7 papers · 1 filter
Small Language Model enabled Autonomous agent for Language-Conditioned Cognitive Radar
Minhaj Uddin Ahmad, Zakia Zaman, Shunqiao Sun +1
Modern radar systems require adapting their processing strategies in response to changing interference, clutter, and data availability. This paper introduces a framework for a smal…
A Game-Theoretic Approach for High-Resolution Automotive FMCW Radar Interference Avoidance
Yunian Pan, Jun Li, Lifan Xu +2
Nonlinear frequency hopping has emerged as a promising approach for mitigating interference and enhancing range resolution in automotive FMCW radar systems. Achieving an optimal ba…
Deep Frequency Attention Networks for Single Snapshot Sparse Array Interpolation
Ruxin Zheng, Shunqiao Sun, Hongshan Liu
Sparse arrays have been widely exploited in radar systems because of their advantages in achieving large array aperture at low hardware cost, while significantly reducing mutual co…
Signal Processing Challenges in Automotive Radar
Sandeep Rao, Rajan Narasimha, Shunqiao Sun
As automotive radars continue to proliferate, there is a continuous need for improved performance and several critical problems that need to be solved. All of this is driving resea…
Advancing Single-Snapshot DOA Estimation with Siamese Neural Networks for Sparse Linear Arrays
Ruxin Zheng, Shunqiao Sun, Hongshan Liu +1
Single-snapshot signal processing in sparse linear arrays has become increasingly vital, particularly in dynamic environments like automotive radar systems, where only limited snap…
IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays
Yunqiao Hu, Shunqiao Sun
Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radars. This is due to the…
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation
Ruxin Zheng, Shunqiao Sun, Hongshan Liu +2
We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot. Classical subspace-based methods like MUSIC and ESPRIT use sp…