dr. R.T. Rajan

Assistant Professor
Circuits and Systems (CAS), Department of Microelectronics

PhD thesis (Oct 2016): Relative Space-Time Kinematics of an Anchorless Network
Promotor: Alle-Jan van der Veen

Expertise: Distributed autonomous systems, Positioning Navigation Timing (PNT), Space systems

Themes: Autonomous sensor systems, XG - Next Generation Sensing and Communication


Raj is an assistant professor with the faculty of electrical engineering, mathematics and computer science (EEMCS) at the Delft university of technology (TUD) and the Co-director of the Delft Sensor AI Lab. He received his PhD in 2016 from the CAS group at TUD, for addressing signal processing challenges of space-based radio astronomy. Previously, he held research positions with diverse responsibilities at IMEC (Eindhoven, 2015-2018), University of Twente (Enschede, 2014), ASTRON (Dwingeloo, 2008-2014), CERN (Geneva, 2007-2008), Politenico di Bari (Bari, 2007-2008), Whirlpool (Pune, 2006-2007), and TIFR-NCRA (Pune, 2005). His research interests lie in statistical inference and machine learning, with applications to distributed and autonomous sensor systems e.g., satellite arrays for space-based interferometry.

If you are interested in pursuing a Master thesis, then drop an email with (a) your interests+passion (b) resume and (c) course list (e.g., IEP, without grades). There are numerous internships and thesis oppurtunities with other companies and institutes.

EE4540 Distributed signal processing

Signal processing techniques for decentralized signal processing

EE4C11 Systems engineering

Introduction to systems engineering processes

ET4386 Estimation and detection

Basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, speech enhancement, radar, telecommunication, localization, system identification, and elsewhere.

Education history

EE3350TU Introduction to Radio Astronomy

(not running) Introduction to the science and technology of radio astronomy

Delft Sensor AI Lab

Distributed AI for sensor networks

Cooperative Relative Navigation of Multi-agent Systems

Develop algorithms for multi-target position, time and orientation tracking in a mobile network of multi-agent systems

Airborne data collection on resilient system architectures

Develop algorithms to realize efficient, robust, cost-effective perception and control for autonomous navigation of drones

PIPP OLFAR: Breakthrough technologies for Interferometry in Space

Combine multiple satellites into one single scientific instrument: a radio telescope in space

Projects history

Low-frequency distributed radio telescope in space

Below 15 MHz, the ionosphere blocks EM signals from the sky. Therefore, can we design a radio telescope in space, using a swarm of inexpensive nano-satellites? Accurate localization and clock recovery is important.

  1. 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.

  2. 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.


  3. 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: https://doi.org/10.1016/j.asr.2019.09.007

  4. 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

  5. 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.

  6. 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),

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

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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.


  14. 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

  15. 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

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

  17. 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.

  18. 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

  19. 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

  20. 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.

  21. 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.

  22. 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.

  23. 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.

  24. 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.

  25. 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.

  26. 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.

  27. 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

  28. 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.

  29. 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.

  30. 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.

BibTeX support

Last updated: 6 Jul 2022