dr. R.T. Rajan

Projects history

1. Designing C2 Links for BVLOS UAS Operations
   Wei Cong, Barry Tee; Rajan, Raj Thilak; Larsen, Morten;
   Drones,
   Volume 10, Issue 6, 2026. DOI: 10.3390/drones10060397
   
   Abstract: ...

   Unmanned Aircraft Systems (UAS) have seen a significant growth in civilian space over the past decade. The number one ranked challenge in UAS operations in Europe is regulatory obstacles such as the Specific Operations Risk Assessment (SORA) for 2023–2025. Existing approaches have focused on individual technical solutions (radio technologies, redundancy schemes, or cryptographic protections) or on high-level safety analysis, but have not integrated regulatory compliance, risk assessment, and repeatable systems models that directly support SORA artifact generation and rapid adaptation across BVLOS operational contexts. Thus, the current state-of-the-art apparatus lacks a systematic Model-Based Systems Engineering (MBSE) approach that can cater to Command and Control (C2) data-link design for Beyond Visual Line-of-Sight (BVLOS) missions. In this work, we propose an MBSE methodology designed to assist engineers in designing a C2 data link for BVLOS drone operations that complies with SORA regulations in the Netherlands and Europe. To validate the use of MBSE in a wide range of complex drone operations, we demonstrate how subtle modifications in the proposed engineering models can be made without any major overhaul of new SORA applications, and this is validate these changes through laboratory software tests and simulations.
   
   document

2. Cooperative Gaussian process-based model predictive control for safe multi-agent navigation
   Ellen H. J. Riemens; Alle-Jan van der Veen ; Raj T. Rajan;
   Journal on Advances in Signal Processing,
   2026. DOI: 10.1186/s13634-026-01306-2
   document

3. Domain-aware Gaussian process state-space models
   Anurodh Mishra; Raj Thilak Rajan;
   Signal Process.,
   Volume 238, pp. 110003, 2026. DOI: 10.1016/J.SIGPRO.2025.110003
   document

4. Geometry-Aware Edge-State Tracking for Robust Affine Formation Control
   Zhonggang Li; Raj Thilak Rajan;
   IEEE Open Journal of Control Systems,
   Volume 5, pp. 107-120, 2026. DOI: 10.1109/OJCSYS.2026.3657987

5. Multiple Model Recursive Gaussian Process for Robust Target Tracking
   Ali Emre Balci; Raj Thilak Rajan;
   IEEE Open Journal of Signal Processing,
   Volume 7, pp. 23-31, 2026. DOI: 10.1109/OJSP.2025.3646127

6. Reliable Maintenance Policy for Distributed Affine Formation Control of UAVs
   Zhou, Hongyu; Li, Zhonggang; Rajan, Raj Thilak;
   In 2026 IEEE Aerospace Conference,
   pp. 1-9, 2026. DOI: 10.1109/AERO66936.2026.11519911
   
   Keywords: ...

   Maintenance;Formation control;Timing;Autonomous aerial vehicles;Printing;Equations;Matrices;Technology;Tagging;Geometry.

7. Gaussian Processes for Sensor Repositioning in PDE-Driven Systems
   Maan Pandya; Bianca Giovanardi; Raj Thilak Rajan;
   In 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
   pp. 20122-20126, 2026. DOI: 10.1109/ICASSP55912.2026.11464851

8. Estimation of Relative Kinematic Parameters of an Anchorless Network
   A. Mishra; R.T. Rajan;
   IEEE Tr. Signal and Information Processing over Networks,
   Volume 11, pp. 831-844, 2025. DOI: 10.1109/TSIPN.2025.3557585
   
   Keywords: ...

