1,721,203 research outputs found
The impact of Galileo Open Service on the Location Based Services markets: a review on the cost structure and the potential revenue streams
Full text linkMany Location Based Services (LBS), such as navigation and tracking services, are using Global Satellite-based Navigation Systems (GNSS). GNSS is the most widely used positioning solution for LBS outdoors, therefore any improvement in the quality of GNSS positioning services will directly improve the quality of LBS and therefore it will generate more revenue and attract more users. One of the upcoming satellite navigation systems is Galileo, which is being deployed by the European Union (EU). Beside all political motivations behind Galileo, the availability of more satellites in view and a more accurate, reliable and continuous positioning service are some of the technological motivations of having yet another of GNSS on sky. Such improvement in positioning service and, as a result, in LBS applications will develop the market and attract more users. However, due to long delays, current powerful competitors which are making the GNSS market increasingly crowded, and also the cost of Galileo being covered by EU taxpayers only, there is a question if another of GNSS is really required and it is able to return all its cost in near future. This chapter assesses the financial aspects of Galileo at the time of writing the book, including increasing costs and impact of losing some parts of market and also its potential revenue and the economic impact of positioning and timing service improvement by Galileo, and finally the impact of Galileo on future markets of LBS is estimated
Galileo E1 code tracking analysis (Matlab-based software)
No full text<p>The attached code gives an example of the analytical performance of three code trackers (non-coherent early-late power NELP, coherent early-late power CELP, and dot-product discriminator DP) of Galileo E1 signals. Both BOC(-) (E1c) and CBOC(+) (E1B) signals are considered. The work is based on the analytical performance reported in:</p>
<p>Lohan, E.S. "Analytical performance of CBOC-modulated Galileo E1 signal using sine BOC(1,1) receiver for massmarket application". (2010) IEEE PLANS, Position Location and Navigation Symposium, art. no. 5507207, pp. 245-253</p>
<p>The file to run is Main_file.m; the rest are supporting functions.</p>
Matlab-based example of a Double Binary Offset Carrier modulation
No full text<p>The Example_BOC.m file shows a basic example of a sine-BOC modulated pseudorandom code and compares its autocorrelation function with the ideal autocorrelation function derived based on the Double Binary Offset carrier (DBOC) modulation concept described in:</p>
<p>Lohan, E. S., A. Lakhzouri, and M. Renfors. 2007. "Binary-Offset-Carrier Modulation Techniques with Applications in Satellite Navigation Systems." <em>Wireless Communications and Mobile Computing</em> 7 (6): 767-779. doi:10.1002/wcm.407.</p>More details can also be found here:
Lohan, E. S., A. Lakhzouri, and M. Renfors. 2006. "Complex Double-Binary-Offset-Carrier Modulation for a Unitary Characterisation of Galileo and GPS Signals." IEE Proceedings: Radar, Sonar and Navigation 153 (5): 403-408. doi:10.1049/ip-rsn:20060005
Software (Matlab) for time-frequency analysis of GNSS signals in L1-B1-E1 bands
No full text<p>Matlab-based program for plotting the ideal Autocorrelation Function (ACF) and Power Spectral Density (PSD) of GNSS signals in L1-B1-E1 bands. It includes GPS, Galileo, Beidou and Glonass signals. The model is based on the model reported in the following paper:</p>
<ul>
<li>E.S. Lohan, K. Borre, "Performance limits in multi-GNSS systems", IEEE Transactions on Aerospace, vol. 52(5), Jan 2017, pp. 2477-- 2494</li>
</ul>
Simulink-based dual-band Galileo/GPS simulator (acquisition and tracking)
No full textThe attachment contain a Dual-band Dual-GNSS Simulink-based simulator for the acquisition and tracking of GNSS signals (Galileo E1/E5 and GPS L1/L5). The simulator and its capabilities are described in detail in the attached Master Thesis completed in December 2019 at Tampere University.
How to cite this work:
Ibrahim Touman, "Simulink-based dual-band Galileo/GPS simulator", Tampere University master thesis software project, Jan 2020, doi 10.5281/zenodo.3600536</p
Campaña de actualización y optimización de medidas Wi-Fi para posicionamiento en interiores
No full textLa mayor parte de las actividades diarias de la gente se llevan a cabo dentro de edificios. Diversos sectores como la medicina, la industria, el sector académico o los sistemas de seguridad requieren el uso de sistemas de posicionamiento en interiores. Como consecuencia, es fundamental desarrollar un sistema de posicionamiento en interiores fiable y preciso. Dado que los sistemas de navegación por satélite no son adecuados para posicionamiento en interiores, numerosos sistemas han surgido con el propósito de realizar dicha función. Sin embargo, cada sistema tiene sus ventajas y desventajas. Por lo tanto, actualmente no existe un sistema de posicionamiento en interiores que pueda ofrecer el mejor servicio en cualquier situación. Las bases de datos correspondientes a la intensidad de señal del Wi-Fi de dos edificios de la Universidad Tecnológica de Tampere requerían una actualización. Por lo tanto, el objetivo de la tesis ha sido actualizar y optimizar las medidas de intensidad de señal recibida en estos dos edificios. Los resultados han sido presentados y analizados esperando que sean de utilidad para el diseño y la mejora de sistemas de posicionamiento en interiores basados en la intensidad de señal del Wi-Fi. Conocer las ventajas y las debilidades de diversos sistemas de posicionamiento en interiores puede resultar de gran utilidad para el diseño y la mejora de estos sistemas. Por lo tanto, se explica la funcionalidad de varios sistemas de posicionamiento en interiores. En esta tesis ha sido utilizado el sistema de posicionamiento basado en la intensidad de señal recibida de Wi-Fi. De esta manera, una base de datos con las medidas ha sido construida. Esta base de datos es usada para simular el sistema de posicionamiento en interiores, que es implementado utilizando un estimador Bayesiano y el algoritmo de k vecinos más próximos. Sucesivamente, los parámetros de los algoritmos han sido optimizados.
