基于GPU的相位干涉仪FX鉴相算法

doi:10.12305/j.issn.1001-506X.2022.11.05

摘要/Abstract

摘要：

针对相位干涉仪测向系统对于大量高速实时信号的处理需求, 设计了基于图形处理单元(graphic processing unit, GPU)的频域互相关(简称为FX)鉴相算法, 完成了相应的并行程序设计, 进行了实时数据的测试验证。为充分发挥GPU强大的浮点运算能力和并行数据处理能力, 将涉及大量并行高速数据计算的核心鉴相算法加载在GPU中, 实现了高速并行数据的相关处理和相位提取; 利用中央处理器(central processing unit, CPU)完成了数据调度、分发和简单的数据处理功能。实验测试结果表明, 在较好地保证鉴相精度的条件下, 本文设计的基于GPU的鉴相算法, 其数据处理速度是基于CPU平台的140倍左右, 鉴相速度明显提升, 较为圆满地实现了实时性、可靠性和准确性的设计初衷。

关键词: 图形处理单元, 相位干涉仪, 鉴相算法, 并行计算

Abstract:

Aiming at the processing requirements of a large number of high-speed and real-time signals in the phase interferometer direction-finding system, a FX phase identification algorithm based on graphics processing unit (GPU) is designed. The corresponding parallel programming is completed, and the real-time data are tested and verified. In order to give full play to the powerful floating-point computation and parallel data processing capability of the GPU, the core phase detection algorithm which involves a large number of parallel high-speed data calculation is loaded into GPU, the correlation processing and phase extraction of high-speed parallel data are realized. At the same time, central processing unit (CPU) is used to complete functions of data scheduling, distribution, and simple data processing. The experimental results show that under the condition of guaranteeing the phase-detection accuracy, the data processing speed of the GPU-based phase detection algorithm designed is about 140 times that based on CPU platform. The phase-detection speed is obviously improved, and the design intentions of real-time, reliability, and accuracy are realized satisfactorily.

Key words: graphics processing unit (GPU), phase interferometer, phase idetification algorithm, parallel computing

中图分类号:

TN911

李冬, 焦义文, 高泽夫, 杨文革, 毛飞龙, 滕飞. 基于GPU的相位干涉仪FX鉴相算法[J]. 系统工程与电子技术, 2022, 44(11): 3320-3329.

Dong LI, Yiwen JIAO, Zefu GAO, Wenge YANG, Feilong MAO, Fei TENG. FX phase identification algorithm for GPU-based phase interferometer[J]. Systems Engineering and Electronics, 2022, 44(11): 3320-3329.

