TY  - EJOU
AU  - Biswas, Pallabi 
AU  - Sur, Samarendra Nath 
AU  - Bera, Rabindranath 
AU  - Imoize, Agbotiname Lucky 
AU  - Li, Chun-Ta 

TI  - Advanced Signal Processing and Modeling Techniques for Automotive Radar: Challenges and Innovations in ADAS Applications
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 1
SN  - 1526-1506

AB  - Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems (ADAS) and autonomous driving, enabling robust environmental perception through precise range-Doppler and angular measurements. It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects. However, real-world deployment of automotive radar faces significant challenges, including mutual interference among radar units and dense clutter due to multiple dynamic targets, which demand advanced signal processing solutions beyond conventional methodologies. This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar. Emphasis is placed on direction-of-arrival (DoA) estimation algorithms such as Bartlett beamforming, Minimum Variance Distortionless Response (MVDR), Multiple Signal Classification (MUSIC), and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT). Among these, ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC, making it particularly advantageous for real-time applications. Furthermore, the study evaluates state-of-the-art tracking algorithms, including the Kalman Filter (KF), Extended KF (EKF), Unscented KF, and Bayesian filter. EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models. The integration of machine learning approaches for target detection and classification is also discussed, highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors (KNN) and the enhanced accuracy provided by Support Vector Machines (SVM). A brief overview of benchmark radar datasets, performance metrics, and relevant standards is included to support future research. The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing, particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.
KW  - Automotive radar; radar waveforms; target direction; tracking; classification

DO  - 10.32604/cmes.2025.067724