Locate the Mobile Device by Enhancing the WiFi-Based Indoor Localization Model

Localization is a fundamental operation in wireless networks. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor applications. For indoor localization, GPS does not work due to the lack of the line of sight to satellites. High-precision indoor localization is critical to many personal and business applications. WiFi-based indoor localization was proposed to be a practical method to locate WiFi-enabled devices due to the popularity of WiFi networks. However, it suffers from large localization errors. Our experimental results indicate that this scheme consistently leads to an average error around 3 meters. The existence of different locations with similar WiFi signal strength is the reason behind the large errors. To improve the localization precision, a hybrid indoor localization scheme, HILL, is proposed in this paper. Inspired by the fact that a large number of WiFi-enabled mobile devices have been deployed, HILL uses 3 phases to improve the precision of WiFi-based localization. First of all, it measures the distances between each pair of peer devices through acoustic ranging. Secondly, the Classical Metric Multidimensional Scaling (MDS) method is applied to the collected distances, which results in a graph consistent with the distances. Finally, the graph generated by MDS is embedded onto the graph corresponding to WiFi-based localization in order to achieve high localization precision. Our experimental results indicate that the average localization error of HILL is about 1 meter.

Plot location errors (distance between recorded and true coordinates) of National Forest Inventory (NFI) cause a mismatch between plot data and remotely sensed data and might prevent re-measurement because plots with large location errors are hard to find. However, few studies have examined the detailed distribution of errors and related factors, and no studies have verified whether the large errors prevent re-measurement. This study analyzed data related to 101 plots in central Japan that had been established by the First NFI (1999–2003) on a 4-km grid. Plot location errors of these plots were measured by revisiting. Selective Availability (SA) of the Global Positioning System, which degraded location accuracy until May 2000, was the most important factor in increasing plot location errors. The mean errors were 58.6 and 15.0 m, with and without SA, respectively. In 12 plots with large plot location errors (the mean error was 84.6 m), re-measurement in the Second NFI (2004–2008) was not conducted because plot locations could not be found. In these situations, alternative new plots were established; however, their species compositions were significantly different from the initial NFI plots. Plot location errors of NFI adversely affect the continuity of plot data as well as the analysis with remotely sensed data.

Received Signal Strength Indicator (RSSI) is simplest among various indoor localization methods. The main drawback is requiring a training phase to build fingerprints database, which is time consuming and sensitive to changes of test environment. Time based approaches such as Time of Arrival (ToA) and Time Difference of Arrival (TDoA) achieve better performance with some hardware expenditure, while location accuracy is significantly affected by multipath interference. By employing Super-resolution techniques (SRTs), multipath components can be estimated to improve localization precision, but limited to OFDM-based Multiple Input Multiple output (MIMO) wireless communication systems. Considering for general radio signals with strong correlation, we propose an enhanced TDoA approach based on RSS-assisted cross-correlation method. Simulation and experimental results demonstrate that our method is effective to mitigate multipath interference, verified by IEEE 802.11b WLAN. Compared with SRTs, our method achieves comparable localization precision, and has superiority in computational complexity and anti-noise performance.

To localize a seen object, the CNS has to integrate the object's retinal location with the direction of gaze. Here we investigate this process by examining the localization of static objects during smooth pursuit eye movements. The normally experienced stability of the visual world during smooth pursuit suggests that the CNS essentially compensates for the eye movement when judging target locations. However, certain systematic localization errors are made, and we use these to study the process of sensorimotor integration. During an eye movement, a static object's image moves across the retina. Objects that produce retinal slip are known to be mislocalized: objects moving toward the fovea are seen too far on in their trajectory, whereas errors are much smaller for objects moving away from the fovea. These effects are usually studied by localizing the moving object relative to a briefly flashed one during fixation: moving objects are then mislocalized, but flashes are not. In our first experiment, we found that a similar differential mislocalization occurs for static objects relative to flashes during pursuit. This effect is not specific for horizontal pursuit but was also found in other directions. In a second experiment, we examined how this effect generalizes to positions outside the line of eye movement. We found that large localization errors were found in the entire hemifield ahead of the pursuit target and were predominantly aligned with the direction of eye movement. In a third experiment, we determined whether it is the flash or the static object that is mislocalized ahead of the pursuit target. In contrast to fixation conditions, we found that during pursuit it is the flash, not the static object, which is mislocalized. In a fourth experiment, we used egocentric localization to confirm this result. Our results suggest that the CNS compensates for the retinal localization errors to maintain position constancy for static objects during pursuit. This compensation is achieved in the process of sensorimotor integration of retinal and gaze signals: different retinal areas are integrated with different gaze signals to guarantee the stability of the visual world.

