1,824 research outputs found
HEVA: Cooperative Localization using a Combined Non-Parametric Belief Propagation and Variational Message Passing Approach
This paper proposes a novel cooperative localization method for distributed wireless networks in 3-dimensional (3D) global positioning system (GPS) denied environments. The proposed method, which is referred to as hybrid ellipsoidal variational algorithm (HEVA), combines the use of non-parametric belief propagation (NBP) and variational Bayes (VB) to benefit from both the use of the rich information in NBP and compact communication size of a parametric form. InHEVA, two novel filters are also employed. The first one mitigates non-line-of-sight (NLoS) time-of-arrival (ToA) messages, permitting it to work well in high noise environments with NLoS bias while the second one decreases the number of calculations. Simulation results illustrate that HEVA significantly outperforms traditional NBP methods in localization while requires only 50% of their complexity. The superiority of VB over other clustering techniques is also shown
A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models
This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT
CIR Parametric Rules Precocity For Ranging Error Mitigation In IR-UWB
The cutting-edge technology to support high ranging accuracy within the indoor environment is Impulse Radio Ultra Wide Band (IR-UWB) standard. Besides accuracy, IR-UWB’s low-complex architecture and low power consumption align well with mobile devices. A prime challenge in indoor IR-UWB based localization is to achieve a position accuracy under non-line-of-sight (NLOS) and multipath propagation (MPP) conditions. Another challenge is to achieve acceptable accuracy in the conditions mentioned above without any significant increase in latency and computational burden. This dissertation proposes a solution for addressing the accuracy and reliability problem of indoor localization system satisfying acceptable delay or computational complexity overhead. The proposed methodology is based on rules for identification of line-of-sight (LOS) and NLOS and the range error bias estimation and correction due to NLOS and MPP conditions. The proposed methodology provides accuracy for two major application domains, namely, wireless sensor networks (WSNs) and indoor tracking and navigation (ITN). This dissertation offers two different solutions for the localization problem. The first solution is a rules-based classification of LOS / NLOS and geometric-based range correction for WSN. In the first solution, the Boolean logic based classification is designed for identification of LOS/NLOS. The logic is based on channel impulse response (CIR) parameters. The second solution is based on fuzzy logic. The fuzzy based solution is appealing well for the stringent precision requirements in ITN. In this solution, the parametric Boolean logic from the first solution is converted and expanded into rules. These rules are implemented into a fuzzy logic based mechanism for designing a fuzzy inference system. The system estimates the ranging errors and correcting unmitigated ranges. The expanded rules and designed methodology are based on theoretical analysis and empirical observations of the parameters. The rules accommodate the parameters uncertainties for estimating the ranging error through the relationship between the input parameters uncertainties and ranging error using fuzzy inference mechanism. The proposed solutions are evaluated using real-world measurements in different indoor environments. The performance of the proposed solutions is also evaluated in terms of true classification rate, residual ranging errors’ cumulative distributions and probability density distributions, as well as outage probabilities. Evaluation results show that the true classification rate is more than 95%. Moreover, using the proposed fuzzy logic based solution, the residual errors convergence of 90% is attained for error threshold of 10 cm, and the reliability of the localization system is also more than 90% for error threshold of 15 cm
Cramer-Rao bounds in the estimation of time of arrival in fading channels
This paper computes the Cramer-Rao bounds for the time of arrival estimation in a multipath Rice and Rayleigh fading scenario, conditioned to the previous estimation of a set of propagation channels, since these channel estimates (correlation between received signal and the pilot sequence) are sufficient statistics in the estimation of delays. Furthermore, channel estimation is a constitutive block in receivers, so we can take advantage of this information to improve timing estimation by using time and space diversity. The received signal is modeled as coming from a scattering environment that disperses the signal both in space and time. Spatial scattering is modeled with a Gaussian distribution and temporal dispersion as an exponential random variable. The impact of the sampling rate, the roll-off factor, the spatial and temporal correlation among channel estimates, the number of channel estimates, and the use of multiple sensors in the antenna at the receiver is studied and related to the mobile subscriber positioning issue. To our knowledge, this model is the only one of its kind as a result of the relationship between the space-time diversity and the accuracy of the timing estimation.Peer ReviewedPostprint (published version
Distributed localization of a RF target in NLOS environments
We propose a novel distributed expectation maximization (EM) method for
non-cooperative RF device localization using a wireless sensor network. We
consider the scenario where few or no sensors receive line-of-sight signals
from the target. In the case of non-line-of-sight signals, the signal path
consists of a single reflection between the transmitter and receiver. Each
sensor is able to measure the time difference of arrival of the target's signal
with respect to a reference sensor, as well as the angle of arrival of the
target's signal. We derive a distributed EM algorithm where each node makes use
of its local information to compute summary statistics, and then shares these
statistics with its neighbors to improve its estimate of the target
localization. Since all the measurements need not be centralized at a single
location, the spectrum usage can be significantly reduced. The distributed
algorithm also allows for increased robustness of the sensor network in the
case of node failures. We show that our distributed algorithm converges, and
simulation results suggest that our method achieves an accuracy close to the
centralized EM algorithm. We apply the distributed EM algorithm to a set of
experimental measurements with a network of four nodes, which confirm that the
algorithm is able to localize a RF target in a realistic non-line-of-sight
scenario.Comment: 30 pages, 11 figure
Sky-GVINS: a Sky-segmentation Aided GNSS-Visual-Inertial System for Robust Navigation in Urban Canyons
Integrating Global Navigation Satellite Systems (GNSS) in Simultaneous
Localization and Mapping (SLAM) systems draws increasing attention to a global
and continuous localization solution. Nonetheless, in dense urban environments,
GNSS-based SLAM systems will suffer from the Non-Line-Of-Sight (NLOS)
measurements, which might lead to a sharp deterioration in localization
results. In this paper, we propose to detect the sky area from the up-looking
camera to improve GNSS measurement reliability for more accurate position
estimation. We present Sky-GVINS: a sky-aware GNSS-Visual-Inertial system based
on a recent work called GVINS. Specifically, we adopt a global threshold method
to segment the sky regions and non-sky regions in the fish-eye sky-pointing
image and then project satellites to the image using the geometric relationship
between satellites and the camera. After that, we reject satellites in non-sky
regions to eliminate NLOS signals. We investigated various segmentation
algorithms for sky detection and found that the Otsu algorithm reported the
highest classification rate and computational efficiency, despite the
algorithm's simplicity and ease of implementation. To evaluate the
effectiveness of Sky-GVINS, we built a ground robot and conducted extensive
real-world experiments on campus. Experimental results show that our method
improves localization accuracy in both open areas and dense urban environments
compared to the baseline method. Finally, we also conduct a detailed analysis
and point out possible further directions for future research. For detailed
information, visit our project website at
https://github.com/SJTU-ViSYS/Sky-GVINS
Soft range information for network localization
The demand for accurate localization in complex
environments continues to increase despite the difficulty in extracting
positional information from measurements. Conventional
range-based localization approaches rely on distance estimates
obtained from measurements (e.g., delay or strength of received
waveforms). This paper goes one step further and develops
localization techniques that rely on all probable range values
rather than on a single estimate of each distance. In particular,
the concept of soft range information (SRI) is introduced,
showing its essential role for network localization. We then
establish a general framework for SRI-based localization and
develop algorithms for obtaining the SRI using machine learning
techniques. The performance of the proposed approach is quantified
via network experimentation in indoor environments. The
results show that SRI-based localization techniques can achieve
performance approaching the Cramer–Rao lower bound and
significantly outperform the conventional techniques especially
in harsh wireless environments.RYC-2016-1938
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
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