El análisis de los resultados muestra que para los valores más bajos de los parámetros el funcionamiento del sistema es el óptimo. El mejor funcionamiento del sistema ha dado lugar a una probabilidad de detección de piso del 99% y una media de error en la distancia entre la estimación y la posición real de 3 m. Sin embargo, efectos negativos como los producidos por valores atípicos de ciertas medidas deben ser tenidos en cuenta. Algunas debilidades del sistema como los desafíos de la parte del entrenamiento del sistema abren el camino para futuras investigaciones y desarrollos del sistema.
Abstract:
Most of the day to day people activities are carried out inside buildings. Many sectors such as, medicine, industry, academia or even security systems require indoor positioning services. As a consequence, it is essential to develop a reliable and accurate indoor positioning system (IPS). Since global navigation satellite systems (GNSSs) are not suitable for indoor localization, several IPSs have emerged. However, each indoor positioning technology has its advantages and disadvantages. Hence, there is not an IPS system with the best performance for every situation. The IPS databases based on the Wi-Fi infrastructure installed in two buildings of the Tampere University of Technology required an update. Therefore, the scope of this thesis has been to update and moreover, optimize the IPS fingerprint databases of these two buildings. The results have been presented and analyzed with the expectance that they will be useful for similar or wider projects. Multiple IPSs are explained, as it is convenient to understand the advantages and the weaknesses of each technology. The technology which provides the positioning services is the fingerprint Wi-Fi received signal strength (RSS). In that way, a measurement database is built. The database is used to simulate the IPS, which is implemented through the Bayesian estimation algorithm and the k-nearest neighbors technique. Successively, the parameters of the algorithm are optimized. The analysis of the results showed that for the lowest values of the parameters, the performance of the system improves with respect to higher values of the parameters. The best performance of the Wi-Fi based IPS results in a floor detection probability nearby 99% and an average distance error below 3 m. However, negative effects, such as the ones produced by outlier measurements, must be taken into account. Some weaknesses of the Wi-Fi based IPS, such as the challenges associated to the training phase, open a path of research that might enhance the system performance
Iterative detection, decoding, and channel estimation in MIMO-OFDM
Full text linkAbstractIterative receiver techniques, multiple-input – multiple-output (MIMO) processing, and orthogonal frequency division multiplexing (OFDM) are amongst the key physical layer technologies when aiming at higher spectral efficiency for a wireless communication system. Special focus is put on iterative detection, decoding, and channel estimation for a MIMO-OFDM system. After designing separately efficient algorithms for the detection, channel decoding, and channel estimation, the objective is to optimize them to work together through optimizing the activation schedules for soft-in soft-out (SfISfO) components. A list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal loss of performance. It is shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. The convergence of the iterative detection and decoding is improved by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. Unlike in pilot based channel estimation, the least-squares (LS) channel estimator based on symbol decisions requires a matrix inversion in MIMO-OFDM. The frequency domain (FD) space-alternating generalized expectation-maximization (SAGE) channel estimator calculates the LS estimate iteratively, avoiding the matrix inversion with constant envelope modulation. The performance and computational complexity of the FD-SAGE channel estimator are compared to those of pilot based LS channel estimation with minimum mean square error (MMSE) post-processing exploiting the time correlation of the channel. A time domain (TD) SAGE channel estimator is derived to avoid the matrix inversion in channel estimation based on symbol decisions for MIMO-OFDM systems also with non-constant envelope modulation. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. It is proposed to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and to find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of the channel estimate’s mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the channel decoder. Trellis search algorithms are shown to find the convergence with the lowest possible complexity using the EXIT functions. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided. Academic dissertation to be presented with the assent of the Faculty of Technology of the University of Oulu for public defence in Auditorium IT116, Linnanmaa, on 10 June 2010, at 12 noonAbstract
Iterative receiver techniques, multiple-input – multiple-output (MIMO) processing, and orthogonal frequency division multiplexing (OFDM) are amongst the key physical layer technologies when aiming at higher spectral efficiency for a wireless communication system. Special focus is put on iterative detection, decoding, and channel estimation for a MIMO-OFDM system. After designing separately efficient algorithms for the detection, channel decoding, and channel estimation, the objective is to optimize them to work together through optimizing the activation schedules for soft-in soft-out (SfISfO) components.
