1 code implementation • 5 May 2024 • Ruxin Zheng, Shunqiao Sun, Hongshan Liu, Honglei Chen, Mojtaba Soltanalian, Jian Li
Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced super-resolution, and robust performance in low Signal-to-Noise Ratio (SNR) environments.
no code implementations • 13 Mar 2024 • Arian Eamaz, Farhang Yeganegi, Yunqiao Hu, Mojtaba Soltanalian, Shunqiao Sun
This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy.
no code implementations • 21 Feb 2024 • Jun Li, Jihwan Youn, Ryan Wu, Jeroen Overdevest, Shunqiao Sun
In automotive radar, time-domain thresholding (TD-TH) and time-frequency domain thresholding (TFD-TH) are crucial techniques underpinning numerous interference mitigation methods.
no code implementations • 9 Dec 2023 • Arian Eamaz, Farhang Yeganegi, Yunqiao Hu, Shunqiao Sun, Mojtaba Soltanalian
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging.
no code implementations • 15 Sep 2023 • Yunqiao Hu, Shunqiao Sun
We utilize a recurrent neural network structure to parameterize the IHT algorithm.
no code implementations • 14 Sep 2023 • Ruxin Zheng, Shunqiao Sun, Hongshan Liu, Honglei Chen, Jian Li
We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot.
no code implementations • 29 Aug 2023 • Shunqiao Sun, Yunqiao Hu, Kumar Vijay Mishra, Athina P. Petropulu
We present a low-complexity widely separated multiple-input-multiple-output (WS-MIMO) radar that samples the signals at each of its multiple receivers at reduced rates.
no code implementations • 1 Nov 2021 • Ruxin Zheng, Shunqiao Sun, David Scharff, Teresa Wu
We present a multi-input multi-output (MIMO) radar transmit and receive signal processing chain, a knowledge-aided approach exploiting the radar domain knowledge and signal structure, to generate high resolution radar range-azimuth spectra for object detection and classification using deep neural networks.
no code implementations • 7 Jul 2020 • Zhaoyi Xu, Athina Petropulu, Shunqiao Sun
The private subcarriers are used to synthesize a virtual array for high angular resolution, and also for improved estimation on the active antenna indices.