arrow
Return

High-Throughput Hyperparameter-Free Sparse Source Location for Massive TDM-MIMO Radar: Algorithm and FPGA Implementation

delete2023-01-01
delete11
PRE
AI
Y
Yongchao Zhang
Y
Yulin Huang *
Y
Yongwei Zhang
S
Shuaidi Liu
J
Jiawei Luo
X
Xiaokun Zhou
J
Jianyu Yang
A
Andreas Jakobsson
DOI:10.1109/TGRS.2023.3323517delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
The sparse iterative covariance-based estimation (SPICE) algorithm is promising for hyperparameter-free sparse source location for time-division-multiplexing multiple-input-multiple-output (TDM-MIMO) radar systems, with well-documented merits in resolution enhancement and sidelobe suppression. Regrettably, the method typically requires a large number of iterations to converge, each requiring high-dimensional matrix operations, rendering the existing batch SPICE method impractical and expensive to implement in hardware when dealing with massive TDM-MIMO observations. In order to enable real-time processing, this article presents a subaperture-recursive (SAR) SPICE method, allowing for recursively refining the location parameters for each received (RX) block observation that becomes available sequentially in time. The proposed method not only offers the same benefits as the batch SPICE method but also allows for computationally efficient online processing, without the need for high-dimensional matrix operations, notably reducing the required hardware resources as well as processing time. We further present a high-throughput architecture for the resulting method on an XCZU15EG-FFVB1156 field-programmable gate array (FPGA). In combination with simulation results, we demonstrate the effectiveness through experimental data measured by a cascaded MIMO radar system with 12 transmit (TX) and 16 receive (RX) antennas, demonstrating that the computational time of resolving closely spaced sources on 256 predefined grid points can be processed in merely 12 ms.
Keywords:
SPICE
Hardware
Manganese
Real-time systems
Sparse matrices
MIMO radar
Field programmable gate arrays
Field-programmable gate array (FPGA)
sparse iterative covariance-based estimation (SPICE)
subaperture-recursive (SAR)
time-division-multiplexing multiple-input--multiple-output (TDM-MIMO)

Journal

IEEE Transactions on Geoscience and Remote Sensing cover
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
Papers:
2.1W
Citations:
10.7W

Organization

L
lund university
Scholars:
4.1W
Papers: 3.9W
Citations: 54
Cited Papers

Cited Papers

Identification of Primary CO in Coal Seam Based on Oxygen Isotope Method
err2017-06-13
err0
PREAI
errYongliang Yang; Zenghua Li; Shisong Hou; Jinhu Li; Leilei Si; Yinbo Zhou
errShare
errSave
An Improved Predictive Controller on the FPGA by Hardware Matrix Inversion
err2018-09-01
err19
PREAI
errXu, Yunwen; Li, Dewei; Xi, Yugeng; Lan, Jian; Jiang, Tengfei
errShare
errSave
The gating mechanism of the large mechanosensitive channel MscL
err2001-02-01
err0
PREAI
errSergei Sukharev; Monica Betanzos; Chien-Sung Chiang; H. Robert Guy
errShare
errSave
Examining Racial and Ethnic Disparities in the Incidence of Urinary Tract Infection During Pregnancy: A Cohort Study
err2024-12-01
err0
PREAI
errMcateer, S.; Wartko, P.; Hajat, A.; Fuller, S.; Shortreed, S. M.; Butler, A.; Enquobahrie, D. A.; Garcia, R.; Dublin, S.
errShare
errSave
errShare
errSave
A Matrix-Inversion Technique for FPGA-Based Real-Time EMT Simulation of Power Converters
err2019-02-01
err36
PREAI
errHadizadeh, Ali; Hashemi, Matin; Labbaf, Mohammad; Parniani, Mostafa
errShare
errSave
Automotive Radars A review of signal processing techniques
err2017-03-01
err738
PREAI
errPatole, Sujeet; Torlak, Murat; Wang, Dan; Ali, Murtaza
errShare
errSave
Wideband Sparse Reconstruction for Scanning Radar
err2018-01-01
err51
PREAI
errZhang, Yongchao; Jakobsson, Andreas; Zhang, Yin; Huang, Yulin; Yang, Jianyu
errShare
errSave
researcher View more