主动声呐实时信号处理算法的MPSoC优化实现

doi:10.12305/j.issn.1001-506X.2025.10.02

摘要/Abstract

摘要：

针对水下无人航行器（underwater unmanned vehicle，UUV）主动声呐系统对信号处理实时性、能效比及集成度的需求，采用模块化设计以及软硬件协同设计思想，提出一种基于异构多处理器片上系统（multi-processor system on chip，MPSoC）的主动声呐实时信号处理算法的加速方案。首先研究适合边缘端部署的声呐信号处理算法；然后设计基于MPSoC的加速计算结构，将数字下变频、逆/快速傅里叶变换、波束形成等具有高计算复杂性的处理步骤移植到可编程逻辑端，实现显著加速；最后将目标检测等复杂度较低的步骤部署在处理器系统端，实现更高的灵活性。仿真及湖上试验结果表明，提出的方案可在数据更新周期的41%时间内完成1帧回波数据的实时处理，并可在复杂水下环境下实时有效探测运动目标。该方案在水下UUV主动声呐探测领域具有广阔的应用前景。

关键词: 水下无人航行器, 主动声呐, 多处理器片上系统, 实时信号处理, 硬件加速

Abstract:

Addressing the real-time, energy-efficient, and integrated processing demands of underwater unmanned vehicle （UUV） active sonar systems, an acceleration scheme for an active sonar real-time signal processing algorithm is proposed, based on heterogeneous a multi-processor system on chip （MPSoC） with a modular design and hardware-software co-design concepts. Firstly, sonar signal processing algorithms suitable for edge deployment are studied. Secondly, an accelerated computing architecture based on MPSoC is designed, transferring computationally intensive processing steps such as digital down conversion, inverse/fast Fourier transform （IFFT/FFT）, and beamforming to the programmable logic terminal, achieving significant acceleration. Finally, deploy low-complexity steps such as target detection on the processor system side, achieving higher flexibility. Simulation and lake test indicate that the proposed scheme can complete real-time processing of one echo data frame within 41% of the data update cycle. It can effectively detect moving targets in complex underwater environment in real-time. This scheme holding great potential for applications in UUV active sonar detection scenarios.

Key words: underwater unmanned vehicle （UUV）, active sonar, multi-processor system on chip （MPSoC）, real-time signal processing, hardware acceleration

中图分类号:

邹佳运, 师英杰, 吴永清, 郝程鹏, 王东辉. 主动声呐实时信号处理算法的MPSoC优化实现[J]. 系统工程与电子技术, 2025, 47(10): 3137-3147.

Jiayun ZOU, Yingjie SHI, Yongqing WU, Chengpeng HAO, Donghui WANG. MPSoC optimization implementation of active sonar real-time signal processing algorithm[J]. Systems Engineering and Electronics, 2025, 47(10): 3137-3147.

