MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture

The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method.

[1]  Wu-chun Feng,et al.  Power and Performance Characterization of Computational Kernels on the GPU , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[2]  Daniel W. Bliss,et al.  Multiple-input multiple-output (MIMO) radar and imaging: degrees of freedom and resolution , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[3]  Bin Liao,et al.  Fast Angle Estimation for MIMO Radar With Nonorthogonal Waveforms , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[4]  D. Fuhrmann,et al.  Transmit beamforming for MIMO radar systems using signal cross-correlation , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[5]  A. Lamecki,et al.  Tuning a Hybrid GPU-CPU V-Cycle Multilevel Preconditioner for Solving Large Real and Complex Systems of FEM Equations , 2011, IEEE Antennas and Wireless Propagation Letters.

[6]  Qian He,et al.  MIMO Radar Moving Target Detection in Homogeneous Clutter , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Holger Blume,et al.  Hardware acceleration of Maximum-Likelihood angle estimation for automotive MIMO radars , 2016, 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP).

[8]  Holger Blume,et al.  Realtime FPGA-based processing unit for a high-resolution automotive MIMO radar platform , 2015, 2015 European Radar Conference (EuRAD).

[9]  Fangqing Wen,et al.  Direction finding in MIMO radar with large antenna arrays and nonorthogonal waveforms , 2019, Digit. Signal Process..

[10]  Troy Kilpatrick.  MIMO radar: theory and application , 2016 .

[11]  Christos Grecos,et al.  A Parallel HEVC Intra Prediction Algorithm for Heterogeneous CPU+GPU Platforms , 2016, IEEE Transactions on Broadcasting.

[12]  R.S. Blum,et al.  High Resolution Capabilities of MIMO Radar , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[13]  Ishuwa C. Sikaneta,et al.  MIMO SAR Processing for Multichannel High-Resolution Wide-Swath Radars , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Fangqing Wen,et al.  Computationally Efficient DOA Estimation Algorithm for MIMO Radar With Imperfect Waveforms , 2019, IEEE Communications Letters.

[15]  F.C. Robey,et al.  MIMO radar theory and experimental results , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[16]  J. Tabrikian,et al.  Target Detection and Localization Using MIMO Radars and Sonars , 2006, IEEE Transactions on Signal Processing.

[17]  Wei Hong,et al.  Higher Order Method of Moments With a Parallel Out-of-Core LU Solver on GPU/CPU Platform , 2014, IEEE Transactions on Antennas and Propagation.

[18]  Xingcheng Li,et al.  Implementation of Space-time Coding and Decoding Algorithms for MIMO Communication System Based on DSP and FPGA , 2019, 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC).

[19]  Xiaowei Hu,et al.  MIMO Radar Imaging With Nonorthogonal Waveforms Based on Joint-Block Sparse Recovery , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Hong Ong,et al.  Comparison of CPU and GPU implementation of computing absolute difference , 2011, 2011 IEEE International Conference on Control System, Computing and Engineering.

[21]  D. J. Rabideau,et al.  Ubiquitous MIMO multifunction digital array radar , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[22]  Braham Himed,et al.  Performance analysis of time division and code division waveforms in co-located MIMO , 2015, 2015 IEEE Radar Conference (RadarCon).

[23]  Chen Hu,et al.  A Deep Collaborative Computing Based SAR Raw Data Simulation on Multiple CPU/GPU Platform , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[24]  Bin Yang,et al.  MIMO radar: time division multiplexing vs. code division multiplexing , 2017 .

[25]  Ta-Sung Lee,et al.  Low-Complexity High-Resolution Parameter Estimation for Automotive MIMO Radars , 2020, IEEE Access.

[26]  William J. Dally,et al.  The GPU Computing Era , 2010, IEEE Micro.

[27]  Fan Zhang,et al.  Accelerating Time-Domain SAR Raw Data Simulation for Large Areas Using Multi-GPUs , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Robin P. Fawcett,et al.  Theory and application , 1988 .

[29]  L.J. Cimini,et al.  MIMO Radar with Widely Separated Antennas , 2008, IEEE Signal Processing Magazine.