   Mobile nodes;Kinematics;Trajectory;Data models;Polynomials;Estimation;Location awareness;Accelerometers;Symmetric matrices;Vectors;Lyapunov-like equation;mobile nodes;multidimensional scaling;localization;time-varying distances.
   
   document

9. Autonomous navigation of quadrupeds using coverage path planning with morphological skeleton maps
   Alexander James Becoy; Kseniia Khomenko; Luka Peternel; Raj Thilak Rajan;
   Frontiers Robotics {AI},
   Volume 12, 2025. DOI: 10.3389/FROBT.2025.1601862
   document

10. Fast Multiagent Formation Stabilization with Sparse Universally Rigid Frameworks
    Zhonggang Li; Geert Leus; Raj Thilak Rajan;
    In 2025 33rd European Signal Processing Conference (EUSIPCO),
    pp. 2392-2396, 2025.
    document

11. Physics-Informed Intelligent Motor Fault Detection
    Sinian Li; Raj Thilak Rajan; Edmund Marth; Patrick Zorn; Wolfgang Gruber; Justin Dauwels;
    In 33rd European Signal Processing Conference (EUSIPCO),
    IEEE, pp. 1158--1162, 2025.
    document

12. Distributed ADMM for Target Localization using Radar Networks
    Srikar Chaganti; Francesco Fioranelli; Raj Thilak Rajan;
    In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),
    IEEE, pp. 1--5, 2025. DOI: 10.1109/ICASSPW65056.2025.11011175
    document

13. Distributed Navigation with Dynamic Obstacles
    Ellen H. J. Riemens; Raj Thilak Rajan;
    In 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    IEEE, pp. 1--5, 2025. DOI: 10.1109/ICASSP49660.2025.10888973
    document

14. Uncertainty-Aware Gate-Lifetime Prediction of p-GaN Gate HEMTs Using Gaussian Processes
    Shuoyan Zhao; Raj Thilak Rajan; Andrea Tallarico; Maurizio Millesimo; Vladislav Volosov; Antonio Imbruglia; Justin Dauwels;
    In 2025 9th International Conference on System Reliability and Safety (ICSRS),
    pp. 152-156, 2025. DOI: 10.1109/ICSRS68021.2025.11422186

15. Recent Advances in Autonomous Systems for Inspection and Predictive Maintenance of Infrastructures: An Overview of the Special Session
    Pamela Zontone; Raj Thilak Rajan; Shunqiao Sun; Lucio Marcenaro;
    In 2025 33rd European Signal Processing Conference (EUSIPCO),
    pp. 1139-1142, 2025. DOI: 10.23919/EUSIPCO63237.2025.11226162

16. Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS
    Politi, Elena; Purucker, Patrick; Larsen, Morten; Reis, Ricardo J. Dos; Rajan, Raj Thilak; Penna, Sergio Duarte; Boer, Jan-Floris; Rodosthenous, Panagiotis; Dimitrakopoulos, George; Varlamis, Iraklis; Höß, Alfred;
    Electronics,
    Volume 13, Issue 2, 2024. DOI: 10.3390/electronics13020340
    
    Abstract: ...

    Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed.
    
    document

17. Joint Ranging and Phase Offset Estimation for Multiple Drones Using ADS-B Signatures
    Mostafa Mohammadkarimi; Geert Leus; Raj Thilak Rajan;
    IEEE Trans. Veh. Technol.,
    Volume 73, Issue 2, pp. 1667-1681, 2024. DOI: 10.1109/TVT.2023.3318192
    document

18. On the Stability of Consensus Control Under Rotational Ambiguities
    Zhonggang Li; Changheng Li; Raj Thilak Rajan;
    {IEEE} Control. Syst. Lett.,
    Volume 8, pp. 3273--3278, 2024. DOI: 10.1109/LCSYS.2024.3521358
    document

19. Cooperative Sense and Avoid for UAVs Using Secondary Radar
    Mostafa Mohammadkarimi; Raj Thilak Rajan;
    IEEE Transactions on Aerospace and Electronic Systems,
    Volume 60, Issue 5, pp. 7041-7055, 2024. DOI: 10.1109/TAES.2024.3410953