图/表 22

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

图10

图11

图12

表2

图13

图14

表3

表4

图15

图16

图17

图18

参考文献 35

1	高一丁, 吴敏, 郝程鹏, 等. 基于FrFT-Keystone运动补偿的OFDM声纳高速微弱目标相参积累检测算法[J]. 系统工程与电子技术, 2024, 46 (4): 1157- 1166.
	GAO Y D, WU M, HAO C P, et al. Coherent integration and detection algorithm for high-speed weak targets in OFDM sonar based on FrFT-Keystone motion compensation[J]. Systems Engineering and Electronics, 2024, 46 (4): 1157- 1166.
2	许彦伟, 薛勐, 刘明刚, 等. 多无人水下航行器协同探测声呐宽带波形设计与性能分析[J]. 电子与信息学报, 2023, 45 (10): 3796- 3804. doi: 10.11999/JEIT221265
	XU Y W, XUE M, LIU M G, et al. Wideband waveform design and performance analysis for multiple unmanned underwater vehicle cooperative detection sonar[J]. Journal of Electronics & Information Technology, 2023, 45 (10): 3796- 3804. doi: 10.11999/JEIT221265
3	李启虎. 声呐信号处理引论 [M]. 北京: 科学出版社, 2012.
	LI Q H. Introduction to sonar signal processing[M]. Beijing: Science Press, 2012.
4	YU G, YANG T C, PIAO S C. Estimating the delay-Doppler of target echo in a high clutter underwater environment using wideband linear chirp signals: evaluation of performance with experimental data[J]. The Journal of the Acoustical Society of America, 2017, 142 (4): 2047- 2057. doi: 10.1121/1.5005888
5	KNIGHT W C, PRIDHAM R G, KAY S M. Digital signal processing for sonar[J]. Proceedings of the IEEE, 1981, 69 (11): 1451- 1506. doi: 10.1109/PROC.1981.12186
6	LERDA O, MIAN A, GINOLHAC G, et al. Robust detection for mills cross sonar[J]. IEEE Journal of Oceanic Engineering, 2024, 49 (3): 1009- 1024. doi: 10.1109/JOE.2024.3374958
7	WU M, HAO C P, HU Q, et al. Sparsity-based processing to enhance the reverberation suppression for FDA-MIMO sonars[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (2): 1556- 1569. doi: 10.1109/TAES.2023.3336852
8	张舒皓, 马晓川, 杨力, 等. 基于多片高性能DSP的主动声呐低速运动小目标探测系统[J]. 水下无人系统学报, 2019, 27 (6): 636- 643.
	ZHANG S H, MA X C, YANG L, et al. Multi-chip DSP system for active sonar detecting low-speed small targets[J]. Journal of Unmanned Undersea Systems, 2019, 27 (6): 636- 643.
9	李宾. 基于多核DSP的声呐信号处理机系统设计 [D]. 北京: 中国科学院大学, 2013.
	LI B. Design of sonar signal processing system based on multi-core DSPs [D]. Beijing: University of Chinese Academy of Sciences, 2013.
10	汲夏, 丛卫华, 杜栓平. 基于C6678多核数字信号处理器的声纳信号并行处理设计[J]. 兵工学报, 2016, 37 (8): 1476- 1481. doi: 10.3969/j.issn.1000-1093.2016.08.020
	JI X, CONG W H, DU S P. Sonar signal processing parallel design based on C6678 multicore DSP[J]. Acta Armamentarii, 2016, 37 (8): 1476- 1481. doi: 10.3969/j.issn.1000-1093.2016.08.020
11	刘纪元, 李淑秋, 李丽英, 等. 合成孔径声呐并行实时处理研究[J]. 电子与信息学报, 2003, (6): 777- 783.
	LIU J Y, LI S Q, LI L Y, et al. Study on real-time and parallel implementation of synthetic aperture sonar signal processing[J]. Journal of Electronics & Information Technology, 2003, (6): 777- 783.
12	王朋, 任宇飞, 黄勇, 等. 基于TMS320C6678三维成像声纳信号处理算法的设计与实现[J]. 海军工程大学学报, 2014, 26 (2): 85- 90，112.
	WANG P, REN Y F, HUANG Y, et al. Design and implementation of 3D acoustical imaging sonar signal processing method based on TMS320C6678[J]. Journal of Naval University of Engineering, 2014, 26 (2): 85- 90，112.
13	LIU X, WANG J Y. Design of multi-channel signal acquisition and transmission unit based on multi-core DSP[C]// Proc. of the OES China Ocean Acoustics Conference, 2021.
14	YANG X, ZHUANG C, FENG W Q, et al. FPGA implementation of a deep learning acceleration core architecture for image target detection[J]. Applied Sciences, 2023, 13 (7): 4144.
15	连红飞, 李东升, 蒋彦雯, 等. 一种伯努利粒子滤波器的FPGA实现[J]. 系统工程与电子技术, 2024, 47 (2): 398- 406.
	LIAN H F, LI D S, JIANG Y W, et al. FPGA implementation of a Bernoulli particle filter[J]. Journal of Systems Engineering and Electronics, 2024, 47 (2): 398- 406.
16	GONG L, WANG C, LI X, et al. Improving HW/SW adaptability for accelerating CNNs on FPGAs through a dynamic/static co-reconfiguration approach[J]. IEEE Trans. on Parallel and Distributed Systems, 2021, 32 (7): 1854- 1865. doi: 10.1109/TPDS.2020.3046762
