6 research outputs found

    Multiple Antenna-based GPS Multipath Mitigation using Code Carrier Information

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐ๊ณตํ•™๋ถ€, 2013. 8. ์ตœ์ง„์˜.์—ฌ๋Ÿฌ ์‘์šฉ๋ถ„์•ผ์—์„œ ์ˆ˜ ์–ต๋Œ€์˜ GPS(Global Positioning System) ์ˆ˜์‹ ๊ธฐ๊ฐ€ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, GPS์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์œ„์น˜๊ธฐ๋ฐ˜ ์„œ๋น„์Šค(LBS: Location Based Services)์—์„œ๋Š” ์—ฌ์ „ํžˆ ๋‹ค์ค‘๊ฒฝ๋กœ ์˜ค์ฐจ์™€ ๊ฐ™์€ ์ „ํŒŒ ๋ฐฉํ•ด๊ฐ€ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ์˜ค์ฐจ๋“ค๋กœ ์ธํ•˜์—ฌ ์ƒ๊ด€ํ•จ์ˆ˜์˜ ์™œ๊ณก์€ ๊ฑฐ๋ฆฌ ์˜ค์ฐจ๊ฐ€ ๋ฐœ์ƒ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ์ธํ•˜์—ฌ GPS์„ ์ด์šฉํ•œ ํ•ญ๋ฒ• ์‹œ์Šคํ…œ์—์„œ์˜ ์œ„์น˜ ์ •ํ™•๋„ ํ–ฅ์ƒ์„ ์œ„ํ•˜์—ฌ, ๋‹ค์ค‘๊ฒฝ๋กœ ์˜ค์ฐจ๋ฅผ ํšจ๊ณผ ์ ์œผ๋กœ ์ค„์ด๊ธฐ ์œ„ํ•œ ๊ฐ•์ธํ•˜๊ณ  ํ˜„์‹ค์ ์ธ ๋ฐฉ๋ฒ•์ด ์š”๊ตฌ๋œ๋‹ค. ๋‹ค์ค‘๊ฒฝ๋กœ๋Š” GPS ์‹ ํ˜ธ๊ฐ€ ์žฅ์• ๋ฌผ์— ์˜ํ•ด ๋ฐ˜์‚ฌ๋‚˜ ํšŒ์ ˆ ๋˜์–ด ์ˆ˜์‹ ๊ธฐ์— ๋„์ฐฉํ•  ๋•Œ ์ž˜ ์ผ์–ด๋‚œ๋‹ค. ๊ฐ€์‹œ๊ฒฝ๋กœ ์‹ ํ˜ธ์— ๊ฒฐํ•ฉ๋œ ๋‹ค์ค‘๊ฒฝ๋กœ ์‹ ํ˜ธ๋Š” GPS ์ˆ˜์‹ ๊ธฐ์˜ ์ƒ๊ด€ํ•จ์ˆ˜์˜ ๋ณ€ํ˜•์„ ์ผ์œผํ‚ค๋ฉฐ ๊ถ๊ทน์ ์œผ๋กœ ์ฐจ๋ณ„ํ•จ์ˆ˜์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฏ€๋กœ ๊ฑฐ๋ฆฌ์˜ค์ฐจ๋ฅผ ๋ฐœ์ƒ์‹œํ‚จ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋‹ค์ค‘๊ฒฝ๋กœ ์˜ค์ฐจ๋Š” ์œ„์„ฑํ•ญ๋ฒ• ์‹œ์Šคํ…œ์—์„œ์˜ ์œ„์น˜์ •ํ™•๋„ ํ–ฅ์ƒ์„ ์œ„ํ•ด ํ•ด๊ฒฐ ๋˜์–ด์•ผ ๋  ๋ฌธ์ œ๋กœ ์Ÿ์ ์ด ๋˜์–ด์™”๋‹ค. ์ตœ๊ทผ์—๋Š” ์ด๋Ÿฌํ•œ ์ „ํŒŒ ๊ฐ„์„ญ์‹ ํ˜ธ๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•˜์—ฌ ๋‹ค์ค‘๊ฐœ์˜ ์•ˆํ…Œ๋‚˜(Multiple Antenna)๋ฅผ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์ด GPS ํ•ญ๋ฒ• ์‹œ์Šคํ…œ์—์„œ ์ด์šฉ๋˜๊ณ  ์žˆ๋‹ค. ํ˜„ ์‹œ์ ์—์„œ, ๋‹ค์ค‘๊ฐœ์˜ ์•ˆํ…Œ๋‚˜๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ์‘์šฉ๋ถ„์•ผ๋Š” ์ฃผ๋กœ ํ•™์ˆ ์ ์ธ ์—ฐ๊ตฌ ๋ฐ ๋ณต์žกํ•œ ๊ตฐ์‚ฌ์šฉ ์—ฐ๊ตฌ๋กœ ์ฃผ๋กœ ์ง„ํ–‰ ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์•ˆํ…Œ๋‚˜ ์ œ์ž‘ ๋ฐฉ๋ฒ• ๋ฐ ์ „๊ธฐ์  ์‹œ์Šคํ…œ์˜ ๊ธ‰๊ฒฉํ•œ ๋ฐœ์ „์œผ๋กœ ์ธํ•ด ์ด์ „์˜ ํ•˜๋“œ์›จ์–ด ๋ฐ ์†Œํ”„์›จ์–ด์ ์ธ ๋ฌธ์ œ๋ฅผ ์‰ฝ๊ฒŒ ํ•ด๊ฒฐ ๋จ์— ๋”ฐ๋ผ ๊ฐ€๊นŒ์šด ๋ฏธ๋ž˜์—๋Š” ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ๊ธฐ๋ฐ˜์˜ ์ˆ˜์‹ ๊ธฐ๊ฐ€ ๋ฏผ๊ฐ„ ์ƒ์šฉ๋ถ„์•ผ๋กœ ํ™•๋Œ€ ๋  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ์ด ๋œ๋‹ค. ๋˜ํ•œ ์•ˆํ…Œ๋‚˜ ์ˆ˜์‹ ๊ธฐ RF๋‹จ์˜ ์†Œํ˜•ํ™”๋กœ ์ธํ•˜์—ฌ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ์˜ ์•ˆํ…Œ๋‚˜ ํฌ๊ธฐ ๋ฌธ์ œ์  ๋˜ํ•œ ํ•ด๊ฒฐ ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์ค‘ GPS ์•ˆํ…Œ๋‚˜๋ฅผ ์ด์šฉํ•˜์—ฌ GPS ํ•ญ๋ฒ•์—์„œ์˜ ์ „ํŒŒ ๊ฐ„์„ญ ๋ฐ ๋‹ค์ค‘๊ฒฝ๋กœ ์˜ค์ฐจ ๊ฐ์‡„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ•ํ•œ ์ „ํŒŒ ๊ฐ„์„ญ ๋ฐ ๋‹ค์ค‘๊ฒฝ๋กœ ์‹ ํ˜ธ์— ๋Œ€ํ•˜์—ฌ ๊ณต๊ฐ„ ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์„ ์ ์šฉํ•œ๋‹ค. ์ œ์•ˆ๋œ ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์€ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜๋ฅผ ๊ธฐ๋ฐ˜์˜ ์ฝ”๋“œ ์ผ€๋ฆฌ์–ด ์ •๋ณด๋ฅผ ์ด์šฉํ•œ ๊ณต๊ฐ„์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์œผ๋กœ ์ „ํŒŒ ๊ฐ„์„ญ ๋ฐ ๋‹ค์ค‘๊ฒฝ๋กœ ์˜ค์ฐจ๋ฅผ ์™„ํ™”์‹œํ‚ค๋ฉฐ, ๋˜ํ•œ ๋น”ํ˜•์„ฑ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์‹ ํ˜ธ ๋Œ€ ์žก์Œ ๋น„์œจ์„ ์ตœ๋Œ€๋กœ ํ•œ๋‹ค. ์ œ์•ˆ๋œ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์†Œํ”„ํŠธ์›จ์–ด GPS ์ˆ˜์‹ ๊ธฐ๋ฅผ ์‚ฌ์šฉ๋œ๋‹ค. ์†Œํ”„ํŠธ์›จ์–ด GPS ์ˆ˜์‹ ๊ธฐ๋ฅผ ์ด์šฉํ•œ ์‹ ํ˜ธ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์€ ์ƒˆ๋กœ์šด ์žฅ๋น„์˜ ์ œํ’ˆํ™” ๋ฐ GPS ์‹ ํ˜ธ ๋ถ„์„์— ์žฅ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ GPS ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋ถ„์„ ๋ฐ ์ˆ˜์‹ ๊ธฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ ๊ฒ€์ฆ ๋“ฑ ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๋ถ„์•ผ์—์„œ ๋„๋ฆฌ ์ด์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์˜ ์„ฑ๋Šฅ ๊ฒ€์ฆ์„ ์œ„ํ•˜์—ฌ ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ๊ฐ€๊ณต IF ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ์†Œํ”„ํŠธ์›จ์–ด ์ˆ˜์‹ ๊ธฐ ๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ์ „ํŒŒ ๊ฐ„์„ญ ๋ฐ ๋‹ค์ค‘๊ฒฝ๋กœ ์˜ค์ฐจ ๊ฐ์‡„์— ๊ฐ•์ธํ•˜๋ฉฐ, GPS ํ•ญ๋ฒ•์‹œ์Šคํ…œ์—์„œ์˜ ์œ„์น˜์ •ํ™•๋„ ํ–ฅ์ƒ์— ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ค€๋‹ค. ๊ทธ๋กœ๋ฏ€๋กœ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ์ฐจ๋Ÿ‰ ํ•ญ๋ฒ• ์‘์šฉ๋ถ„์•ผ์—์„œ ๋ฐฉํ•ด์‹ ํ˜ธ ๊ฐ์‡„์— ์‚ฌ์šฉ๋  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค.Although hundreds of millions of receivers are used all around the world, the performance of location-based services(LBS) provided by GPS is still compromised by interference which includes unintentional distortion of correlation function due to multipath propagation. For this reason, the requirement for proper mitigation techniques becomes crucial in GPS receivers for robust, accurate, and reliable positioning. Multipath propagation can easily occur when environmental features cause combinations of reflected and diffracted replica signals to arrive at the receiving antenna. These signals which are combined with the original line-of-sight (LOS) signal can cause distortion of the receiver correlation function and ultimately distortion of the discrimination functionhence, errors in range estimation occur. Therefore, multipath error in the satellite navigation system to improve location accuracy is an important issue to be addressed. Recently, interference mitigation techniques utilizing multiple antennas have gained significant attention in GPS navigation systems. Although at the time of this dissertation, employing multiple antennas in GPS applications is mostly limited to academic research and possibly complicated military applications, it is expected that in the near future, antenna array-based receivers will also become widespread in civilian commercial markets. Rapid advances in antenna design technology and electronic systems make previously challenging problems in hardware and software easier to solve. Furthermore, due to the significant effort devoted to miniaturization of RF front-ends and antennas, the size of antenna array based receivers will no longer be a problem. Given the above, this dissertation investigates multiple antenna-based GPS the interference suppression and multipath mitigation. Firstly, a modified spatial processing technique is proposed that is capable of mitigating both high power interference and coherent and correlated GPS multipath signals. The use of spatial-temporal processing for GPS multipath mitigation is studied. A new method utilizing code carrier information based on multiple antennas is proposed to deal with highly correlated multipath components and to increase the signal to noise ratio of the beamformer by synthesizing antenna array processing. In order to verify the proposed method, a software defined GPS receiver