Advancing the Frontiers of Radar & Sensing
Pioneering research in radar systems, signal processing, and sensor technologies at the College of EME, NUST — shaping the future of detection, imaging, and electronic intelligence.
X-Band & Ku-Band FMCW
GLRT & Neural Radar
IEEE TAES & JASA
Core Research Thrusts
Our work spans the full spectrum of radar and sensor intelligence — bridging rigorous statistical detection theory with high-frequency hardware testbeds.
Channel Matrix-Based MIMO Radar
A unified framework for MIMO radar signal modelling and detection
Our primary research thrust develops and analyses a channel matrix-based data model for MIMO radar and sonar. Unlike conventional covariance-based approaches, this framework explicitly captures the linear time-varying channel between transmit waveforms and received signals, enabling more expressive clutter models and tighter statistical bounds. We derive GLRT detectors, study their asymptotic distributions, and compare channel matrix and covariance-based clutter representations across a range of operational scenarios.
Adaptive Detection & Waveform Optimisation
Optimal detectors and waveforms for cognitive radar
Designing adaptive detectors and optimizing transmit waveforms for cognitive radar operating in clutter-rich environments, including SINR/MI-based waveform design and jammer-robust detection across complex spectral domains.
Model-Aided Deep Learning for Radar
Combining physics-based models with neural network intelligence
Integrating physics-based radar signal models with deep learning architectures for robust target detection in challenging environments, including cognitive sonar and cooperative radar-communications.
SAR Imaging & FMCW Systems
From hardware prototyping to high-resolution radar imaging
Designing and prototyping FMCW-based radar systems — including an X-band SAR lab model — alongside SAR image formation algorithms, VNA-based imaging, and antenna design for radar front-ends.
Breakthrough Publications
High-impact peer-reviewed contributions across IEEE Transactions on Aerospace and Electronic Systems, JASA, and premier IEEE radar conferences.
Adaptive Detectors for Channel Matrix-Based MIMO Radar
T. Ali and C. D. Richmond•IEEE Transactions on Aerospace and Electronic Systems
Adaptive Detector for MIMO Radar in the Presence of Waveform-dependent Clutter
T. Ali and C. D. Richmond•2025 59th Asilomar Conference on Signals, Systems, and Computers
A Comparison of Channel Matrix-Based and Covariance-Based Clutter Models
T. Ali and C. D. Richmond•2025 IEEE International Radar Conference (RADAR), Atlanta, GA, USA
Jammer Robust Model-Aided Deep Learning-Based Target Detection for Cognitive Sonar
T. Ali and C. D. Richmond•The Journal of the Acoustical Society of America, vol. 152, no. 4
Lab News & Updates
FYP Students completed their projects
Congratulations to our final year students for successfully completing and demonstrating their radar prototypes.
Paper Accepted at Asilomar Conference 2025
Our latest work MIMO radar target detection has been accepted for presentation at the Asilomar Conference, USA.
Join Our Research Group
We are actively seeking motivated BS, MS and PhD students, and industry collaborators to advance the frontiers of radar and sensing technology. If you are passionate about signal processing, radar systems, or machine learning for sensing, we want to hear from you.