{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:53:10Z","timestamp":1740149590180,"version":"3.37.3"},"reference-count":54,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models\u2019 performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters.<\/jats:p>","DOI":"10.3390\/s23136127","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T05:42:47Z","timestamp":1688449367000},"page":"6127","source":"Crossref","is-referenced-by-count":2,"title":["The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4460-6640","authenticated-orcid":false,"given":"Giorgio","family":"de Alteriis","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9268-9420","authenticated-orcid":false,"given":"Davide","family":"Ruggiero","sequence":"additional","affiliation":[{"name":"STMicroelectronics, Analog, MEMS and Sensor Group R&D, 80022 Arzano, Italy"}]},{"given":"Francesco","family":"Del Prete","sequence":"additional","affiliation":[{"name":"STMicroelectronics, Analog, MEMS and Sensor Group R&D, 80022 Arzano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9441-2927","authenticated-orcid":false,"given":"Claudia","family":"Conte","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}]},{"given":"Enzo","family":"Caputo","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}]},{"given":"Verdiana","family":"Bottino","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}]},{"given":"Filippo","family":"Carone Fabiani","sequence":"additional","affiliation":[{"name":"Department of Economics, Management and Statistics, University Milano-Bicocca, 20126 Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8843-0109","authenticated-orcid":false,"given":"Domenico","family":"Accardo","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4875-2845","authenticated-orcid":false,"given":"Rosario","family":"Schiano Lo Moriello","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1080\/00423114.2019.1610182","article-title":"Trends in Vehicle Motion Control for Automated Driving on Public Roads","volume":"57","author":"Klomp","year":"2019","journal-title":"Veh. Syst. Dyn."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"376","DOI":"10.4018\/IJHISI.2018070102","article-title":"Models for Drone Delivery of Medications and Other Healthcare Items","volume":"13","author":"Scott","year":"2018","journal-title":"Int. J. Healthc. Inf. Syst. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"342","DOI":"10.4103\/jfmpc.jfmpc_413_18","article-title":"Unmanned Aerial Vehicle (Drones) in Public Health: A SWOT Analysis","volume":"8","author":"Laksham","year":"2019","journal-title":"J. Fam. Med. Prim. Care"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100016","DOI":"10.1016\/j.array.2020.100016","article-title":"Distance Measurement System for Autonomous Vehicles Using Stereo Camera","volume":"5","author":"Zaarane","year":"2020","journal-title":"Array"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10111-018-0484-0","article-title":"How Can Humans Understand Their Automated Cars? HMI Principles, Problems and Solutions","volume":"21","author":"Carsten","year":"2019","journal-title":"Cogn. Technol. Work."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Silvestri, A.T., Papa, I., and Squillace, A. (2023). Influence of Fibre Fill Pattern and Stacking Sequence on Open-Hole Tensile Behaviour in Additive Manufactured Fibre-Reinforced Composites. Materials, 16.","DOI":"10.3390\/ma16062411"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Silvestri, A.T., Amirabdollahian, S., Perini, M., Bosetti, P., and Squillace, A. (2021, January 14). Direct Laser Deposition for Tailored Structure. Proceedings of the ESAFORM 2021, Virtual.","DOI":"10.25518\/esaform21.4124"},{"key":"ref_8","first-page":"14","article-title":"The application of mems technology to determine an aircraft orientation","volume":"1","author":"Isgandarov","year":"2021","journal-title":"Bull. Civ. Aviat. Acad."},{"key":"ref_9","first-page":"1","article-title":"INS\/GPS Technology Trends","volume":"116","author":"Schmidt","year":"2011","journal-title":"Technology"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Benser, E.T. (2015, January 23\u201326). Trends in Inertial Sensors and Applications. Proceedings of the 2nd IEEE International Symposium on Inertial Sensors and Systems, IEEE ISISS 2015\u2014Proceedings, Hapuna Beach, HI, USA.","DOI":"10.1109\/ISISS.2015.7102358"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"De Alteriis, G., Silvestri, A.T., Conte, C., Bottino, V., Caputo, E., Squillace, A., Accardo, D., and Schiano Lo Moriello, R. (2023). Innovative Fusion Strategy for MEMS Redundant-IMU Exploiting Custom 3D Components. Sensors, 23.","DOI":"10.3390\/s23052508"},{"key":"ref_12","first-page":"1","article-title":"Fundamental Aspects in Sensor Network Metrology","volume":"12","author":"Vedurmudi","year":"2023","journal-title":"Acta IMEKO"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Han, S., Meng, Z., Omisore, O., Akinyemi, T., and Yan, Y. (2020). Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement\u2014A Review. Micromachines, 11.","DOI":"10.3390\/mi11111021"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.21014\/actaimeko.v12i2.1532","article-title":"Lo On the Suitability of Redundant Accelerometers for the Implementation of Smart Oscillation Monitoring System: Preliminary Assessment","volume":"12","author":"Caputo","year":"2023","journal-title":"Acta IMEKO"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10426914.2023.2219302","article-title":"Laser-Directed Energy Deposition of H13: Processing Window and Improved Characterization Procedures","volume":"2023","author":"Silvestri","year":"2023","journal-title":"Mater. Manuf. Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Iadarola, G., Disha, D., De Santis, A., Spinsante, S., and Gambi, E. (2022, January 27\u201329). Global Positioning System Measurements: Comparison of IoT Wearable Devices. Proceedings of the 2022 IEEE 9th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Pisa, Italy.","DOI":"10.1109\/MetroAeroSpace54187.2022.9855994"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Navidi, N., and Landry, R. (2021). A New Perspective on Low-Cost Mems-Based AHRS Determination. Sensors, 21.","DOI":"10.3390\/s21041383"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1109\/JSEN.2019.2941273","article-title":"MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman Filtering","volume":"20","author":"Narasimhappa","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102","DOI":"10.21014\/acta_imeko.v7i2.533","article-title":"A Vision-Based Navigation System for Landing Procedure","volume":"7","author":"Papa","year":"2018","journal-title":"Acta IMEKO"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"21675","DOI":"10.1109\/JSEN.2021.3059050","article-title":"Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic","volume":"21","author":"Liu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Groves, P.D. (2015). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, IEEE Aerospace and Electronic Systems Magazine. [2nd ed.].","DOI":"10.1109\/MAES.2014.14110"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6818","DOI":"10.1109\/JSEN.2022.3150073","article-title":"Improved Vehicle Localization Using On-Board Sensors and Vehicle Lateral Velocity","volume":"22","author":"Gao","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_23","unstructured":"Ristic, B., Arulampalam, S., and Gordon, N. (2003). Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TCST.2022.3174511","article-title":"Autonomous Vehicle Kinematics and Dynamics Synthesis for Sideslip Angle Estimation Based on Consensus Kalman Filter","volume":"31","author":"Xia","year":"2022","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.1109\/JSEN.2017.2787578","article-title":"Adaptive EKF Based on HMM Recognizer for Attitude Estimation Using MEMS MARG Sensors","volume":"18","author":"Tong","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fan, Q., Zhang, H., Sun, Y., Zhu, Y., Zhuang, X., Jia, J., and Zhang, P. (2018). An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model. Sensors, 18.","DOI":"10.3390\/s18051404"},{"key":"ref_27","unstructured":"De Alteriis, G., Conte, C., Accardo, D., Rufino, G., Schiano Lo Moriello, R., and Alvarez, O.H. (2022, January 3\u20137). Advanced Technique to Support ADS System Failure Exploiting MEMS Inertial Sensors. Proceedings of the AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022, San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104120","DOI":"10.1016\/j.trc.2023.104120","article-title":"An Automated Driving Systems Data Acquisition and Analytics Platform","volume":"151","author":"Xia","year":"2023","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9995","DOI":"10.1109\/TITS.2021.3097385","article-title":"Inertial Sensing Meets Machine Learning: Opportunity or Challenge?","volume":"23","author":"Li","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"De Alteriis, G., Accardo, D., Conte, C., and Schiano Lo Moriello, R. (2021). Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy. Sensors, 21.","DOI":"10.3390\/s21144851"},{"key":"ref_31","unstructured":"(2006). IEEE Standard Specification Format Guide and Test Procedure for Single-Axis Laser Gyros. Standard No. IEEE Std 647-2006."},{"key":"ref_32","first-page":"87","article-title":"Prediction of Gyro Motor\u2019s State Based on Grey Theory and BP Neural Network","volume":"3","author":"Zha","year":"2010","journal-title":"Zhongguo Guanxing Jishu Xuebao\/J. Chin. Inert. