{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:33:51Z","timestamp":1761597231205,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2010,10,15]],"date-time":"2010-10-15T00:00:00Z","timestamp":1287100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF\/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS\/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF\/smoother algorithms for INS\/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks\u2013\u2013the Cascade Correlation Neural Network (CCNNs)\u2013\u2013to overcome the limitations of conventional techniques based on KF\/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes.<\/jats:p>","DOI":"10.3390\/s101009252","type":"journal-article","created":{"date-parts":[[2010,10,15]],"date-time":"2010-10-15T10:51:19Z","timestamp":1287139879000},"page":"9252-9285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS\/GPS Integration Algorithms"],"prefix":"10.3390","volume":"10","author":[{"given":"Kai-Wei","family":"Chiang","sequence":"first","affiliation":[{"name":"Department of Geomatics, National Cheng-Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7915-2477","authenticated-orcid":false,"given":"Hsiu-Wen","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Geomatics, National Cheng-Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2010,10,15]]},"reference":[{"key":"ref_1","unstructured":"Tao, V, and Li, J (2007). Advances in Mobile Mapping Technology, Taylor and Francis Group. International Society for Photogrammetry and Remote Sensing (ISPRS) Book Series."},{"key":"ref_2","unstructured":"Goad, CC (1991, January 10\u201312). The Ohio State University Mapping System: The positioning component. Williamsburg, VA, USA."},{"key":"ref_3","unstructured":"Coetsee, J, Brown, A, and Bossler, J (,  1994). GIS data collection using the GPS Van supported by a GPS\/inertial mapping system. Salt Lake City, UT, USA. Part 1."},{"key":"ref_4","first-page":"1.3.1","article-title":"A mobile mapping system based on GPS, GIS and multisensor","volume":"32","author":"Li","year":"1999","journal-title":"Int Arch Photogramm Remote Sens"},{"key":"ref_5","first-page":"5.A.5.1","article-title":"Surveying and mapping of urban streets by photogrammetric traverse","volume":"32","author":"Silva","year":"1999","journal-title":"Int Arch Photogramm Remote Sens"},{"key":"ref_6","first-page":"1085","article-title":"Mobile Mapping\u2014An emerging technology for spatial data acquisition","volume":"63","author":"Li","year":"1997","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_7","unstructured":"Grejner-Brzezinska, D, and Toth, C (2000, January 16\u201323). Precision mapping of highway linear features. Amsterdam, The Netherlands."},{"key":"ref_8","unstructured":"Cramer, M, Stallmann, D, and Halla, N (September, January 30). High precision georeferencing using GPS\/INS and image matching. Banff, Canada."},{"key":"ref_9","unstructured":"Hock, C, Caspary, W, Heister, H, Klemm, J, and Sternberg, H (, January April). Architecture and design of the kinematic survey system KiSS. Stuttgart, Germany."},{"key":"ref_10","unstructured":"El-Sheimy, N (1996). The development of VISAT\u2014A mobile survey system for GIS applications, Department of Geomatics Engineering, the University of Calgary. Ph.D. thesis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1002\/j.2161-4296.1995.tb02334.x","article-title":"Inertial navigation technology from 1970\u20131995","volume":"42","author":"Greenspan","year":"1995","journal-title":"Navig. J. Inst. Navig"},{"key":"ref_12","unstructured":"Titterton, DH, and Weston, JL (1997). Strapdown Inertial Navigation Technology, Peter Peregrinus Ltd."},{"key":"ref_13","unstructured":"Brown, RG, and Hwang, PYC (1992). Introduction to Random Signals, John Wiley & Sons."},{"key":"ref_14","unstructured":"Gelb, A (1974). Applied Optimal Estimation, The MIT Press."},{"key":"ref_15","first-page":"292","article-title":"Does a navigation algorithm have to use Kalman filter?","volume":"45","author":"Vanicek","year":"1999","journal-title":"Can. Aeronaut. Space J"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2886","DOI":"10.3390\/s8042886","article-title":"An integrated MEMS gyroscope array with higher accuracy output","volume":"8","author":"Chang","year":"2008","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/JMEMS.2006.876779","article-title":"Error sources in in-plane silicon tuning-fork MEMS gyroscopes","volume":"15","author":"Weinberg","year":"2006","journal-title":"J. Microelectromech. Syst"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1049\/ip-f-2.1993.0015","article-title":"Novel approach to nonlinear\/non-Gaussian Bayesian state estimation","volume":"140","author":"Gordon","year":"1993","journal-title":"IEE Proceedings-F Radar Signal Pro"},{"key":"ref_19","unstructured":"Maybeck, PS (1994). Stochastic Models, Estimation, and Control, Academic Press."