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] National Marine Electronics Association, NMEA 0183: Standard for interfacing marine electronic devices, version 3.01, 2002. [11] Stateczny A., AIS and Radar Data Fusion for Maritime Navigation , ‘Scientific Journals of the Maritime University of Szczecin’, 2004, No. 2, pp. 329–336. [12] Terma A/S, SCANTER Track Management Protocol, Document No. 303949 SI, Denmark, 2011, [online], [access 21.04.2019]. [13] Terma A/S, Scanter, Video Distribution and Tracking Unit, SCANTER 2001, Product Specification

References 1. Bar-Shalom Y., Willett P., Tian X.: Tracking and data fusion. YBS Publishing, Storrs 2011 2. Borkowski P.: Algorithm of multi-sensor navigational data fusion - testing of estimation quality. Polish Journal of Environmental Studies, Vol. 17, No. 3B, 2008 (43-47) 3. Borkowski P., Pietrzykowski Z., Magaj J. Mąka M.: Fusion of data from GPS receivers based on a multi-sensor Kalman filter. Transport Problems, Vol. 3, No. 4, 2008 (5-11) 4. Borkowski P., Stateczny A.: An algorithm of navigational data integration. Maintenance Problems No. 68, 2008

References 1. Anand, A., Ramadurai, G. and Vanajakshi, L. (2013). Data Fusion Based Traffic Density Estimation and Prediction, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, accepted author version, DOI: 10.1080/15472450.2013.806844 2. Bachmann, C. (2011). Multi-Sensor Data Fusion for Traffic Speed and Travel Time Estimation , MSc Thesis, Toronto 3. Böker, G., Lunze, J. (2002). Stability and performance of switching Kalman filters, International Journal of Control , 75(16/17): 1269-1281 4. Claudel, C. G., Bayen, A. M

-Jiménez, N. Burrus, and M. Abderrahim, “A-Contrario Detection of Aerial Target Using a Time-of-Flight Camera,” Sensor Signal Processing for Defence (SSPD 2012), 2012. [9] A. Canclini, L. Baroffio, M. Cesana, A. Redondi, and M. Tagliasacchi “Object recognition in visual sensor networks based on compression and transmission of binary local features,” [Online]. Available: [10] F. Castanedo, “A Review of Data Fusion Techniques

. 805–829. Castanedo, F. (2013). A review of data fusion techniques, The Scientific World Journal 2013 : 704504, DOI: 10.1155/2013/704504. Cha, Y.-H., Ha, Y.-C., Yoo, J.-I., Min, Y.-S., Lee, Y.-K. and Koo, K.-H. (2017). Effect of causes of surgical delay on early and late mortality in patients with proximal hip fracture, Archives of Orthopaedic and Trauma Surgery 137 (5): 625–630. de Bruijne, M. (2016). Machine learning approaches in medical image analysis: From detection to diagnosis, Medical Image Analysis 33 : 94–97, DOI: 10.106/ Dittman, D

References Estler, W. T., Edmundson, K. L., Peggs, G. N., Parker, D. H. (2002). Large-scale metrology - an update. CIRP Annals-Manufacturing Technology , 51 (2), 587-608. Luo, R. C., Yih, C.-C., Su, K. L. (2002). Multisensor fusion and integration: Approaches, applications, and future research directions. IEEE Sensors Journal , 2 (2), 107-119. More, K., Ingman, D. (2008). Quality approach for multi-parametric data fusion. NDT&E International , 41 (3), 155-162. Hall, D. L., Llinas, J. (1997). An Introduction to multisensor data fusion. Proceedings of the IEEE

(5): 823-870. DOI : 10.1080/01431160600746456. Marinho E., Fasbender D., de Kok R., 2012. Spatial Assessment of Categorical Maps: A Proposed Framework. In: Proceedings of the 4th GEOBIA, 602-607. Rio de Janeiro, Brazil: São José dos Campos: INPE. Molenaar M., 2001. Hierarchical Object-Based Image Analysis of High-Resolution Imagery for Urban Land Use Classification. In: IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Area, 35-39. Rome, University of Rome LA Sapienza: IEEE . DOI : 10.1109/ DFUA.2001.985721. Nussbaum S., Niemeyer I., Canty M.J., 2006


The problem of inference about the joint distribution of two categorical variables based on knowledge or observations of their marginal distributions, to be referred to as categorical data fusion in this paper, is relevant in statistical matching, ecological inference, market research, and several other related fields. This article organizes the use of proxy variables, to be distinguished from other auxiliary variables, both in terms of their effects on the uncertainty of fusion and the techniques of fusion. A measure of the gains of efficiency is provided, which incorporates both the identification uncertainty associated with data fusion and the sampling uncertainty that arises when the theoretical bounds of the uncertainty space are unknown and need to be estimated. Several existing techniques for generating fusion distributions (or datasets) are described and some new ones proposed. Analysis of real-life data demonstrates empirically that proxy variables can make data fusion more precise and the constructed fusion distribution more plausible.

International Statistical Institute, August 16–22, 2009, Durban, South Africa. Fellegi, I.P. and A.B. Sunter. 1969. “A Theory for Record Linkage.” Journal of the American Statistical Association 64(328): 1183 – 1210. Doi: . Filippello, R., U. Guarnera and G. Jonas Lasinio. 2004. “Use of auxiliary information in statistical matching.” Proceedings of the XLII Conference of the Italian Statistical 9–11 June 2014, Bari, Italy: 37–40. Fosdick, B.K., M. DeYoreo and J.P. Reiter. 2016. “Categorical Data Fusion using Auxiliary

of the Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, pp. 4881–4884. Fortino, G., Galzarano, S., Gravina, R. and Li, W. (2015). A framework for collaborative computing and multi-sensor data fusion in body sensor networks, Information Fusion 22 : 50–70. Gheid, Z. and Challal, Y. (2016). Novel efficient and privacy-preserving protocols for sensor-based human activity recognition, 13th International Conference on Ubiquitous Intelligence and Computing (UIC 2016), Toulouse, France . Gravina, R., Ma, C., Pace, P., Aloi, G., Russo, W., Li, W. and