Performance analysis indoor location tracking framework with SIP on IPv6

Global positioning technologies such as the Global Positioning System (GPS) are ubiquitously available for different positioning applications. Within indoor environments, coverage of the explicit sensors based on GPS is limited. Developing an indoor location tracking system based on the Received Signal Strength Indicator (RSSI) of the Wireless Local Area Network (WLAN) is considered cost effective method. The widely used technique for estimating the position out of the RSSI measurements is the Extended Kalman Filter (EKF). However, EKF has high computational complexity due to the calculation of Jacobian matrices and suffers from filer instability. In this paper, we propose the Scaled Unscented Kalman Filter (SUKF), which is one of the Sigma Point Kalman Filters (SPKF) family, to overcome the limitations of the EKF. SUKF shall work over the WLAN IEEE 802.11n networks to exploit the RSSI range measurements for localizing and tracking of a mobile node. For performance evaluation, SUKF is compared with the EKF. Results are illustrated using Monte Carlo simulation in MATLAB.

The Global Positioning System (GPS) has eventually become a common positioning technology. While GPS enabled many applications, satellite signals have yet to overcome many obstacles to enable indoor positioning. Meanwhile, due to the wide deployment of Wireless Local Area Networks (WLAN) in recent years, WLAN positioning algorithms have become popular for mobile device positioning in indoor environments. The most accurate WLAN positioning algorithms exploit the so-called fingerprinting concept which consists of two stages. In the offline stage (training), the Received Signal Strength Indicator (RSSI) from a set of available Access Points (AP) is measured for a number of reference locations and stored in a database. Due to the availability of many APs and the complex structure of indoor environments, this information is distinctive for each reference location and thus is called a position fingerprint. In the online stage (testing), a mobile device receives RSSIs from the APs and their fingerprint is compared to the fingerprints that are stored in the database for a best possible match. WLAN fingerprinting can provide high accuracy in indoor environments when there are many APs which ensure fingerprint uniqueness. This paper provides a novel approach to use WLAN multipath signals as possible fingerprints for positioning algorithms for applications with lower number of access points. The paper provides an initial study.

As WiFi technology becomes more widespread and integrated, precise location tracking within complex indoor environments is gaining significance in contemporary public spaces.Nevertheless, challenges persist in indoor scenarios, including issues such as noise and multi-path effects stemming from the degradation of the Received Signal Strength Indicator (RSSI). Additionally, obstacles causing signal shading contribute to a decrease in the accuracy of indoor location tracking. Within this paper, we introduce an innovative indoor localization algorithm termed Vision Transformer Indoor Localization (VTIL). This algorithmleverages RSSI and Vision-Transformer (ViT) technologies to enhance indoor positioning accuracy. Initially, the RSSI fingerprints undergo normalization by scaling them to the maximum and minimum values. To mitigate the impact of noise and irrelevant features, the Principal Component Analysis (PCA) algorithmis then employed for effective feature extraction.Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our approach achieves a noteworthy 37.26% reduction in the average distance estimation error when compared to existing indoor fingerprint localization algorithms.

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.

Tracking a moving user in indoor environments using Bluetooth low energy beacons.

An indoor localization system in wireless sensor networks has become a hot development area. Received signal strength indicator (RSSI)-based localization is a promising technique since it requires a relatively low configuration, battery power and easy control. However, the received signal strength is influenced by channel interference and propagation environments. This characteristic affects channel stability and location accuracy in RSSI-based localization. As a result, we propose a novel indoor localization system consisting of a preprocessing method and a post-processing method. To improve channel stability, the pre-processing method selects an optimal channel in terms of the smallest distance error. The optimal channel is less affected to IEEE 802.11. To develop location accuracy, the post-processing method performs maximum likelihood estimation-based location tracking scheme considering the reliability of the RSSI value measurement. We apply three methods to the existing MLE to improve the reliability of the RSSI value. By using this indoor localization system with preprocessing and post-processing, the location error can be reduced. We also proved the performance of the indoor localization system by conducting experiments in a real indoor environment.

Location based service (LBS) cannot be realized unless the location of the user is available. For indoor LBS, indoor positioning must be utilized and many researchers have been working on indoor positioning and tracking. For example, extended Kalman filter (EKF) was exploited in Bluetooth based indoor positioning. Nowadays, WLAN (wireless local area network) is available virtually everywhere. Thus, WLAN based indoor positioning and tracking is more economical than Bluetooth based ones. The main purpose of this paper is to propose a new WLAN based EKF indoor tracking method by extending existing Bluetooth based EKF positioning method. Experimental results comparing our method with other previous methods are discussed.

