Hybrid positioning with smart phones

Ubiquitous Positioning and Mobile Location-Based Services in Smart Phones

Volumn , Issue , 2012, Pages 159-194

  Liu, Jingbin ^a  

^a FINNISH GEODETIC INSTITUTE   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84870513828     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-4666-1827-5.ch007     Document Type: Chapter

References (63)

1. Anderson, F. C., & Pandy, M. G. (2001). Dynamic optimization of human walking. Journal of Biomechanical Engineering, 123, 381-390. doi:10.1115/1.1392310
2. Andreotti, M., Aquino, M., Woolfson, M., Walker, J., & Moore, T. (2006). Signal propagation analysis and signature extraction for GNSS indoor positioning. Proceedings of IEEE/ION PLANS 2006, San Diego, CA, April 2006, (pp. 913-919).
3. Apple Inc. (2010). Retrieved March 29, 2011, from http://www.apple.com/iphone/specs.html
4. Ardö, H., Åström, K., & Berthilsson, R. (2007). Real time Viterbi optimization of hidden Markov models for multi target tracking. In Proceedings of IEEE Workshop on Motion and Video Computing (WMVC07), (p. 2). Texas, USA.
5. Axelrad, P., & Brown, R. G. (1996). GPS navigation algorithms. In Parkinson, B. W., & Spilker, J. J. Jr., (Eds.), Global positioning systems: Theory and applications (Vol. 1 and 2). Washington, DC, USA: American Institute of Aeronautics and Astronautics.
6. Baclawski, K. P. (2004). Bayesian network development. In Proceedings of the International Workshop on Software Methodologies, Tools and Techniques, (pp. 18-48). Tokyo, Japan.
7. Bahl, P., & Padmanabhan, V. (2000). An inbuilding RF based user location and tracking system. In Proceedings of IEEE INFOCOM (pp. 775-784). Tel-Aviv, Israel: Radar. doi:10.1109/INFCOM.2000.832252
8. Beauregard, S., & Haas, H. (2006). Pedestrian dead reckoning: A basis for personal positioning. In Proceedings of the 3rd Workshop on Positioning, Navigation and Communication (WPNC06), (pp. 27-35).
9. Bengio, Y. (1997). The Viterbi algorithm. Retrieved July 7, 2011, from http://www.iro.umontreal.ca/~bengioy/ift6265/hmms/node9.html#SECTION00023000000000000000
10. Brown, R., & Hwang, P. Y. C. (1997). Introduction to random signals and applied kalman filtering. New York, NY: John Wiley & Sons Inc.
11. Chai, X., & Yang, Q. (2005). Multiple-goal recognition from low-level signals. In Proceedings of the Twentieth National Conference on Artificial Intelligence, (pp. 3-8). Pittsburgh, USA.
12. Chiou, Y.-S., Wang, C.-L., & Yeh, S.-C. (2006). An adaptive location estimator based on Kalman filtering for dynamic indoor environments. In Proceedings of IEEE Vehicular Technology Conference, (pp. 1-5). Montreal, Canada.
13. Forum, N. F. C. (2010). NFC digital protocol technical specification. Retrieved July 27, 2011, from http://www.nfc-forum.org/specs/spec_license/document_form/custom_layout?1311768136666
14. Foxlin, E. (2005). Pedestrian tracking with shoemounted inertial sensors. Computer Graphics and Applications, 25(6), 38-46. doi:10.1109/MCG.2005.140
15. Frank, K., Vera-Nadales, M. J., Robertson, P., & Angermann, M. (2010). Reliable real-time recognition of motion related human activities using MEMS inertial sensors. In Proceedings of ION GNSS, 2010, 2919-2932.
16. Gennery, D. B. (1992). Visual tracking of known three-dimensional objects. International Journal of Computer Vision, 7(3), 243-270. doi:10.1007/BF00126395
17. Ghahramani, Z. (2001). An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence, 15(1), 9-42. doi:10.1142/S0218001401000836
18. Ghahramani, Z., & Jordan, M. I. (1997). Factorial hidden Markov models. Machine Learning, 29, 245-273. doi:10.1023/A:1007425814087
19. Godha, S., Lachapelle, G., & Cannon, M. E. (2006). Integrated GPS/INS system for pedestrian navigation in a signal degraded environment. Paper presented at ION GNSS 2006, TX, USA.