is used. Software-based GPS signal processing technique has already produced benefits for prototyping new equipment and analyzing GPS signal quality. Not only do such receivers provide an excellent research tool for GPS algorithm verification, they also improve GPS receiver performance in a wide range of conditions. In this dissertation, the enhancement of the proposed method is presented in terms of the simulations and software defined GPS receiver using simulated IF data. From the result, the proposed method is robust to interference suppression, and multipath mitigation, and shows a strong possibility for use in improving location accuracy. Thus, this method can be employed to mitigate interference signals in vehicular navigation applications.Contents Abstract i Acknowledgements iv Contents v List of Figures x List of Tables xiv Chapter 1.Introduction 1 1.1 Introduction 1 1.2 Background and Motivation 2 1.2.1 Strong Narrowband and Wideband Interference 6 1.2.2 Multipath 7 1.3 Antenna Array Processing in GPS 11 1.3.1 Interference Suppression 11 1.3.2 Multipath Mitigation 13 1.4 Software-Defined GPS Receiver 15 1.5 Objective and Contribution 17 1.6 Dissertation Outline 18 Chapter 2. Global Positioning System 21 2.1 GPS System Overview 21 2.2 Basic Concept of GSP 25 2.3 Determining Satellite to User 28 2.4 Calculation of User Position 33 2.5 GPS Error Sources 40 2.5.1 Receiver Clock Bias 41 2.5.2 Satellite Clock Bias 42 2.5.3 Atmospheric Delay 43 2.5.4 Ephemeris Delay 46 2.5.5 Multipath Error 47 2.5.6 Receiver Noise 55 2.6 Summary 55 Chapter 3. Antenna Array Processing and Beamforming 56 3.1 Background on Antenna Arrays and Beamformers 56 3.1.1 Signal Model 59 3.2 Conventional Optimum Beamformers 69 3.2.1 Minimum Variance Distortionless Response Beamformer 69 3.2.2 Maximum Likelihood Estimator 71 3.2.3 Maximum Signal to Noise Interference Ratio Beamformer 72 3.2.4 Minimum Power Distortionless Response Beamformer 75 3.2.5 Linear Constrained Minimum Variance and Linear Constrained Minimum Power Beamformers 76 3.2.6 Eigenvector Beamformer 77 3.3 Space-Time Processing 81 3.4 Array Calibration 85 3.5 Summary 86 Chapter 4. Multipath Mitigation using Code-Carrier Information 87 4.1 Introduction 87 4.2 Interference Suppression and Multipath Mitigation 88 4.2.1 Signal Model 88 4.2.2 Interference Suppression by Subspace Projection 90 4.2.3 Multipath Mitigation by Subspace Projection 93 4.3 Determination of Multipath Satellites using Code-carrier Information 95 4.4 MSR Beamformer 100 4.5 Simulation Results 102 4.5.1 Subspace Projection and Beamforming 102 4.5.2 Performance Comparison 109 4.6 Summary 111 Chapter 5. Performance Verification using Software-Defined GPS Receiver 113 5.1 Introduction 113 5.2 Software-Defined GPS Receiver Methodology 114 5.2.1 Software-Defined GPS Receiver Signals 115 5.2.2 Software-Defined GPS Receiver Modules 116 5.3 Architecture of Software-Defined GPS Receiver 120 5.3.1 GPS Signal Generation 120 5.3.2 Interference Signal Generation 124 5.3.1 Front-End Signal Processing 125 5.4 Experimental Results 126 5.3.1 Static Environments 128 5.3.2 Dynamic Environments 133 5.5 Summary 136 Chapter 6. Conclusions and Future Work 138 6.1 Conclusions 138 6.2 Future Work 139 Bibliography 142 Appendix 168 Abstract in Korean 170 Acknowledgments 173Docto