},{"key":"ref_20","unstructured":"Sukkarieh, S (2000). Australian Centre for Field Robotics, Dept. of Mechanical and Mechatronic Engineering, The University of Sydney. Ph.D. Thesis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2240","DOI":"10.3390\/s8042240","article-title":"Error and performance analysis of MEMS-based inertial sensors with a low cost GPS receiver","volume":"8","author":"Park","year":"2008","journal-title":"Sensors"},{"key":"ref_22","unstructured":"Shin, EH (2004, January 21\u201324). A quaternion-based unscented kalman filter for the integration of GPS and MEMS INS. Long Beach, CA, USA."},{"key":"ref_23","unstructured":"Bergman, N (1999). Recursive Bayesian Estimation Navigation and Tracking Applications, Link\u00f6ping University. Ph.D. Thesis."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Aggarwal, P (2008, January 16\u201319). Hybrid Extended Particle Filter (HEPF) for INS\/GPS integrated system. Savannah, Georgia.","DOI":"10.1109\/PLANS.2008.4570072"},{"key":"ref_25","unstructured":"Kubo, Y, and Wang, J (2008, January 16\u201319). INS\/GPS integration using gaussian sum particle filter. Savannah, GA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s10291-002-0024-4","article-title":"Multi-sensors integration using neuron computing for land vehicle navigation","volume":"6","author":"Chiang","year":"2003","journal-title":"GPS Solutions"},{"key":"ref_27","unstructured":"Wang, J, Ding, W, and Wang, J (2007, January 25\u201328). Improving Adaptive Kalman Filter in GPS\/SDINS integration with neural network. Fort Worth, TX, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1109\/TNN.2006.890811","article-title":"Sensor integration for satellite-based vehicular navigation using neural networks","volume":"18","author":"Sharaf","year":"2007","journal-title":"IEEE Trans. Neural Netw"},{"key":"ref_29","unstructured":"El-Sheimy, N, Abdel-Hamid, W, and Lachapelle, G (2004, January 21\u201324). An adaptive neuro-fuzzy model for bridging GPS outages in MEMS-IMU\/GPS land vehicle navigation. Long Beach, CA, USA."},{"key":"ref_30","unstructured":"Goodall, C, El-Sheimy, N, and Chiang, KW (2005, January 13\u201316). The development of a GPS\/MEMS INS integrated system utilizing a hybrid processing architecture. Long Beach, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2586","DOI":"10.3390\/s90402586","article-title":"An artificial neural network embedded position and orientation determination algorithm for low cost MEMS INS\/GPS integrated sensors","volume":"9","author":"Chiang","year":"2009","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Doucet, A, De Freitas, N, and Gordon, N (2001). Sequential Monte Carlo Methods in Practice, Springer.","DOI":"10.1007\/978-1-4757-3437-9"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0004-3702(01)00069-8","article-title":"Robust monte carlo localization for mobile robots","volume":"128","author":"Thrun","year":"2001","journal-title":"Arti. Intell"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/78.978396","article-title":"Particle filters for positioning, navigation and tracking","volume":"50","author":"Gustafsson","year":"2002","journal-title":"IEEE Trans. Signal Process"},{"key":"ref_35","unstructured":"van der Merwe, R, Doucet, A, De Freitas, N, and Wan, E (2000). The unscented particle filter. Adv Neural Inf Process Syst (NIPS13), 584\u2013590."},{"key":"ref_36","unstructured":"Aggarwal, P, Syed, Z, and El-Sheimy, N Hybrid Extended Particle Filter (HEPF) for Integrated Civilian Navigation System. Monterey, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1117\/12.280797","article-title":"New extension of the Kalman filter to nonlinear system","volume":"3068","author":"Julier","year":"1997","journal-title":"Proc. SPIE"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1088\/0957-0233\/15\/10\/015","article-title":"A new weight updating method for INS\/GPS integration architectures based on neural networks","volume":"15","author":"Chiang","year":"2003","journal-title":"Meas. Sci. Technol"},{"key":"ref_39","unstructured":"Haykin, S (1999). Neural Networks: A Comprehensive Foundation, Prentice-Hall. [2nd ed]."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bishop, CM (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_41","unstructured":"Alpaydin, E (1991). GAL, Neural Networks that Grow When they Learn and Shrink When They Forget, International Computer Science Institute."},{"key":"ref_42","unstructured":"Touretzky, DS (1991). Advances in Neural Information Processing System 2, Morgan Kaufmann Publishers."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TAES.2008.4560208","article-title":"Constructive neural networks based MEMS\/GPS integration scheme","volume":"44","author":"Chiang","year":"2008","journal-title":"IEEE Trans. Aerosp. Electron. Syst"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/10\/10\/9252\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:03:35Z","timestamp":1760220215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/10\/10\/9252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,10,15]]},"references-count":43,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2010,10]]}},"alternative-id":["s101009252"],"URL":"https:\/\/doi.org\/10.3390\/s101009252","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2010,10,15]]}}}