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

In today’s era finding the location and navigation of an object is essential for many reasons. The primary reason is locating the object, and navigation helps systems to make decisions faster. Secondary reason is we may detect fault and posses faster processing and operation. Another side of development and research is for cost-effective techniques involving indoor and outdoor systems. Many researchers are working on indoor positioning and developing lightweighted solutions using radio frequencies. Managing and monitoring radio frequencies in an indoor system have physical barriers; they also have some technological limitations where we can provide solutions. The solutions for indoor localization and tracking are GPS signal, Bluetooth, infrared, RFID, and wireless LAN. All these radio frequencies deal with a crucial issue of gaining RSS (Received Signal Strength). In this work, we are focusing on RFID-based indoor environment where RSS is our main attention, we also target on gaining efficiency and accuracy, stability, robustness. The proposed algorithm of this work is mainly targeting with directional antenna by which we can overcome the physical and other barriers of radio frequency. These kinds of indoor environments such as health care, loyalty management system, automatic parking allotment system, tracking services for older people, or customers inside living communities, mobile robot, logistics system, etc., can be facilitated using directional antenna and RFID tagging for navigation indoor positioning.

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.

This paper describes research towards a system for locating wireless nodes in a home environment requiring merely a single access point. The only sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface, e.g., Wi-Fi. Wireless signal strength maps for the positioning filter are obtained by a two-step parametric and measurement driven ray-tracing approach to account for absorption and reflection characteristics of various obstacles. Location estimates are then computed using Bayesian filtering on sample sets derived by Monte Carlo sampling. We outline the research leading to the system and provide location performance metrics using trace-driven simulations and real-life experiments. Our results and real-life walk-troughs indicate that RSSI readings from a single access point in an indoor environment are sufficient to derive good location estimates of users with sub-room precision.

To provide valuable services in ubiquitous and pervasive computing, it is necessary to estimate the location of users or objects in the environment. The Global Positioning System (GPS) has supported many applications using outdoor location estimation, but there is still a need for alternative technologies in buildings. Assuming that multiple wireless sensors will be attached to ubiquitous environments, they could also support location estimation and tracking. This paper presents optimizations for an indoor location tracking system using wireless sensors, called LocSens. LocSens works with a minimum number of sensor nodes. It is established and tested in a real indoor scenario over multiple rooms. LocSens could be improved by optimizing algorithms and using more precise sensor boards. Results confirm improved accuracy in location estimation and tracking of moving objects.

There are no large-scale deployments to navigate the people indoor environments as the GPS for outdoor environments. For this reason, the development of high-performance systems of indoor localization has been considered the potential research areas by both academia and industry in last decade. These indoor localization research studies are divided into two groups in the literature which are readerbased and tag-based systems. Most of these studies are reader-based. These readers and tags can determine the received signal strength levels of the signal using the radio frequency technology. We present a tagbased and cost-effective indoor localization system using NRF24L series radio modules. Different from the available tag-based systems, we propose a smart transmit power scheme run in the tags called SmartTag. The SmartTag provides communication with the nearest reader using different power levels. However, the existing tag-based methods require the received signal strength indicator (RSSI) information to achieve high localization accuracy. Our proposed system eliminates RSSI information with the SmartTag. We have developed a simulation of the prototype system, and we used Monte Carlo simulations in many different scenarios. We have also implemented the system prototype and conducted in an actual indoor environment to demonstrate the performance of the SmartTag by comparing with the Monte Carlo simulations. From the indoor experimental results, we confirm that the detections of room and objects are accurate and positioning accuracy is improved thanks to the SmartTag localization. If we briefly summarize our work, we have determined the position in indoor environments by communicating with the nearest reader in the medium thanks to the scheme on the SmartTag.

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

At present, the theory and algorithm of wireless indoor positioning systems have been widely researched. Some applications of these systems have been successfully used such as inventory management. Nevertheless, positioning using radio signals indoor is subject to multipath propagation and other causes. Therefore, how to determine the location accurately has been recognized as a difficult task. This paper focuses on one of the positioning methods called received signal strength indication(RSSI) in a Wireless Local Area Network(WLAN). To improve the positioning accuracy, we proposed a scheme which to estimate the distance between access point(AP) and mobile station(MS) using the average of pilot signal strength in frequency domain. We then propose another scheme to estimate the position of the MS based on the estimated distances. At last, the simulations results show that the proposed schemes has a higher degree accuracy compared with the conventional methods.