20. Joint Ranging and Phase Offset Estimation of Multiple Aviation Vehicles Using Secondary Radar
    Mohammadkarimi, Mostafa; Leus, Geert; Rajan, Raj Thilak;
    In 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    pp. 9131-9135, 2024. DOI: 10.1109/ICASSP48485.2024.10446219

21. Malleable Kernel Interpolation for Scalable Structured Gaussian Process
    Hanyuan Ban; Ellen H. J. Riemens; Raj Thilak Rajan;
    In 32nd European Signal Processing Conference (EUSIPCO),
    IEEE, pp. 997--1001, 2024.
    document

22. GPS-VIO Fusion with Online Rotational Calibration
    Junlin Song; Pedro J. Sanchez{-}Cuevas; Antoine Richard; Raj Thilak Rajan; Miguel A. Olivares{-}M{\'{e}}ndez;
    In IEEE International Conference on Robotics and Automation (ICRA),
    IEEE, pp. 11906--11912, 2024. DOI: 10.1109/ICRA57147.2024.10611466
    document

23. RAPF: Efficient path planning for lunar microrovers
    Thomas Manteaux; David Rodríguez-Martínez; Raj Thilak Rajan;
    In 2024 International Conference on Space Robotics (iSpaRo),
    pp. 56-63, 2024. DOI: 10.1109/iSpaRo60631.2024.10688117

24. Artificial Potential Field-Based Path Planning for Cluttered Environments
    Mosab Diab; Mostafa Mohammadkarimi; Raj Thilak Rajan;
    In 2023 IEEE Aerospace Conference,
    pp. 1-8, 2023. DOI: 10.1109/AERO55745.2023.10115857

25. Distributed Particle Filter Based on Particle Exchanges
    Rui Tang; Ellen Riemens; Raj Thilak Rajan;
    In 2023 IEEE Aerospace Conference,
    pp. 1-8, 2023. DOI: 10.1109/AERO55745.2023.10115781

26. On the Choice of Reference in Offset Calibration
    Raj Thilak Rajan;
    In 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP),
    IEEE, pp. 291--295, 2023. DOI: 10.1109/CAMSAP58249.2023.10403465
    document

27. Geometry-Aware Distributed Kalman Filtering for Affine Formation Control under Observation Losses
    Zhonggang Li; Raj Thilak Rajan;
    In 26th International Conference on Information Fusion (FUSION),
    IEEE, pp. 1--7, 2023. DOI: 10.23919/FUSION52260.2023.10224101
    document

28. Distributed Gaussian Process Hyperparameter Optimization for Multi-Agent Systems
    Peiyuan Zhai; Raj Thilak Rajan;
    In 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    IEEE, pp. 1--5, 2023. DOI: 10.1109/ICASSP49357.2023.10096267
    document

29. Decentralized coordination for truck platooning
    Zeng, Yikai; Wang, Meng; Rajan, Raj Thilak;
    Computer-Aided Civil and Infrastructure Engineering,
    2022. DOI: https://doi.org/10.1111/mice.12899
    document

30. The science case and challenges of space-borne sub-millimeter interferometry
    Leonid I. Gurvits; Zsolt Paragi; Ricardo I. Amils; Ilse {van Bemmel}; Paul Boven; Raj Thilak Rajan; others;
    Acta Astronautica,
    Volume 196, Issue 314-333, 2022. DOI: https://doi.org/10.1016/j.actaastro.2022.04.020
    document

31. Frequency augumented clock synchronizaion for space-based interferometry
    Felix Abel; Prem Sundaramoorthy; Raj Thilak Rajan;
    In Small Satellite Systems and Services : 4S Symposium,
    ESA/ESTEC, pp. 14, May 2022.
    document

32. Distributed Kalman Filters for Relative Formation Control of Multi-Agent Systems
    Martijn van der Marel; Raj Thilak Rajan;
    In 30th European Signal Processing Conference (EUSIPCO),
    IEEE, pp. 1422--1426, 2022.
    document