A list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal loss of performance. It is shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. The convergence of the iterative detection and decoding is improved by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits.
Unlike in pilot based channel estimation, the least-squares (LS) channel estimator based on symbol decisions requires a matrix inversion in MIMO-OFDM. The frequency domain (FD) space-alternating generalized expectation-maximization (SAGE) channel estimator calculates the LS estimate iteratively, avoiding the matrix inversion with constant envelope modulation. The performance and computational complexity of the FD-SAGE channel estimator are compared to those of pilot based LS channel estimation with minimum mean square error (MMSE) post-processing exploiting the time correlation of the channel. A time domain (TD) SAGE channel estimator is derived to avoid the matrix inversion in channel estimation based on symbol decisions for MIMO-OFDM systems also with non-constant envelope modulation.
An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. It is proposed to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and to find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of the channel estimate’s mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the channel decoder. Trellis search algorithms are shown to find the convergence with the lowest possible complexity using the EXIT functions. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided
Channel Charting assisted Radio Localization: Towards Reliability and Robustness
Full text linkIndoor localization plays an important role in various fields, such as health care, industrial production or smart shopping centers. Often, radio localization is employed due to its high accuracy, scalability, and energy efficiency. Classical ToA based localization may achieve centimeter accuracy, with LoS between receiver and transmitter. However, due to the complexity of indoor environments, the LoS is often blocked, lowering the accuracy and reliability dramatically. To enable localization in such challenging environments, fingerprint-based models can be employed. Fingerprint-based models learn a function mapping distinct radio signatures to positions given a labeled database. Those models are immune to NLoS propagation as they only require similar radio signatures at certain positions. However, due to the dynamic nature of productive environments, e.g. industrial facilities, fingerprints regularly change, rendering a maintenance process necessary, which includes an expensive labeling process of the recorded signals, i.e., CSI. Thus, in this thesis, novel fingerprinting concepts are contributed to reduce or eliminate the requirement for labeling and with the ability to identify corrupted fingerprints or signals not included in the training area.
This cumulative thesis is built around three main publications. In the first publication a novel channel charting algorithm for ToA based localization systems is contributed. Channel charting allows to model the radio geometry, i.e., relative coordinates of CSI in space, to enable self-supervised fingerprinting. We formalized a novel geodesic distance metric between pairs of CIR measurements for high bandwidth radio systems. Our metric allows to model global channel charts, enabling self-supervised fingerprinting. The efficiency of our approach is investigated on two real-world datasets, highly outperforming supervised fingerprinting with only a few data samples available. The second publication contributes a radio system independent channel charting approach based on velocity information. Given noisy velocity estimation derived from, e.g., odometry systems or PDR systems, data-independent distances between CSI can be derived by simple integration over time. We also propose an adaptive map matching algorithm to entirely avoid reference positions. Our evaluations show that we achieve comparable results to supervised fingerprinting without the requirement of expensive ground truth reference positions. To ensure reliable fingerprinting, in the last publication a model monitoring approach is contributed. Our approach is based on the uncertainty estimation of neural networks to identify corrupted fingerprints and the spatial limitations of a fingerprinting model. This enables an efficient maintenance process and allows the deployment of fingerprinting models only in NLoS dominated areas.
In conclusion, a novel fingerprinting concept is proposed, which exploits the intrinsic structure of measured CSI to lower the effort for labeling, while our model monitoring approaches enhance the reliability. In combination, the proposed algorithms help to lower the expenses for deployment and maintenance and enable robust radio localization in infrastructure-restricted, NLoS dominated areas, rendering radio-based fingerprinting a viable alternative to classical ToA radio localization
From Compression of Wearable-based Data to Effortless Indoor Positoning
Full text linkCotutela: Universidad de defensa de la tesis doctoral Tampere University
Doctorat InternacionalThe dissertation focuses on boosting the energy efficiency of IoT and wearable devices by implementing lossy compression techniques onto sensor-based time-series data and into indoor localization paradigms. The thesis deals with lossy compression mechanisms that can be implemented for energy-e¿cient, delay-sensitive wearable data gathering, transfer, and storage. The novel DLTC compression method ensures optimal compression ratio and reconstruction error trade-off, with minimum complexity and delay. In the scope of indoor positioning, the proposed bit-level, feature-wise, and sample-wise reduction of the radio map supports accurate positioning while saving resources in data storage and transfer. The work implements a multi-dimensional compression of the radio map to boost the performance e¿ciency of the positioning system and proposes a cascade model to compensate for k-NN¿s drawback of computationally expensive prediction on voluminous datasets
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