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

At present, most mature localization technologies are based on the line-of-sight (LoS) environment. However, many human activities occur in the indoor non-line-of-sight (NLoS) environment, and thus localization in this challenging environment is also indispensable. In addition, the localization using a single access point (AP) is more suitable for limited space, such as shopping malls and teaching buildings. Therefore, it is of great significance to study the localization technology of single AP in indoor NLoS environments. This paper proposes a localization algorithm to solve this difficult problem based on the natural reflection path, which can associate the locations of the target, scatterer, and AP. Firstly, the localization model is constructed based on the angle-of-departure ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> oD), angle-of-arrival (AoA), and relative-time-of-flight (rToF) of the natural reflection path. Unlike absolute time-of-flight (ToF), rToF can eliminate synchronization errors. Then, we derive the closed form solution of the target location, and the least square (LS) algorithm is used to solve the target location. Finally, we use the commercial-grade ray-tracing simulation software to conduct the experiments. Simulation results show that the algorithm can achieve high localization accuracy with only a few natural reflection paths.

Fingerprint-based localization systems mainly utilize the received signal strength (RSS) to estimate the location of a receiver, and the localization accuracy depends largely on the number and placement of access points (APs). In this paper, we propose a novel method for placing the APs, which aims to minimize the total number of similar fingerprints (SFs) over the entire array of training locations. Minimizing SFs will increase the diversity of RSS array and hence improve localization accuracy. To solve the problem using a simple brute force search would be highly computationally expensive and inefficient. Instead, we propose a heuristic optimization algorithm based on Simulated Annealing (SA). Numerical results are obtained for both the brute force search and the SA based approach. Finally, the proposed algorithm's outputs, i.e., the APs' locations, are used with a K-Nearest Neighbors based localization algorithm, and the resulting localization errors are analyzed.

In this research, the accuracy of vehicle localization using Global Navigation Satellite System (GNSS) is improved by utilizing node constraints embedded in Moving Horizon Estimation (MHE). In the area where the satellite signal does not reach, or multipath appears, GNSS measurements will be biased and result in large localization error. In this study, GNSS, odometry, and map information are fused by MHE to address this issue. MHE incorporate constraints to improve estimation accuracy. In this study, nodes on the map are utilized as constraints to modify the estimated positions using nodes for each point without requiring a high-precision map. In addition, MHE can modify the past estimation within the horizon, which correct the series of estimated position so that they coincide with the running trajectory. Furthermore, the estimation error due to GNSS bias is corrected by estimating the bias itself using the displacement at the node. This correction is effective even after the constraint at nodes goes out of the horizon. We conducted simulations of vehicle localization in which nodes were placed on the trajectory. It was verified that the proposed method suppressed the GNSS positioning error. Therefore, MHE with nodes as constraint could improve the estimation affected by the positioning error of GNSS.

Localization of mobile phones is important to location-based mobile services, but achieving good location estimation of mobile phones is difficult especially in environment whose path loss exponent is unknown. In this paper, we present a Wi-Fi localization solution specifically designed for dense WLANs with unknown path loss exponent. In order to leverage between the computational cost and localization accuracy, our solution establishes a neighbor selection scheme based on the Voronoi diagram to identify a subset of Access Points (APs) to participate in localization. It considers the identified subset of APs and a mobile phone to be located as a mass-spring system. Provided with information of known coordinates of APs, the solution estimates the path loss exponent of the physical environment, infers inter-distances between APs and the mobile phone from Wi-Fi signals received, and implements spring relaxation algorithm to approximate the geographical location of the mobile phone, where this location estimation is fed back to refine the estimated exponent iteratively. Extensive simulation results confirm that our solution is able to provide location estimation with an attractive average accuracy of below 2 m in a typical Wi-Fi setup.

Compared with the Wi-Fi Received Signal Strength (RSS) commonly-used for the indoor localization, the Channel State Information (CSI) can be used for precise ranging to achieve the high Wi-Fi indoor localization accuracy since it includes the fine-grained physical-layer information such as the amplitude and phase of each subcarrier during the signal transmission. Due to the lack of the theoretical analysis of the localization error bound in the existing CSI ranging-based localization methods, it is not easy to compare the ideal performance of different localization methods. Therefore, this paper proposes a CSI ranging-based Wi-Fi indoor localization error bound analysis method, which is based on the indoor signal propagation model to derive out the CSI ranging-based localization error bound by considering the relationship between the localization accuracy and the path loss, shadow fading, multipath effect and asynchronous effect. Besides, through the experimental comparison, this paper analyzes the difference between the actual localization error and the derived localization error bound, as well as discusses the impact of different experimental parameters on the localization performance.