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1016\/j.ymssp.2015.11.004","article-title":"Temperature Drift Modeling of MEMS Gyroscope Based on Genetic-Elman Neural Network","volume":"72\u201373","author":"Chong","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Grekov, A.N., and Kabanov, A.A. (2022, January 4\u201310). Machine Learning Boosting Algorithms for Determining Euler Angles in an Inertial Navigation System. Proceedings of the 2022 International Russian Automation Conference, Sochi, Russia.","DOI":"10.1109\/RusAutoCon54946.2022.9896248"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Eskofier, B.M., Lee, S.I., Daneault, J.F., Golabchi, F.N., Ferreira-Carvalho, G., Vergara-Diaz, G., Sapienza, S., Costante, G., Klucken, J., and Kautz, T. (2016, January 16\u201320). Recent Machine Learning Advancements in Sensor-Based Mobility Analysis: Deep Learning for Parkinson\u2019s Disease Assessment. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590787"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.robot.2018.03.013","article-title":"Slippage Prediction for Off-Road Mobile Robots via Machine Learning Regression and Proprioceptive Sensing","volume":"105","author":"Gonzalez","year":"2018","journal-title":"Rob. Auton. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6503","DOI":"10.1109\/TITS.2020.2993052","article-title":"Estimate the Pitch and Heading Mounting Angles of the IMU for Land Vehicular GNSS\/INS Integrated System","volume":"22","author":"Chen","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1109\/JSEN.2014.2298896","article-title":"Enhanced, Delay Dependent, Intelligent Fusion for Ins\/Gps Navigation System","volume":"14","author":"Jaradat","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rambach, J.R., Tewari, A., Pagani, A., and Stricker, D. (2016, January 19\u201323). Learning to Fuse: A Deep Learning Approach to Visual-Inertial Camera Pose Estimation. Proceedings of the 2016 IEEE International Symposium on Mixed and Augmented Reality, ISMAR, Merida, Mexico.","DOI":"10.1109\/ISMAR.2016.19"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1017\/S0373463314000307","article-title":"GPS\/INS\/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications","volume":"67","author":"Li","year":"2014","journal-title":"J. Navig."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jwo, D.J., Chuang, C.H., Yang, J.Y., and Lu, Y.H. (2012, January 5\u20138). Neural Network Assisted Ultra-Tightly Coupled GPS\/INS Integration for Seamless Navigation. Proceedings of the 2012 12th International Conference on ITS Telecommunications, ITST, Taipei, Taiwan.","DOI":"10.1109\/ITST.2012.6425204"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, Apress.","DOI":"10.1007\/978-1-4842-4470-8"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Everitt, B., and Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-9650-3"},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_45","unstructured":"Verma, Y. (Analytics India Magazine, 2021). A Complete Understanding of Dense Layers in Neural Networks, Analytics India Magazine."},{"key":"ref_46","first-page":"18866","article-title":"Role of Layers and Neurons in Deep Learning With the Rectified Linear Unit","volume":"13","author":"Takekawa","year":"2021","journal-title":"Cureus"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"947","DOI":"10.2514\/8.5282","article-title":"Gradient Theory of Optimal Flight Paths","volume":"30","author":"Kelley","year":"1960","journal-title":"ARS J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/TSMC.2020.3042785","article-title":"Artificial Vision by Deep CNN Neocognitron","volume":"51","author":"Fukushima","year":"2021","journal-title":"IEEE Trans. Syst. Man. Cybern. Syst."},{"key":"ref_50","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, ICML, Lille, France."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short Term Memory. Neural Computation","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_52","unstructured":"Macedo, I. (2023, January 10). Implementing the Particle Swarm Optimization (PSO) Algorithm in Python. Available online: https:\/\/medium.com\/analytics-vidhya\/implementing-particleswarm-optimization-pso-algorithm-in-python-9efc2eb179a6."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","article-title":"A Review on Genetic Algorithm: Past, Present, and Future","volume":"80","author":"Katoch","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8853","DOI":"10.1109\/TIM.2020.2998909","article-title":"A PSO-MMA Method for the Parameters Estimation of Interarea Oscillations in Electrical Grids","volume":"69","author":"Bonavolonta","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T05:27:14Z","timestamp":1688534834000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,3]]},"references-count":54,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136127"],"URL":"https:\/\/doi.org\/10.3390\/s23136127","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,7,3]]}}}