Our Research Focus
We develop novel algorithms, systems, and mathematical methodologies across the full radar and sensing pipeline — from waveform optimization and hardware prototyping to advanced statistical detectors and model-aided deep learning.
Core Methodological Frameworks
Click each research thrust to expand detailed mathematical signal models, hardware specifications, and specific investigative thrusts.
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.
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.
Academic & Industry Partners
Collaborating with leading global institutions and industry leaders to pioneer breakthrough sensing innovations.
Duke University
Joint research in theoretical radar signal processing and statistical detector derivations.
Arizona State University
Collaborative thrusts in channel matrix-based MIMO radar models and cognitive sensing.
Electrodynamics Pvt. Ltd.
Industry-funded prototyping for Passive WiFi Radar and Ground Penetrating Radar systems.