Research

1) Areas of Expertise (or, what statistics do for us)

Our research is applied in areas such as:

  • Positioning systems and localization, including GNSS and non-GNSS systems:

    • Advanced GNSS receiver designs targeting robustness, integrity, and reliability (i.e. rejection of jamming / spoofing / meaconing, multipath mitigation)

    • Open loop GNSS receivers and Direct Position Estimation.

    • Indoor positioning (based on non-GNSS infrastructures such as WiFi, UWB, ZigBee, Bluetooth or INS)

    • Signals of opportunity for navigation (e.g. DTV, WiFi, LTE, FM, AM, etc.)

  • Receiver design for communication systems:

    • Acquisition and synchronization.

    • Resource management and energy efficiency using Game Theoretical tools.

    • Emerging communications systems and solutions for: 5G, space communications, radar, WSN, and M2M communications.

  • Simultaneous Localization and Mapping (SLAM) techniques:

    • Data and Sensor fusion.

    • Unmanned vehicles.

    • Search and rescue technologies for disaster management and relief.

  • Biostatistics and Mathematical Biology:

    • Epidemiological surveillance and forecasting.

    • Inference in computational neuroscience.

    • Genomics.

    • Engineering support in biomedical and biostatistical applications.

  • Engineering developments:

    • RF front ends.

    • Baseband prototyping of signal processing solutions.

    • Software Defined Radio solutions (CPU, GPU, DSP, and FPGA)

  • Emerging applications

 

2) Fundamental Research (or, what we do for statistics)

  • Design, analysis and implementation of inferential methodologies:

    • Both frequentist and Bayesian paradigms are investigated.

    • Computational methods for Maximum Likelihood estimation

    • Computational methods for Bayesian inference, including exact and MCMC methods, as well as Factor Graphs modelling.

    • Point estimation, Hypothesis testing, and Interval estimation both in centralized and distributed systems.

    • Fundamental estimation bounds.

  • Nonlinear Bayesian filtering and sequential signal processing:

    • Gaussian filters: Kalman filtering and Extended Kalman filtering

    • Derivative-free Gaussian filters: Unscented Kalman filtering, Cubature Kalman filtering, and Quadrature Kalman filtering.

    • Particle filtering and Sequential Monte Carlo methodology for nonlinear/non-Gaussian state-space models.

  • Sequential inference in high-dimensional complex systems:

    • Bayesian filtering in high-dimensional systems.

    • Detection and estimation algorithms over complex systems.

    • Partitioning schemes for dimensionality reduction.

  • Robust Statistics:

    • Model inaccuracies (parametric/distributional uncertainties)

    • Adaptive filtering, outlier rejection, and covariance matching.

    • Robustness of sequential methods

    • Robustness analysis