33. Relative Kinematics Estimation Using Accelerometer Measurements
    Anurodh Mishra; Raj Thilak Rajan;
    In 30th European Signal Processing Conference (EUSIPCO),
    IEEE, pp. 1856--1860, 2022.
    document

34. Applications and Potentials of Intelligent Swarms for magnetospheric studies
    Raj Thilak Rajan; Shoshana Ben-Maor; Shaziana Kaderali; Calum Turner; Mohammed Milhim; Catrina Melograna; Dawn Haken; Gary Paul; Vedant; V. Sreekumar; Johannes Weppler; Yosephine Gumulya; Riccardo Bunt; Asia Bulgarini; Maurice Marnat; Kadri Bussov; Frederick Pringle; Jusha Ma; Rushanka Amrutkar; Miguel Coto; Jiang He; Zijian Shi; Shahd Hayder; Dina Saad Fayez Jaber; Junchao Zuo; Mohammad Alsukour; Cécile Renaud; Matthew Chris;
    Acta Astronautica,
    2021. DOI: https://doi.org/10.1016/j.actaastro.2021.07.046
    
    Keywords: ...

    Satellite swarms, Intelligent swarms, Heliophysics, Magnetosphere, Cubesats, Next generation space systems.
    
    
    Abstract: ...

    Earth’s magnetosphere is vital for today’s technologically dependent society. To date, numerous design studies have been conducted and over a dozen science missions have flown to study the magnetosphere. However, a majority of these solutions relied on large monolithic satellites, which limited the spatial resolution of these investigations, as did the technological limitations of the past. To counter these limitations, we propose the use of a satellite swarm carrying numerous and distributed payloads for magnetospheric measurements. Our mission is named APIS — Applications and Potentials of Intelligent Swarms. The APIS mission aims to characterize fundamental plasma processes in the Earth’s magnetosphere and measure the effect of the solar wind on our magnetosphere. We propose a swarm of 40 CubeSats in two highly-elliptical orbits around the Earth, which perform radio tomography in the magnetotail at 8–12 Earth Radii (RE) downstream, and the subsolar magnetosphere at 8–12 RE upstream. These maps will be made at both low-resolutions (at 0.5 RE, 5 s cadence) and high-resolutions (at 0.025 RE, 2 s cadence). In addition, in-situ measurements of the magnetic and electric fields, plasma density will be performed by on-board instruments. In this article, we present an outline of previous missions and designs for magnetospheric studies, along with the science drivers and motivation for the APIS mission. Furthermore, preliminary design results are included to show the feasibility of such a mission. The science requirements drive the APIS mission design, the mission operation and the system requirements. In addition to the various science payloads, critical subsystems of the satellites are investigated e.g., navigation, communication, processing and power systems. Our preliminary investigation on the mass, power and link budgets indicate that the mission could be realized using Commercial Off-the-Shelf (COTS) technologies and with homogeneous CubeSats, each with a 12U form factor. We summarize our findings, along with the potential next steps to strengthen our design study.
    
    document

35. A roadmap towards a space-based radio telescope for ultra-low frequency radio astronomy
    M.J. Bentum; M.K. Verma; R.T. Rajan; A.J. Boonstra; C.J.M. Verhoeven; E.K.A. Gill; A.J. {van der Veen}; H. Falcke; M. Klein Wolt; B. Monna; S. Engelen; J. Rotteveel; L.I. Gurvits;
    Advances in Space Research,
    Volume 65, Issue 2, pp. 856-867, 2020. High-resolution space-borne radio astronomy. DOI: 10.1016/j.asr.2019.09.007
    document

36. Lunar Orbit Design of a Satellite Swarm for Radio Astronomy
    Mok, Sung-Hoon; Guo, Jian; Gill, Eberhard; Rajan, Raj Thilak;
    In 2020 IEEE Aerospace Conference,
    pp. 1-9, 2020. DOI: 10.1109/AERO47225.2020.9172468