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements are not yet complete. Like any other process, localization also has security requirements. The use of ultra-wideband (UWB) indoor localization systems have recently grown quickly in industries, with a reliable, fast, and have high accuracy performances. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, as TDOA measurement errors increase, the accuracy of the localization decreases. The accuracy of the TDOA measurement is influenced the accuracy of the localization and affected by multiple factors such as time synchronization, errors in sensor positions, missing data is caused by network attack. To reduce the influence of a sensor measurement error in localization, this paper proposes an improved localization algorithm for source localization using deep learning to address a TDOA measurements error or missing data in an asynchronous localization called DeepTAL. Unlike the conventional algorithm, DeepTAL can obtain highly accurate localization data in the presence of TDOA measurement errors or missing data. The algorithm starts with TDOA measurements without time synchronization. A network based on Long Short-Term Memory (LSTM) is then applied to achieve a stronger learning and better representation of the determined target state and TDOA prediction. The network can express features more abstractly at higher levels and increase recognition accuracy. After that, the target node is accurately located by TDOAs. We implement the DeepTAL algorithm on an asynchronous localization system with UWB signals. The experiments show that the proposed DeepTAL algorithm is efficient in improving the localization precision as measurement errors or missing data.

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

The gateway access point (AP) in a wireless mesh network becomes the natural bottleneck node around which all the traffic relayed by APs that is exchanged among the terminals and the Internet tend to concentrate. So far most of the practical deployments of mesh wireless local area networks (WLANs) focused on public safety and public access have taken place in rural or suburban areas where the low density of users and the low data-rate applications in use do not impose stringent traffic conditions, making the conventional single-radio DCF-based system defined by IEEE 802.11 a feasible implementation option. However, under relatively high traffic-load conditions, the large number of packet collisions produced by the accumulation of traffic in the vicinity of gateway APs may greatly reduce the overall network throughput and largely increase the delay, especially in case of packets that traverse several hops, thus affecting real-time applications like voice over IP (VoIP). To cope with this problem a polling mechanism compliant with the IEEE 802.11e hybrid-coordination-function controlled channel access (HCCA) which operates in a single network interface card (NIC) in the vicinity of gateway APs has been proposed in this paper. The polling scheme is complemented with a Distributed Coordination Function (DCF) channel access that also operates in the vicinity of gateway APs in a different NIC and on a different channel. The HCCA NIC allows any gateway AP to exchange data frames with its surrounding APs in a scheduled and bidirectional way while the DCF NIC provides gateway APs a contention-based way to receive data frames from their respective surrounding APs. Computer simulations carried out in OPNET version 10.0 to evaluate the combination of both contention-based and contention-free access schemes in the area surrounding gateway APs show that the proposed mechanism can largely increase the total throughput while providing low transmission delay. As no changes to the IEEE 802.11 related protocols are required, the proposed scheme represents an attractive option to implement a mesh WLAN.

With the increased deployment of wireless local area networks (WLAN) in many popular locations with multiple access points (AP), the limited number of usable radio channels provided by the IEEE 802.11 standard has resulted in a worse than expected network performance, such as very low throughput. The existing static radio channel allocation (RCA) schemes can only partially solve the problem in the initial planning of APs, but cannot adapt to environment changes and dynamic traffic conditions afterwards. We propose a new dynamic RCA scheme that aims to improve network performance by explicitly minimizing the cochannel interference among the neighboring APs that share a radio channel. Simulation results have demonstrated that the proposed scheme has not only significantly increased the overall network throughput, but also greatly reduced the cochannel interference among the APs

The placement of the access points (APs) has a significant impact on the wireless system performance, especially for irregular heterogeneous networks with hierarchical APs such as macro/micro base-stations, pico-stations, and femto-stations. Traditional system modeling and optimization are based on the regular 2D hexagonal cellular topology and a set of predefined large-scale propagation models, which is highly abstract and may be inaccurate. This paper considers the AP placement optimization problem in realistic deployment environments, where radio-wave propagation characteristics are accurately modeled using ray-tracing techniques. Toward this end, this paper proposes a novel concept of area proportional fairness utility for the entire network under a given user geographic distribution, and proposes an iterative method to optimize the placement of the APs for utility improvement while taking into account the mutual interference between the APs. The significant benefit of the placement optimization is shown in a numerical experiment conducted in a realistic Chicago downtown topology, where placement optimization is shown to improve the sum rate by up to 40%.