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参考文献 34

1	ZHANG X Y, GU Q G, HUANG H D, et al. Protection comparative analysis of typical phase-shifting transformers[C]//Proc. of the Purple Mountain Forum on Smart Grid Protection and Control, 2020.
2	AMANUEL T , GHIRMAY A , GHEBREMESKEL H , et al. Comparative analysis of signal processing techniques for fault detection in three phase induction motor[J]. Journal of Electronics and Informatics, 2021, 3 (1): 61- 76. doi: 10.36548/jei.2021.1.006
3	GHOBADI H, SAVAS C, SPOGLI L, et al. A comparative study of different phase detrending algorithms for scintillation monitoring[C]//Proc. of the 33rd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020.
4	GULKO V L, MESCHERYAKOV A A. Polarization amplitude-phase direction finding methods in two-canal UHF radio beacon navigation systems[EB/OL]. [2021-09-25]. https://arxiv.org/abs/2012.12728.
5	PENA B D, BLAKELY L, RENO M J. Parameter tuning analysis for phase identification algorithms in distribution system model calibration[C]//Proc. of the IEEE Kansas Power & Energy Conference, 2021.
6	WANG Q J , JIN T , MOHAMED M A , et al. A novel linear optimization method for section location of single-phase ground faults in neutral noneffectively grounded systems[J]. IEEE Trans.on Instrumentation and Measurement, 2021, 70, 3513410.
7	ZHOU L , LI Q H , ZHANG Y J , et al. Consumer phase identification under incomplete data condition with dimensional calibration[J]. International Journal of Electrical Power & Energy Systems, 2021, 129, 106851.
8	EMAMI H , SHARIFI A A . A robust seasons algorithm to mitigate the search complexity of ES-PTS in finding phase weighting factors[J]. Soft Computing, 2021, 25 (16): 11405- 11419. doi: 10.1007/s00500-021-05944-6
9	KWIATKOWSKI P . Digital-to-time converter for test equipment implemented using FPGA DSP blocks[J]. Measurement, 2021, 177, 109267. doi: 10.1016/j.measurement.2021.109267
10	TIAN H W , GUO S , ZHAO P , et al. Design and implementation of a real-time multi-beam sonar system based on FPGA and DSP[J]. Sensors, 2021, 21 (4): 1425. doi: 10.3390/s21041425
11	IQBAL J, MANIKANDAN T. FPGA-based reconfigurable architectures for DSP computations[M]//Advances in Smart System Technologies. Singapore: Springer, 2021: 587-594.
12	HALABI L M , ALSOFYANI I M , LEE K B . Hardware implementation for hybrid active NPC converters using FPGA-based dual pulse width modulation[J]. Journal of Paver Eelectronics, 2021, 21 (11): 1669- 1679.
13	DEC G R . LSTM cell implementation on FPGAs[J]. Parallel Processing Letters, 2021, 31 (2): 2150011. doi: 10.1142/S0129626421500110
14	INDRA D, SATRA R, ASSHIDDIQ M J, et al. Performance analysis of GPU-CPU for the face detection[C]//Proc. of the 3rd East Indonesia Conference on Computer and Information Technology, 2021.
15	SHARIFI M R , AKBARIFARD S , QADERI K , et al. Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation[J]. Scientific Reports, 2021, 11 (1): 15611. doi: 10.1038/s41598-021-95159-4
16	ELSHAWI R , WAHAB A , BARNAWI A , et al. DLBench: a comprehensive experimental evaluation of deep learning frameworks[J]. Cluster Computing, 2021, 24 (3): 2017- 2038. doi: 10.1007/s10586-021-03240-4
17	TEZCAN C . Optimization of advanced encryption standard on graphics processing units[J]. IEEE Access, 2021, 9, 67315- 67326. doi: 10.1109/ACCESS.2021.3077551
18	ZHU B R , JIANG Y Y , GU M , et al. A GPU acceleration framework for motif and discord based pattern mining[J]. IEEE Trans.on Parallel and Distributed Systems, 2021, 32 (8): 1987- 2004. doi: 10.1109/TPDS.2021.3055765
