Development of Geolocation Data Compression for Transportation Target Identification
Abstract
Applications of an intelligent transportation system (ITS) often require wireless communication networks to support navigation and location identification, which is emerging as an important research issue in ITS. Of the various infrastructures for supporting ITS development, the wireless sensor network is the most commonly used medium to transmit data from a target source to the receiving sensors, between sensors, or both. Such data, often referred to as geolocation data, can then be used to calculate the time difference of arrival (TDOA) to the various sensors for the purpose of precise location identification. However, the limitation on the system bandwidth and energy resources motivates the use of data compression within the network when data are transmitted between sensors. This paper presents three data compression approaches specifically designed for geolocation data transmitted between sensors within a wireless communication network for the purposes of transportation target identification and location-finding. The approaches include the wavelet transform with arithmetic encoding method, the wavelet packet analysis with the Fisher-information method, and the Texas Southern University model (the monotone increase method). The effectiveness of each presented method is evaluated by calculation of the precision of TDOA from the decompressed data, which are used for determination of source location. Sensitivity analysis is conducted on all the presented methods in relation to compression ratio, signal-to-noise ratio, and computational time. A comparison of the three methods permits recommendation of the optimal compression tool for various applications.
Get full access to this article
View all access and purchase options for this article.
References
1. Zhao Y. Mobile Phone Location Determination and Locating on Intelligent Transportation System. IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 1, March 2000, pp. 55–64.
2. Sayed A. H., Tarighat A., and Khajehnouri N. Network-Based Wireless Location. Signal Processing, Vol. 22, July 2005, pp. 24–40.
3. Zhao Y. Vehicle Navigation and Information Systems. Wiley Encyclopedia of Electrical and Electronics Engineering, Vol. 23 (Webster J. G., ed.), Wiley, New York, 1999, pp. 106–118.
4. Stein S. Algorithms for Ambiguity Function Processing. IEEE Transactions on Signal Processing, Vol. 41, Aug. 1993, pp. 2717–2719.
5. Fowler M. L. Non-MSE Wavelet-Based Data Compression for Emitter Location. Presented at Conference on Mathematics and Applications of Data/Image Coding, Compression and Encryption IV, San Diego, Calif., July 29–Aug. 2, 2001.
6. Drane C., and Rizons C. Positioning Systems in Intelligent Transportation Systems. Artech House, Norwood, Mass., 1998.
7. Liberti J. C., and Rappaport T. S. Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications. Prentice-Hall, Upper Saddle River, N.J., 1999, chs. 9 and 10.
8. Caffery J. J. Wireless Location in CDMA Cellular Radio Systems. Kluwer, Norwell, Mass., 1999.
9. Shin D., and Sung T. Analysis of Positioning Errors in Radionavigation Systems. Proc., IEEE Intelligent Transportation Systems Conference, Oakland, Calif., Aug. 25–29, 2001.
10. Zhu L., and Zhu J. A New Model and Its Performance for TDOA Estimation. Proc., IEEE Vehicular Technology Conference, 2001. Oct. 7–11, 2001, Vol. 4, pp. 2750–2753.
11. Stewart A. D. Comparing Time-Based and Hybrid Time-Based/Frequency Based Multi-Platform Geo-Location Systems. MS thesis. Naval Postgraduate School, Monterey, Calif., Sept. 1997.
12. Azaria M., and Hertz D. Time Delay Estimation by Generalized Cross Correlation Methods. IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 32, No. 2, April 1984, pp. 280–285.
13. Knapp C., and Carter G. C. The Generalized Correlation Method for Estimation of Time Delay. IEEE Transactions of Acoustics, Speech, and Signal Processing, Vol. 24, No. 4, Aug. 1976, pp. 320–327.
14. Streight D. A. Application of Cyclostationary Signal Selectivity to the Carry-On Multi-Platform GPS Assisted Time Difference of Arrival System, MS thesis. Naval Postgraduate School, Monterey, Calif., March 1997.
15. Scaglione A., and Servetto S. On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks. Presented at MOBICOM'02, Atlanta, Ga., Sept. 23–26, 2002.
16. Chen M., and Fowler M. L. The Importance of Data Compression for Energy Efficiency in Sensor Networks. Presented at 2003 Conference on Information Sciences and Systems, Johns Hopkins University, Baltimore, Md., March 12–14, 2003.
17. Fowler M. L., and Chen M. Fisher-Information-Based Data Compression for Estimation Using Two Sensors. IEEE Transactions on Aerospace and Electronic Systems, July 2005, pp. 1131–1137.
18. Fowler M. L., and Chen M. Optimizing Non-MSE Distortion for Data Compression in Emitter Location Systems. Presented at Conference on Information Sciences and Systems, Johns Hopkins University, Baltimore, Md., March 12–14, 2003.
19. Fowler M. L., and Chen M. Geometry-Adaptive Data Compression for TDOA/FDOA Location. Proc., IEEE International Conference on Acoustics, Speech, and Signal Processing 2005, Philadelphia, Pa., March 18–23, 2005, Vol. 4, pp. 1069–1072.
20. Fowler M. L., and Chen M. Evaluating Fisher Information from Data for Task-Driven Data Compression. Proc., IEEE 40th Annual Conference on Information Sciences and Systems, Princeton, N.J., March 2006, pp. 967–972.
21. Chen M. Data Compression for Inference Tasks in Wireless Sensor Networks. PhD dissertation. Binghamton University, State University of New York, Binghamton, 2006.
22. Yu L., Liu X., and Chen X. Data Compression for Emitter Location Finding in Sensor Networks. IEEE Proceedings of Information Technology: New Generations, 2008, pp. 1210–1215.
23. Perkins W. Data Compression with Application to Geo-Location. MS thesis. Louisiana State University, Baton Rouge, 2007.
24. Tammana G. A., and Zheng Y. F. Wavelet Packet and TCQ Coding for SAR Raw Data Compression. Journal of Wavelet Theory and Application., Vol. 1, No. 1, 2007, pp. 97–114.
25. Witten I. H., Radford M. N., and Cleary J. G. Arithmetic Coding for Data Compression. Communications of the ACM, Vol. 30, No. 6, 1987, pp. 520–540.
26. Zhang Y., and Li J. Linear Predictor-Based Lossless Compression of Vibration Sensor Data: Systems Approach. Journal of Engineering Mechanics, Vol. 133, No. 4, April 2007, pp. 431–441.
Cite article
Cite article
Cite article
OR
Download to reference manager
If you have citation software installed, you can download article citation data to the citation manager of your choice
Information, rights and permissions
Information
Published In
Article first published online: January 1, 2009
Issue published: January 2009
Authors
Metrics and citations
Metrics
Journals metrics
This article was published in Transportation Research Record: Journal of the Transportation Research Board.
VIEW ALL JOURNAL METRICSArticle usage*
Total views and downloads: 6
*Article usage tracking started in December 2016
Altmetric
See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores
Articles citing this one
Receive email alerts when this article is cited
Web of Science: 0
Crossref: 2
- Lossy Data Compression for IoT Sensors: A Review
- Spatial Sampling with Fisher Information for Optimal Maintenance Manag...
Figures and tables
Figures & Media
Tables
View Options
Get access
Access options
If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:
loading institutional access options
Alternatively, view purchase options below:
Purchase 24 hour online access to view and download content.
Access journal content via a DeepDyve subscription or find out more about this option.