17	YANG T, ZHANG X Y, XU Q, et al. An embedded-GPU-based scheme for real-time imaging processing of unmanned aerial vehicle borne video synthetic aperture radar[J]. Remote Sensing, 2024, 16 (1): 191. doi: 10.3390/rs16010191
18	SANFORD C J, THOMAS B W, HUNTER A J. Fourier-domain wavefield rendering for rapid simulation of synthetic aperture sonar data[J]. IEEE Journal of Oceanic Engineering, 2024, 49 (4): 1501- 1515.
19	李晓敏, 侯朝焕, 鄢社锋. 一种基于GPU的主动声纳宽带信号处理实时系统[J]. 传感技术学报, 2011, 24 (9): 1279- 1283. doi: 10.3969/j.issn.1004-1699.2011.09.011
	LI X M, HOU C H, YAN S F. A real-time signal processing system of broadband active sonar based on GPU[J]. Chinese Journal of Sensors and Actuators, 2011, 24 (9): 1279- 1283. doi: 10.3969/j.issn.1004-1699.2011.09.011
20	BUSKENES J I, ÅSEN J P, NILSEN C I C, et al. An optimized GPU implementation of the MVDR beamformer for active sonar imaging[J]. IEEE Journal of Oceanic Engineering, 2015, 40 (2): 441- 451. doi: 10.1109/JOE.2014.2320631
21	詹飞, 马晓川, 杨力. 众核处理架构在水下航行器相位编码脉冲回波检测中的应用[J]. 声学学报, 2018, 43 (4): 445- 452.
	ZHAN F, MA X C, YANG L. Application of many-core processing architecture to echo detection of phase-coded pulse for autonomous underwater vehicle[J]. ACTA ACUSTICA, 2018, 43 (4): 445- 452.
22	ADVANCED MICRO DEVICES. Zynq UltraScale+ MPSoC data sheet: overview[EB/OL]. [2024-08-04]. https://www.amd.com/content/dam/xilinx/support/documents/data_sheets/ds891-zynq-ultrascale-plus-overview.pdf.
23	ADVANCED MICRO DEVICES. Zynq UltraScale+ device technical reference manual [EB/OL]. [2024-08-04]. https: //docs.amd.com/r/en-US/ug1085-zynq-ultrascale-trm.
24	李强, 武文波, 何明一. 基于MPSoC的遥感图像目标检测算法硬件加速研究[J]. 航天返回与遥感, 2022, 43 (1): 58- 68. doi: 10.3969/j.issn.1009-8518.2022.01.006
	LI Q, WU W B, HE M Y. Accelerator of remote sensing image object detection based on MPSoC[J]. Spacecraft Recovery & Remote Sensing, 2022, 43 (1): 58- 68. doi: 10.3969/j.issn.1009-8518.2022.01.006
25	PéREZ A, RODRíGUEZ A, OTERO A, et al. Run-time reconfigurable MPSoC-based on-board processor for vision-based space navigation[J]. IEEE Access, 2020, 8, 59891- 59905. doi: 10.1109/ACCESS.2020.2983308
26	WIEHLE S, MANDAPATI S, GUNZEL D, et al. Synthetic aperture radar image formation and processing on an MPSoC[J]. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60
27	王康景. 基于ZYNQ MPSOC的实时高分辨率SAR成像处理系统 [D]. 西安: 西安电子科技大学, 2020.
	WANG K J. Real-time and high-resolution SAR imaging processing system based on ZYNQ MPSOC [D]. Xi’an: Xidian University, 2020.
28	阚成良. AUV载多波束声呐接收系统硬件平台设计与实现[D]. 哈尔滨: 哈尔滨工程大学, 2019.
	KAN C L. Design and implementation of hardware platform for multi-beam sonar receiving system with AUV[D]. Harbin: Harbin Engineering University, 2019.
29	田坦. 声呐技术[M]. 哈尔滨: 哈尔滨工程大学出版社, 2010: 10−20.
	TIAN T. Sonar technology[M]. Harbin: Harbin Engineering University Press, 2010: 10−20.
30	KELLY E, WISHNER R. Matched-filter theory for high-velocity, accelerating targets[J]. IEEE Trans. on Military Electronics, 1965, 9 (1): 56- 69. doi: 10.1109/TME.1965.4323176
31	DATTA D, DUTTA H S. Hardware optimized digital down converter for multi-standard radio receiver[J]. Analog Integrated Circuits and Signal Processing, 2024, 118 (3): 567- 575. doi: 10.1007/s10470-023-02227-y
32	鄢社锋, 马晓川. 宽带波束形成器的设计与实现[J]. 声学学报, 2008, 33 (4): 316- 326.
	YAN S F, MA X C. Designs and implementations of broadband beamformers[J]. ACTA ACUSTICA, 2008, 33 (4): 316- 326.
33	DONG H T, MA S L, SUO J, et al. Matched stochastic resonance enhanced underwater passive sonar detection under non-Gaussian impulsive background noise[J]. Sensors, 2024, 24 (9): 2943. doi: 10.3390/s24092943
34	LEPPINEN H. Current use of Linux in spacecraft flight software[J]. IEEE Aerospace and Electronic Systems Magazine, 2017, 32 (10): 4- 13. doi: 10.1109/MAES.2017.160182
35	HANS J K. The userspace I/O HOWTO [EB/OL]. [2024-08-04]. https://www.kernel.org/doc/html/latest/driver-api/uio-howto.html.

