Performance analysis of direction estimation in mimo radar for various antenna array configurations and correlated source scenarios

Multiple-Input Multiple-Output (MIMO) radar uses arrays of antennas at both the transmitter and receiver, providing much more spatial degrees of freedom than conventional single-array radars and enabling richer, more capable sensing. This thesis examines how transmit (Tx) and receive (Rx) array layouts in MIMO radar influence direction-of-arrival (DOA) and direction-of-departure (DOD) estimation performance across systematically designed scenarios. The research framework allows analysis of various configurations well beyond standard uniform linear arrays. Non-uniform, nested, and co-prime arrays are tested through detailed modeling while including correlated source effects. These models operate within realistic radar conditions and make estimation accuracy measurable. Multiple scenarios allow performance comparisons when transmit and receive geometry variations combine with different source correlation levels. Comparisons show distinct advantages and operational constraints for each array architecture, and these findings provide practical implementation insights. The investigation focuses on prominent subspace-based estimation algorithms, specifically RD-MUSIC, 2D-MUSIC, and ESPRIT methodologies. Performance assessment includes SNR condition variations, snapshot quantity changes, and transmit/receive array dimension modifications. RMSE measurements provide angular estimation accuracy as the main metric across all tests. Python simulations throughout the study use multi-source scene modeling combined with realistic array geometries. Previous research shows that DOD and DOA estimation performance depends heavily on array layout variations and on inter-array as well as source correlation. Resolution capabilities and error characteristics connect directly to these fundamental factors through systematic dependencies. Key design factors include transmit and receive geometric structures alongside correlated source presence. Specific operational environments require careful matching of array architecture and algorithm choices.