Sorry, you need to enable JavaScript to visit this website.

Low-Complexity Parameter Learning for OTFS Modulation Based Automotive Radar

Primary tabs

Citation Author(s):
Chenwen Liu, Shengheng Liu, Zihuan Mao, Yongming Huang, Haiming Wang
Submitted by:
Shengheng Liu
Last updated:
21 June 2021 - 11:14pm
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Chenwen Liu
Paper Code:
1768

Abstract 

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.

up
0 users have voted:

Dataset Files

slides

(30)

Poster

(28)