2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSS-6.1
Paper Title LOW-COMPLEXITY PARAMETER LEARNING FOR OTFS MODULATION BASED AUTOMOTIVE RADAR
Authors Chenwen Liu, Shengheng Liu, Zihuan Mao, Yongming Huang, Haiming Wang, Southeast University, China
SessionSS-6: Intelligent Sensing and Communications for Emerging Applications
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Special Sessions: Intelligent Sensing and Communications for Emerging Applications
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Abstract Orthogonal time frequency space (OTFS) as an emerging modulation technique in the 5G and beyond era exploits full time-frequency diversity and is robust against doubly-selective channels in high mobility scenarios. In this work, we consider an OTFS modulation based automotive joint radar-communication system and focus on the design of low-complexity parameter estimation algorithm for radar targets. It is well known that target parameter estimation in OTFS radar is computationally much more expensive than the orthogonal frequency division multiplex based platform, which hampers low-cost and real-time implementation. In this context, an efficient Bayesian learning scheme is proposed for OTFS automotive radars, which leverages the structural sparsity of radar channel in the delay-Doppler domain. We also reduce the dimension of the measurement matrix by incorporating the prior knowledge on the motion parameter limit of the true targets. Numerical simulation results are presented to demonstrate the superior performance of the proposed method in comparison with the state-of-the-art.