19	YEO B , LEE M J , KUNO Y . GPU-accelerated event reconstruction for the COMET Phase-I experiment[J]. Computer Physics Communications, 2021, 258, 107606. doi: 10.1016/j.cpc.2020.107606
20	KIM J H , SHAN U , KIM D H . GPU-based embedded edge server configuration and offloading for a neural network service[J]. The Journal of Supercomputing, 2021, 77 (8): 8593- 8621. doi: 10.1007/s11227-021-03623-9
21	CLUCAS R, BLAKELY P, NIKIFORAKIS N. Ripple: simplified large-scale computation on heterogeneous architectures with polymorphic data layout[EB/OL]. [2021-09-25]. https://arxiv.org/abs/2104.08571v1.
22	王可东, 李鸿田, 侯绍东, 等. GPS信号FFT捕获的GPU实现[J]. 全球定位系统, 2011, 36 (6): 12- 16.
	WANG K D , LI H T , HOU S D , et al. Implementation of a GPS signal FFT acquisition algorithm with a GPU chip[J]. GNSS World of China, 2011, 36 (6): 12- 16.
23	刘燕都, 郑海昕, 王小平. 基于CUDA的数字调相信号并行解调程序设计[J]. 通信技术, 2016, 49 (2): 227- 232.
	LIU Y D , ZHENG H X , WANG X P . Parallel programming of PSK signal demodulation based on CUDA[J]. Communications Technology, 2016, 49 (2): 227- 232.
24	杨千禾. GPU并行计算在雷达信号处理中的应用[J]. 软件导刊, 2021, 20 (3): 199- 203.
	YANG Q H . Application of GPU parallel computing in radar signal processing[J]. Software Guide, 2021, 20 (3): 199- 203.
25	谢维华, 张军. 一种实时GNSS软件接收机CUDA相关器算法[C]//第5届中国卫星导航学术年会, 2014: 40-44.
	XIE W H, ZHANG J. A new CUDA correlator algorithm for real-time GNSS software receiver[C]//Proc. of the 5th China Satellite Navigation Academic Annual Conference, 2014: 40-44.
26	陈大强. 基于GPU的警戒雷达信号处理软件设计[D]. 成都: 电子科技大学, 2018.
	CHEN D Q. Design of warning radar signal processing software based on GPU[D]. Chengdu: University of Electronic Science and Technology of China, 2018.
27	TEINBERGS J, ABERFELDS A, BEZRUKOVS V, et al. Analysis of the research status of phase interferometer deblurring[C]//Proc. of the IEEE International Conference on Artificial Intelligence and Industrial Design, 2021.
28	CHEN Y F , HUANG Z Y , WANG D , et al. Single-scan, dual-functional interferometer for fast spatiotemporal characterization of few-cycle pulses[J]. Optics Letters, 2020, 45 (18): 5081- 5084.
29	XU M J , LIU C . Single station passive location CRLB analysis in consideration of motion rotation baseline[J]. Radar Science and Technology, 2019, 17 (1): 77- 82.
30	YE X , LIU Z , MA Y , et al. A novel normalized cross-correlation speckle-tracking ultrasound algorithm for the evaluation of diaphragm deformation[J]. Frontiers in Medicine, 2021, 8, 612933.
31	PRASETIYOWATI M I , MAULIDEVI N U , SURENDRO K . Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest[J]. Journal of Big Data, 2021, 8, 84.
32	SHIRAISHI Y , KAWAHARA Y , YAMASHITA O , et al. Neural decoding of electrocorticographic signals using dynamic mode decomposition[J]. Journal of Neural Engineering, 2020, 17 (3): 036009.
33	ZHANG X , WANG Q J , HE T . Transient and steady-state viscoelastic contact responses of layer-substrate systems with interfacial imperfections[J]. Journal of the Mechanics and Physics of Solids, 2020, 145, 104170.
34	BHATIA J, DAYAL A, JHA A, et al. Object classification technique for mmWave FMCW radars using range-FFT features[C]//Proc. of the International Conference on Communication Systems & Networks, 2021.

基线名称	42	14	12	32	43	13
长度/m	0.2	0.3	0.5	0.5	0.7	1

设计指标	指标参数
射频频率	2 250
采样频率	50
中频频率	137.5
下变频频率	2 387.5

运算单元	GPU平台/ms	CPU平台/ms	加速比
类型转换	66.925	1 273.26	19.0
FFT	25.365	1 403.81	55.3
共轭相乘	75.525	3 158.24	41.9
能量累积	117.135	3 854.70	32.9

测试次数	GPU平台/ms	CPU平台/ms	平台加速比
第1次	895	121 660	135.9
第2次	841	120 050	142.7
第3次	872	120 930	138.7
第4次	862	114 910	13.33
第5次	853	116 220	13.62
第6次	872	113 470	13.01
第7次	878	124 450	14.17
第8次	884	118 920	13.45
第9次	849	118 110	13.91
第10次	829	120 040	14.48

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