Daniela DE PALMA

Daniela DE PALMA

Ricercatore Universitario

Settore Scientifico Disciplinare ING-INF/04: AUTOMATICA.

Dipartimento di Ingegneria dell'Innovazione

Centro Ecotekne Pal. O - S.P. 6, Lecce - Monteroni - LECCE (LE)

Ufficio, Piano terra

Area di competenza:

Robotics, Automatic Control Systems.

Curriculum Vitae

Daniela De Palma is Assistant Professor, since 2020, at the Department of Innovation Engineering, University of Salento, Lecce, Italy, where she teaches the course of Estimation and Data Analysis with Applications. She received the Master Degree in Automation Engineering (summa cum laude) from the University of Salento, Lecce, Italy, in 2008. Before starting the academic research experience, she worked in software and process control industries. From 2014 to 2019 she was a research fellow at the Department of Innovation Engineering at University of Salento, where she received the Ph.D. degree in Information Engineering in April 2017. In October/November 2014 she has been at the Institute for Systems and Robotics, Instituto Superior Técnico (IST), University of Lisbon, Lisbon, Portugal. She collaborates with the Interuniversity Center of Integrated Systems for the Marine Environment (ISME) since 2016, and she is member of ISME since 2021. Her research interests include modeling, navigation, guidance, and control of autonomous robotic vehicles, observers and estimation theory, modelling and parameter identification. Specific research topics include modeling and identification of marine robots, single beacon observability (range-only navigation applications), multi-vehicle relative localization, outliers robust filtering. She has published over 20 papers in international journals and proceedings of international conferences on the subject. She has contributed to several national and international projects in the area of autonomous robotics and autonomous underwater vehicles including PRIN MARIS (2013-2016), EU H2020 WiMUST (2015-2018), EU H2020 DexROV (2015-2018), EU H2020 ROBUST (2015-2020) and EUMarineRobots (2018-ongoing). She is currently the local scientific responsible of the PNRM DAMPS “Distributed Autonomous Mobile Passive Sonar system” (2020-2021) on the use of a team of AUVs for the realization of a distributed passive sonar system.

Didattica

A.A. 2020/2021

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2020/2021

For matriculated on 2019/2020

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

A.A. 2019/2020

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

Year taught 2019/2020

For matriculated on 2018/2019

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

Torna all'elenco
ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/05

Course type Laurea Magistrale

Credits 6.0

Teaching hours Ore Attività frontale: 54.0

For matriculated on 2020/2021

Year taught 2021/2022

Course year 2

Semestre Secondo Semestre (dal 01/03/2022 al 10/06/2022)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS (ING-INF/05)
ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2019/2020

Year taught 2020/2021

Course year 2

Semestre Secondo Semestre (dal 01/03/2021 al 11/06/2021)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Sufficiency in calculus, probability theory, linear algebra.

This course offers a broad overview of fundamental and emerging topics in the area of estimation theory and data analysis; furthermore, a set of applications are illustrated in the fields of robotics, multi-agent and cyber-physical systems, and social systems. It is aimed at providing principles and tools to state and solve estimation problems in technological systems, and the solution is numerically sought with the aid of a suitable software (Mathworks Matlab).

Learning Outcomes. After the course the student should be able to:

(Knowledge and understanding)
Describe and explain the main peculiarities (both advantages and disadvantages) of each mathematical framework for the estimation problems considered in the course.

(Applying knowledge and understanding) + (Communication) + (Making judgements)
Be aware of, describe and explain practical problems of bad data gathering and robustness issues in the framework of estimation theory.

(Applying knowledge and understanding) + (Learning skills)
For a given practical problem at hand, be able to state an estimation problem in a natural mathematical setting, either stochastic or deterministic, based on the problem assumptions.

(Applying knowledge and understanding) + (Communication) + (Making judgements)
Build a simulation framework to find a computer-aided solution of the stated mathematical problem with the use of a suitable software.

Frontal lessons and lectures.

Oral exam and development of a project.
The objective of the exam is to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek a solution through an algorithm.

Introduction. Mathematical background and connections with other courses.
Set membership estimation: introduction, fundamental results and theorems. Set membership estimation: some applications.
Stochastic Estimators: definitions, properties, performances and fundamental limitations. Foundations of maximum likelihood estimation. The Bayesian approach to the estimation problem. Kalman filter: discrete-time stochastic state models, (two-steps) structure, computation of the optimal gain, the alternative geometric approach. Steady–state behavior. Extended Kalman Filter. Applications of Kalman Filter. Smoothing Algorithms. Robust estimation: introduction, fundamental definitions, estimator classes and performances.  
Applications of the previous issues and results to various fields.

  1. Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan “Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software”, 2001 John Wiley & Sons, Inc.