    Visual and Camera Sensors

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    This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

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    In recent times, we have seen an incredible growth of users adopting mobile devices andwearables, and while the hardware capabilities of these devices have greatly increased year after year, mobile communications still remain a bottleneck for most applications. This is partially caused by the companiesโ€™ cloud infrastructure, which effectively represents a large scale communication hub where all kinds of platforms compete with each other for the serversโ€™ processing power and channel throughput. Additionally, wireless technologies used in mobile environments are unreliable, slow and congestion-prone by nature when compared to the wired medium counterpart. To fix the back-and-forth mobile communication overhead, the โ€œEdgeโ€ paradigm has been recently introduced with the aim to bring cloud services closer to the customers, by providing an intermediate layer between the end devices and the actual cloud infrastructure, resulting in faster response times. Publish/Subscribe systems, such as Thyme, have also been proposed and proven effective for data dissemination at edge networks, due to the interactionsโ€™ loosely coupled nature and scalability. Nonetheless, solely relying on P2P interactions is not feasible in every scenario due to wireless protocolsโ€™ range limitations. In this thesis we propose and develop Thyme- Infrastructure, an extension to the Thyme framework, that utilizes available stationary nodes within the edge infrastructure to not only improve the performance of mobile clients within a BSS, by offloading a portion of the requests to be processed by the infrastructure, but also to connect multiple clusters of users within the same venue, with the goal of creating a persistent and global end-to-end storage network. Our experimental results, both in simulated and real-world scenarios, show adequate response times for interactive usage, and low energy consumption, allowing the application to be used in a variety of events without excessive battery drainage. In fact, when compared to the previous version of Thyme, our framework was generally able to improve on all of these metrics. On top of that, we evaluated our systemโ€™s latencies against a full-fledged cloud solution and verified that our proposal yielded a considerable speedup across the board

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    Actas del XIX Workshop de Investigadores en Ciencias de la Computaciรณn (WICC 2017), realizado en el Instituto Tecnolรณgico de Buenos Aires (ITBA), el 27 y 28 de abril de 2017.Red de Universidades con Carreras en Informรกtica (RedUNCI

    Analyzing Granger causality in climate data with time series classification methods

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    Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
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