20. Hansen, R., Wind, R., Jensen, C. S., & Thomsen, B. (2009). Seamless indoor/outdoor positioning handover for location-based services in Streamspin. 2009 Tenth International IEEE Conference on Mobile Data Management Systems Services and Middleware, (pp. 267-272). Retrieved from http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5088943
21. Haug, A. J. (2005). A tutorial on Bayesian estimation and tracking techniques applicable to nonlinear and non-Gaussian process. MITRE Technical Report MTR 05W0000004. Retrieved July 21, 2011 from http://www.mitre.org/work/tech_papers/tech_papers_05/05_0211/05_0211.pdf
22. Hoff, B., & Azuma, R. (2000). Autocalibration of an electronic compass in an outdoor augmented reality system, (pp. 159-164). Paper presented at the meeting of IEEE and ACM International Symposium on Augmented Reality, Munich, Germany.
23. Huynh, D. T. G. (2008). Human activity recognition with wearable sensors. Technische Universität Darmstadt, Dr.-Ing Thesis, Germany. Retrieved November 07, 2011, from http://tuprints.ulb.tudarmstadt.de/1132/1/Dissertation_Tam_Huynh.pdf
24. Jie, Y., Qiang, Y., & Lionel, N. (2008). Learning adaptive temporal radio maps for signal-strengthbased location estimation. IEEE Transactions on Mobile Computing, 7(7), 869-883. doi:10.1109/TMC.2007.70764
25. Kim, J. W., Han, J. J., Hwang, D.-H., & Chansik, P. (2004). A step, stride and heading determination for the pedestrian navigation system. Journal of Global Positioning Systems, 3(1-2), 273-279. doi:10.5081/jgps.3.1.273
26. Koozyt Inc. (2007). Retrieved November 22, 2011, from http://www.placeengine.com/en
27. Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249-268.
28. Kushki, A., Plataniotis, K. N., & Venetsanopoulos, A. N. (2006). Location tracking in Wireless Local Area Networks with adaptive radio maps. Meeting of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 5, (pp. 741-744).
29. Kushki, A., Plataniotis, K. N., & Venetsanopoulos, A. N. (2007). Kernel-based positioning in wireless local area networks. IEEE Transactions on Mobile Computing, 6(6), 689-705. doi:10.1109/TMC.2007.1017
30. Kushki, A., Plataniotis, K. N., & Venetsanopoulos, A. N. (2010). Intelligent dynamic radio tracking in indoor wireless local area networks. IEEE Transactions on Mobile Computing, 9(3), 405-419. doi:10.1109/TMC.2009.141
31. Liu, J., Chen, R., Pei, L., Chen, W., Tenhunen, T., Kuusniemi, H., et al. (2010). Accelerometer assisted robust wireless signal positioning based on a hidden Markov model. In Proceedings of Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION, Palm Springs, CA.
32. Liu, S., Zhang, Z., & Hung, J. (1989). A high accuracy magnetic heading system composed of fluxgate magnetometer and a microcomputer. Proceedings of the IEEE National Aerospace and Electronics Conference, 1, 148-152.
33. Maria Josefa, V. N. (2010). Recognition of human motion related activities from sensors. Master Thesis, University of Malaga, Spain.
34. Martin-Escalona, I., Barcelo-Arroyo, F., & Ciurana, M. (2011). Middleware for positioning in cellular networks. In Melikov, A. (Ed.), Cellular networks - Positioning, performance analysis, reliability (p. 88). InTech Publisher. doi:10.5772/15379
35. Mezentsev, O. A. (2005). Sensor aiding of HSGPS pedestrian navigation. PhD Dissertation, Geomatics Engineering, University of Calgary, Canada.
36. Moafipoor, S. (2009). Intelligent personal navigator supported by knowledge-based systems for estimating dead reckoning navigation parameters. PhD Dissertation, The Ohio State University, USA.
37. Moafipoor, S., Grejner-Brzezinska, D. A., & Toth, C. K. (2007). Adaptive calibration of a magnetometer compass for a personal navigation system. Proceedings of International Global Navigation Satellite Systems Society IGNSS Symposium, Sydney, Australia, Retrieved July 2, 2011 from http://www.ignss.org/LinkClick.aspx?fileticket=7oH5%2FnHXZyY%3D&tabid=88&mid=424
38. Moon, T. K., & Stirling, W. C. (2000). Mathematical methods and algorithms for signal processing. Prentice Hall.