  2. D. Simon, “Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches”. John Wiley & Sons, 2006

  3. Anderson, Brian D.O., and John B. Moore. “Optimal Filtering”, 1979.

  4. L. Ljung, “System Identification: Theory for the User”. Prentice Hall PTR, Upper Saddle River, NJ, 1999.

  5. Rousseeuw PJ, Leroy AM. “Robust Regression and Outlier Detection”. John Wiley & Sons: Hoboken, NJ,

    USA, 2003.

  6. Huber PJ, Ronchetti EM. “Robust Statistics” - Second Edition. Wiley: New York, 2009.

  7. S. Bittanti, “Model Identification and Data Analysis”. John Wiley & Sons: Hoboken, NJ, USA, 2019.

  8. Milanese, M., Norton, J., Piet-Lahanier, H., Walter, É. (Eds.). (2013). “Bounding approaches to system

    identification”. Springer Science & Business Media.

  9. Zaki, Mohammed J., and Wagner Meira Jr. “Data mining and analysis: fundamental concepts and

    algorithms”. Cambridge University Press, 2014.

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS (ING-INF/04)
ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/04

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore Attività frontale: 81.0

For matriculated on 2018/2019

Year taught 2019/2020

Course year 2

Semestre Secondo Semestre (dal 02/03/2020 al 05/06/2020)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Sufficiency in calculus, probability theory, linear algebra.

This course offers a broad overview of fundamental and emerging topics in the area of estimation theory and data analysis; furthermore, a set of applications are illustrated in the fields of robotics, multi-agent and cyber-physical systems, and social systems. It is aimed at providing principles and tools to state and solve estimation problems in technological systems, and the solution is numerically sought with the aid of a suitable software (Mathworks Matlab).

Learning Outcomes. After the course the student should be able to:

(Knowledge and understanding)
Describe and explain the main peculiarities (both advantages and disadvantages) of each mathematical framework for the estimation problems considered in the course.

(Applying knowledge and understanding) + (Communication) + (Making judgements)
Be aware of, describe and explain practical problems of bad data gathering and robustness issues in the framework of estimation theory.

(Applying knowledge and understanding) + (Learning skills)
For a given practical problem at hand, be able to state an estimation problem in a natural mathematical setting, either stochastic or deterministic, based on the problem assumptions.

(Applying knowledge and understanding) + (Communication) + (Making judgements)
Build a simulation framework to find a computer-aided solution of the stated mathematical problem with the use of a suitable software.

Frontal lessons and lectures.

Oral exam and development of a project.
The objective of the exam is to determine to what extent the student has: 1) the ability to identify and use data to formulate responses to well-defined problems, 2) problem solving abilities to seek a solution through an algorithm.

Introduction. Mathematical background and connections with other courses.
Set membership estimation: introduction, fundamental results and theorems. Set membership estimation: some applications.
Stochastic Estimators: definitions, properties, performances and fundamental limitations. Foundations of maximum likelihood estimation. The Bayesian approach to the estimation problem. Kalman filter: discrete-time stochastic state models, (two-steps) structure, computation of the optimal gain, the alternative geometric approach. Steady–state behavior. Extended Kalman Filter. Applications of Kalman Filter. Smoothing Algorithms. Robust estimation: introduction, fundamental definitions, estimator classes and performances.  
Applications of the previous issues and results to various fields.

  1. Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan “Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software”, 2001 John Wiley & Sons, Inc.

  2. D. Simon, “Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches”, John Wiley & Sons, 2006

  3. Anderson, Brian D.O., and John B. Moore. “Optimal Filtering”, 1979.

  4. L. Ljung, “System Identification: Theory for the User”, Prentice Hall PTR, Upper Saddle River, NJ, 1999.

  5. Rousseeuw PJ, Leroy AM. “Robust Regression and Outlier Detection”. John Wiley & Sons: Hoboken, NJ,

    USA, 2003.

  6. Huber PJ, Ronchetti EM. “Robust Statistics” - Second Edition. Wiley: New York, 2009.

  7. Milanese, M., Norton, J., Piet-Lahanier, H., Walter, É. (Eds.). (2013). “Bounding approaches to system

    identification” Springer Science & Business Media.

  8. Zaki, Mohammed J., and Wagner Meira Jr. “Data mining and analysis: fundamental concepts and

    algorithms”, Cambridge University Press, 2014.

