of indoor environmental conditions (such as motors and metallic structures) and require low-cost reliable solutions to improve accuracy. The current state of the art utilises algorithms to adjust the IMU data and improve accuracy, resulting in error propagation. The research outlined in this paper explores the use of passive RFID tags as a low cost, non-invasive method to reorient an IMU step and heading algorithm. This is achieved by confirming reference location to correct drift in scenarios where magnetometer and zero velocity updates are not available. The RFID tag correction method is demonstrated to map the route taken by an asset carried by personnel in an indoor environment. The test scenario task is representative of warehousing and delivery tasks where asset and personnel tracking are required. [ABSTRACT FROM AUTHOR]