37. Autonomous Mission Planning for OLFAR: A Satellite Swarm in Lunar Orbit for Radio Astronomy
    Mok, S; Guo, J; Gill, EKA; Rajan, RT;
    In 71st International Astronautical Congress (IAC),
    IAF/AIAA, 2020.
    document

38. APIS: Applications and Potentials of Intelligent Swarms for magnetospheric studies
    R.T. Rajan; S. Ben-Maor; S. Kaderali; Others;
    In 71th International Astronautical Congress (IAC),
    2020.
    document

39. End-of-life of satellite swarms
    Turner, Calum; Raj Thilak Rajan;
    In 71st International Astronautical Congress (IAC),
    IAF/AIAA, 2020.
    document

40. Relative kinematics of an anchorless network
    R. T. Rajan; G. Leus; A.J. van der Veen;
    Signal Processing,
    Volume 157, pp. 266-279, April 2019. ISSN: 0165-1684. DOI: 10.1016/j.sigpro.2018.11.005
    document

41. Low-frequency observations using high-altitude balloon experiments (LOBE)
    Raj Thilak Rajan; P.Sundaramoorthy; C.J.C.Vertegaal; A.Montagne; V.Karunanithi; M.K.Verma; M.Bentum; C.Verhoeven;
    In 70th International Astronautical Congress (IAC),
    IAF, October 2019.
    document

42. High Data-Rate Inter-Satellite Link (ISL) For Space-Based Interferometry
    Visweswaran Karunanithi; Raj Thilak Rajan; P.Sundaramoorthy; M.K.Verma; C.Verhoeven; M. Bentum; E.W. McCune;
    In 70th International Astronautical Congress (IAC),
    IAF, October 2019.
    document

43. Multiresolution Time-of-arrival Estimation from Multiband Radio Channel Measurements
    T. Kazaz; R.T. Rajan; G.J.M. Janssen; A.J. van der Veen;
    In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    Brighton, UK, IEEE, pp. 4395-4399, May 2019. ISBN: 978-1-4799-8132-8. DOI: 10.1109/ICASSP.2019.8683601
    document

44. Reference-Free Calibration in Sensor Networks
    Raj Thilak Rajan; Rob-van Schaijk; Anup Das; Jac Romme; Frank Pasveer;
    IEEE Sensor letters,
    Volume 2, Issue 3, pp. 1-4, Sept. 2018. DOI: 10.1109/LSENS.2018.2866627
    document

45. Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout
    Anup Das; Paruthi Pradhapan; Willemijn Groenendaal; Prathyusha Adiraju; Raj Thilak Rajan; Francky Catthoor; Siebren Schaafsma; Jeffrey L. Krichmar; Nikil Dutt; Chris {Van Hoof};
    Neural Networks,
    Volume 99, pp. 134-147, 2018. DOI: https://doi.org/10.1016/j.neunet.2017.12.015
    
    Keywords: ...

    Electrocardiogram (ECG), Spiking neural networks, Liquid state machine, Spike timing dependent plasticity (STDP), Homeostatic plasticity, Fuzzy c-Means clustering.
    
    
    Abstract: ...

    Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.
    
    document

46. A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods
    Xie, Xingzhe; Semanjski, Ivana; Gautama, Sidharta; Tsiligianni, Evaggelia; Deligiannis, Nikos; Rajan, Raj; Pasveer, Frank; Philips, Wilfried;
    ISPRS International Journal of Geo-Information,
    Volume 6, Issue 12, pp. 389, Dec 2017. DOI: 10.3390/ijgi6120389
    document

47. Data-driven modeling techniques for indoor CO2 estimation
    Vergauwen, Bob; Agudelo, Oscar Mauricio; Rajan, Raj Thilak; Pasveer, Frank; De Moor, Bart;
    In 2017 IEEE SENSORS,
    pp. 1-3, 2017. DOI: 10.1109/ICSENS.2017.8234156

48. Relative Space-Time Kinematics Of an Anchorless Network
    R.T. Rajan;
    PhD thesis, TU Delft, Fac. EEMCS, October 2016.
    document