ESTIMATION AND DATA ANALYSIS WITH APPLICATIONS (ING-INF/04)

Pubblicazioni

International journal papers

j9 Daniela De Palma, Giovanni Indiveri, Gianfranco Parlangeli, Control Protocols for Range-Based Navigation of a Networked Group of Underwater Vehicles. Frontiers in Robotics and AI, 7, 2020, ISSN=2296-9144.

j8 Daniele De Vito, Daniela De Palma, Enrico Simetti, Giovanni Indiveri, and Gianluca Antonelli. Experimental validation of the modeling and control of a multibody underwater vehicle manipulator system for sea mining exploration. Journal of Field Robotics, August 2020.

j7 Roberta Ingrosso, Daniela De Palma, Giulio Avanzini, and Giovanni Indiveri. Dynamic modeling of underwater multi-hull vehicles. Robotica, 38(9):1682-1702, 2020.

j6 Gianluca Antonelli, Filippo Arrichiello, Andrea Caiti, Giuseppe Casalino, Daniela De Palma, Giovanni Indiveri, Matteo Razzanelli, Lorenzo Pollini, and Enrico Simetti. ISME activity on the use of autonomous surface and underwater vehicles for acoustic surveys at sea. ACTA IMEKO, 7(2):24 – 31, 2018. ISSN: 0237028X

j5 Daniela De Palma and Giovanni Indiveri. Output outlier robust state estimation. International Journal of Adaptive Control and Signal Processing, 31(4):581–607, 2017. ISSN: 08906327

j4 Daniela De Palma, Filippo Arrichiello, Gianfranco Parlangeli, and Giovanni Indiveri. Underwater localization using single beacon measurements: Observability analysis for a double integrator system. Ocean Engineering, 142:650–665, 2017. ISSN: 00298018

j3 G. Indiveri, D. De Palma, and G. Parlangeli. Single range localization in 3-D: Observability and robustness issues. IEEE Transactions on Control Systems Technology, 24(5):1853–1860, 2016. ISSN: 10636536

j2 P.A. Di Lillo, E. Simetti, D. De Palma, E. Cataldi, G. Indiveri, G. Antonelli, and G. Casalino. Advanced rov autonomy for efficient remote control in the dexrov project. Marine Technology Society Journal (Research Initiatives in Europe: Cooperation for Blue Growth), 50(4):67–80, July/August 2016. Guest Editors: Andrea Caiti, Giuseppe Casalino and Andrea Trucco, ISSN: 00253324

j1 Pedro Abreu, Gianluca Antonelli, Filippo Arrichiello, Andrea Caffaz, Andrea Caiti, Giuseppe Casalino, Nicola Catenacci Volpi, Ivan Bielic de Jong, Daniela De Palma, Henrique Duarte, Joao Pedro Gomes, Jonathan Grimsdale, Giovanni Indiveri, Sergio Jesus, Konstantin Kebkal, Elbert Kelholt, Antonio Pascoal, Daniel Polani, Lorenzo Pollini, Enrico Simetti, and Alessio Turetta. Widely scalable mobile underwater sonar technology: an overview of the H2020 WiMUST project. Marine Technology Society Journal (Research Initiatives in Europe: Cooperation for Blue Growth), 50(4):42–53, July/August 2016. Guest Editors: Andrea Caiti, Giuseppe Casalino and Andrea Trucco, ISSN: 00253324

 

Book’s chapter

b2 Lorenzo Pollini, Gianluca Antonelli, Filippo Arrichiello, Andrea Caiti, Giuseppe Casalino, Daniela De Palma, Giovanni Indiveri, Matteo Razzanelli and Enrico Simetti. Autonomous underwater vehicles: design and practice, chapter: AUV navigation, guidance, and control for geoseismic data acquisition, pages 650. IET, Editors: Frank Ehlers, 2020. ISBN-13: 978-1-78561-703-4

b1 Daniela De Palma, Giovanni Indiveri, and Gianfranco Parlangeli. Cooperative Localization and Navigation: Theory, Research, and Practice, chapter: Multi-vehicle cooperative range based navigation, pages 636. Taylor & Francis Ltd, August 26, 2019. ISBN: 9781138580619

 

International conference papers

c13 Daniela De Palma and Giovanni Indiveri. Outlier Robust State Estimation Through Smoothing on a Sliding Window. Proceedings of the 21st IFAC World Congress 2020 - IFAC 2020, Germany, July 13-17, 2020.

c12 Daniela De Palma and Giovanni Indiveri. Navigation filters for autonomous underwater vehicles during geotechnical surveying experiments. In IFAC-PapersOnLine, Proceedings of the 11th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles - CAMS 2018, volume 51, pages 171 – 176, Opatija, Croatia, September 2018. ISSN: 24058963

c11 Roberta Ingrosso, Daniela De Palma, Giovanni Indiveri, and Giulio Avanzini. Preliminaryresults of a dynamic modelling approach for underwater multi-hull vehicles. In IFAC-PapersOnLine, Proceedings of the 11th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles - CAMS 2018, volume 51, pages 86–91, Opatija, Croatia, September 2018. ISSN: 24058963