39. Navizon Inc. (2010). Retrieved November 22, 2011, from http://www.navizon.com/index.php
40. Ning, H., Wang, L., Hu, W., & Ta, T. (2002). Model-based tracking of human walking in monocular image sequence. The 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, Beijing, China, Vol. 1, (pp. 537-540).
41. Nokia Corporation. (2010). Retrieved March 29, 2011, from http://europe.nokia.com/findproducts/devices/nokian8/specifications
42. Ojeda, L., & Borenstein, J. (2007). Non-GPS navigation for security personnel and first responders. Journal of Navigation, 60(3), 391-403. doi:10.1017/S0373463307004286
43. openBmap. (2011). Retrieved November 22, 2011, from http://openbmap.org/
44. Papagelis, A., & Kalles, D. (2001). Breeding decision trees using evolutionary techniques. Eighteenth International Conference on Machine Learning, (pp. 393-400).
45. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.
46. Pei, L., Chen, R., Liu, J., Chen, W., Kuusniemi, H., Tenhunen, T., et al. (2010). Motion recognition assisted indoor wireless navigation on a mobile phone. Meeting of ION GNSS 2010, (pp. 3366-3375). Portland, USA.
47. Petovello M. (2011). Absolutely relative positioning. InsideGNSS, 6(3), 32-36.
48. Pirttikangas, S., Fujinami, K., & Nakajima, T. (2006). Feature selection and activity recognition from wearable sensors (pp. 516-527). Ubiquitous Computing Systems.
49. Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286. doi:10.1109/5.18626
50. Randell, C., & Muller, H. (2000). Context awareness by analyzing accelerometer data. Fourth IEEE International Symposium on Wearable Computers, (p. 175). Washington DC, USA.
51. Ristic, B., Arulampalam, S., & Gordon, N. (2004). Beyond the Kalman filter: Particle filters for tracking applications. Artech House Publishers.
52. Rolland, J. P., Davis, L. D., & Baillot, Y. (2001). A survey of tracking technology for virtual environments. In Bardfield, W., & Caudell, T. (Eds.), Rolland, J. P., Davis, L. D., & Baillot, Y (pp. 67-112). Lawrence Erlbaum Press.
53. Saarinen, J. (2009). A sensor-based personal navigation system and its application for incorporating humans into a human-robot team. Ph.D. dissertation, Helsinki University of Technology, Finland.
54. Samsung Electronics. (2011). Retrieved July 22, 2011, from http://www.samsung.com/uk/galaxys/pecs.html
55. Särkkä, S. (2010). Bayesian estimation of timevarying processes: Discrete-time systems. Aalto University. Retrieved May 15, 2011 from http://www.lce.hut.fi/~ssarkka/course_k2010/full_course_booklet.pdf
56. Simic, M., & Pejovic, P. (2011). Positioning in cellular networks. In Melikov, A. (Ed.), Cellular networks - Positioning, performance analysis, reliability. InTech Publisher. doi:10.5772/14698
57. Skyhook Inc. (2011). Retrieved November 22, 2011, from http://www.skyhookwireless.com/
58. Stiefmeier, T. (2008). Real-time spotting of human activities in industrial environments. Ph.D. dissertation, TU Darmstadt, Germany.
59. Van Dierendonck, A. J. (1996). GPS receivers. In Parkinson, B. W., & Spilker, J. J. Jr., (Eds.), Global positioning systems: Theory and applications (Vol. 1 and 2). Washington, DC: American Institute of Aeronautics and Astronautics.
60. Wang, J. H., & Gao, Y. (2005). A new magnetic compass calibration algorithm using neural networks. Measurement Science & Technology, 17, 153-160. doi:10.1088/0957-0233/17/1/025
61. Welch, L. R. (2003). Hidden Markov models and the Baum-Welch algorithm. IEEE Information Theory Society Newsletter, 53(4), 1-13.
62. Willaredt, J. (2011). WiFi and cell-ID based positioning - Protocols, standards and solutions. SNET Project WT 20102011. Retrieved July 2, 2011, from http://www.snet.tu-berlin.de/fileadmin/fg220/courses/WS1011/snet-project/wifi-cellid-positioning_willaredt.pdf
63. Yin, J., Chai, X., & Yang, Q. (2004). High-level goal recognition in a wireless LAN. Meeting of the 19th National Conference on Artificial Intelligence, (pp. 578-584).