49. Joint ranging and synchronization for an anchorless network of mobile nodes
    R.T. Rajan; A.J. van der Veen;
    IEEE Tr. Signal Processing,
    Volume 63, Issue 8, pp. 1925--1940, April 2015.
    document

50. Joint relative position and velocity estimation for an anchorless network of mobile nodes
    R.T. Rajan; G. Leus; A.J. van der Veen;
    Signal Processing,
    Volume 115, pp. 66-78, October 2015. DOI: 10.1016/j.sigpro.2015.02.023
    document

51. Space-based Aperture Array For Ultra-Long Wavelength Radio Astronomy
    R.T. Rajan; A.J. Boonstra; M. Bentum; M. Klein-Wolt; F. Belien; M. Arts; N. Saks; A.J. van der Veen;
    Experimental Astronomy,
    December 2015. DOI: 10.1007/s10686-015-9486-6
    document

52. Joint Clock Synchronization and Ranging: Asymmetrical Time-stamping and Passive Listening
    S.P. Chepuri; R.T. Rajan; G. Leus; A.J. van der Veen;
    IEEE Signal Processing Letters,
    Volume 20, Issue 1, pp. 51-54, January 2013.
    document

53. Synchronization for space based ultra low frequency interferometry
    R.T. Rajan; M.J. Bentum; A.J. Boonstra;
    In IEEE Aerospace Conference,
    Big Sky, Montana, US, IEEE, March 2013.
    document

54. Distributed correlators for Interferometery in space
    R.T. Rajan; M.J. Bentum; A. Gunst; A.J. Boonstra;
    In IEEE Aerospace Conference,
    Big Sky, Montana, US, IEEE, March 2013.
    document

55. Joint Non-Linear Ranging and Affine Synchronization Basis for a Network of Mobile Nodes
    R.T. Rajan; A.J. van der Veen;
    In Proc. 21st European Signal Processing Conference (EUSIPCO),
    Marrakech (Marokko), September 2013.
    document

56. Relative velocity estimation using Multidimensional Scaling
    R.T. Rajan; G.J.T. Leus; A.J. van der Veen;
    In Proc. 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2013),
    St. Maarten (Dutch Antilles), December 2013.
    document

57. The Road To OLFAR - A Roadmap To Interferometric Long-Wavelength Radio Astronomy Using Miniaturized Distributed Space Systems
    S. Engelen; K.A. Quillien; C. Verhoeven; A. Noroozi; P. Sundaramoorthy; A.J. van der Veen; R.T. Rajan; A.J. Boonstra; M. Bentum; A. Meijerink; A. Budianu;
    In IAC 2013,
    Beijing, China, September 2013.
    document

58. Joint motion estimation and clock synchronization for a wireless network of mobile nodes
    R.T. Rajan; A.J. van der Veen;
    In Proc. IEEE ICASSP,
    Kyoto (Japan), IEEE, pp. 2845-2848, May 2012.
    document

59. Orbiting Low Frequency Antenna Array for Radio Astronomy
    R.T. Rajan; S. Engelen; M.J. Bentum; C.J.M. Verhoeven;
    In IEEE Aerospace Conference,
    Montana, USA, pp. 1-11, March 2011. DOI: 10.1109/AERO.2011.5747222
    document

60. Joint ranging and clock synchronization for a satellite array
    R.T. Rajan; A.J. van der Veen;
    In Proc. SPAMEC,
    Cluj-Napoca (Romania), Eurasip, August 2011.
    document

61. Joint ranging and clock synchronization for a wireless network
    R.T. Rajan; A.J. van der Veen;
    In 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP),
    Puerto Rico, IEEE, pp. 297-300, December 2011. ISBN 978-1-4577-2103-8.
    document

62. OLFAR, Adaptive topology for satellite swarms
    A. Budianu; R.T. Rajan; S. Engelen; A. Meijerink; C.J.M. Verhoeven; M.J. Bentum;
    In IAC 2011,
    Cape Town, October 2011.
    document

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