c10 D. De Palma, G. Indiveri, and A. M. Pascoal. Advances on a null-space-based approach to range-only underwater steering and positioning. In 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS 2018), pages 472–479, April 2018. ISBN: 978-153861647-5

c9 Daniela De Palma and Giovanni Indiveri. Underwater vehicle guidance control design within the dexrov project: preliminary results. In IFAC-PapersOnLine, Proceedings of the 10th IFAC Conference on Control Applications in Marine Systems, volume 49, pages 265–272, Trondheim, Norway, 13 - 16 September 2016. ISSN: 24058963

c8 Jeremi Gancet, Peter Weiss, Gianluca Antonelli, Max Folkert Pfingsthorn, Sylvain Calinon, Alessio Turetta, Cees Walen, Diego Urbina, Shashank Govindaraj, Pierre Letier, Xavier Martinez, Joseph Salini, Bertrand Chemisky, Giovanni Indiveri, Giuseppe Casalino, Paolo Di Lillo, Enrico Simetti, Daniela De Palma, Andreas Birk, Tobias Fromm, Christian Mueller, Ajay Tanwani, Ioannis Havoutis, Andrea Caffaz, and Lisa Guilpain. Dexterous undersea interventions with far distance onshore supervision: the dexrov project. In IFAC-PapersOnLine, Proceedings of the 10th IFAC Conference on Control Applications in Marine Systems CAMS 2016, volume 49, pages 414 – 419, Trondheim, Norway, 13 - 16 September 2016. ISSN: 24058963

c7 Giovanni Indiveri, Gianluca Antonelli, Filippo Arrichiello, Andrea Caffaz, Andrea Caiti, Giuseppe Casalino, Nicola Catenacci Volpi, Ivan Bielic de Jong, Daniela De Palma, Henrique Duarte, Joao Pedro Gomes, Jonathan Grimsdale, Sergio Jesus, Konstantin Kebkal, Elbert Kelholt, Antonio Pascoal, Daniel Polani, Lorenzo Pollini, Enrico Simetti, and Alessio Turetta. Overview and first year progress of the widely scalable mobile underwater sonar technology h2020 project. In IFAC-PapersOnLine, Proceedings of the 10th IFAC Conference on Control Applications in Marine Systems (CAMS 2016), volume 49, pages 430 – 433, Trondheim, Norway, 13 - 16 September 2016. ISSN: 24058963

c6 G. Antonelli, A. Caffaz, G. Casalino, N. C. Volpi, I. B. de Jong, D. De Palma, H. Duarte, J. Grimsdale, G. Indiveri, S. Jesus, K. Kebkal, A. Pascoal, D. Polani, and L. Pollini. The widely scalable mobile underwater sonar technology (wimust) h2020 project: First year status. In OCEANS 2016 - Shanghai, pages 1–8, April 2016. ISBN: 978-146739724-7

c5 Nicola Catenacci Volpi, Daniela De Palma, Daniel Polani, and Giovanni Indiveri. Computation of empowerment for an autonomous underwater vehicle. In IFAC-PapersOnLine, Proceedings of the 9th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2016), volume 49, pages 81 – 87, Leipzig, Germany, 29 June - 1 July 2016. ISSN: 24058963

c4 Daniela De Palma, Giovanni Indiveri, and António M. Pascoal. A null-space-based behavioral approach to single range underwater positioning. In IFAC-PapersOnLine, Proceedings of the 10th IFAC Conference on Manoeuvring and Control of Marine Craft MCMC 2015, volume 48, pages 55–60, Copenhagen, 24-26 August 2015. ISSN: 24058963

c3 Filippo Arrichiello, Daniela De Palma, Giovanni Indiveri, and Gianfranco Parlangeli. Observability analysis for single range localization. In Proceedings of MTS/IEEE Oceans ’15, pages 1–10, Genova, Italy, May 18-21 2015. ISBN: 978-147998736-8

c2 D. De Palma, G. Indiveri, and G. Parlangeli. Multi-vehicle relative localization based on single range measurements. In IFAC-PapersOnLine, Proceedings of the 3rd IFAC Workshop on MultiVehicle System - MVS 2015, volume 48, pages 17–22, Genova, Italy, 18 May 2015. ISSN: 24058963

c1 Giovanni Indiveri, Daniela De Palma, and Gianfranco Parlangeli. Single range localization in 3D: observability and robustness issues. In Workshop Proceedings of IAS-13, pages 339–346. 13th International Conference on Intelligent Autonomous Systems, July 15-19 2014. ISBN: 978-88-95872-06-3

Temi di ricerca

• Navigation, Guidance and Control (NGC) for autonomous vehicles
• Single Beacon Observability (range-only navigation applications)
• Multi-vehicle relative localization
• Observability-based guidance
• Outliers robust filtering
• Modelling and parameter identification