资源类型	消耗量	资源总量	占比/%
FF	90 805	682 000	13.30
LUT	56 181	341 000	16.46
BRAM	8.72 Mb	26.2 Mb	33.27
DSP Slice	72	3528	2.04

目标参数	参数值
平台运行速度/（m/s）	5.0
目标距离/m	2 900
目标速度/（m/s）	−5.0
目标方位/（°）	4.0
信噪比/dB	−15

目标参数	MPSoC检测结果	CPU检测结果	真实值
目标距离/m	2 920	2 887	2 900
目标速度/（m/s）	−5.2	−5.1	−5.0
目标方位/（°）	3.9	3.8	4.0

阶段	名称	MPSoC用时/ms	CPU用时/ms	备注
信号预处理	数字下变频	4.26	84.86	—
	FFT	6.11	29.03	64次2 048点用时
	波束形成	13.42	57.38	64通道×1 281频点×15方向用时
	IFFT	1.51	6.68	15次2 048点用时
信号后处理	求模值	9.52	6.63	15次2 048点用时
	背景归一化	6.34	6.33	15次用时
	目标检测与参数估计	0.78	6.63	—
总用时		41.94	184.58	—

[1]	连红飞, 李东升, 蒋彦雯, 范红旗, 肖怀铁, 王国嫣. 一种伯努利粒子滤波器的FPGA实现[J]. 系统工程与电子技术, 2025, 47(2): 398-405.
[2]	陈昊然, 王天昊, 路美娜, 宋茂新, 罗环, 吴晓宇, 骆冬根, 裘桢炜. 基于HLS的高精度位移测量算法的硬件加速设计[J]. 系统工程与电子技术, 2025, 47(2): 341-351.
[3]	叶有时，唐林波，赵保军，蔡晓芳. 基于SOPC的深空目标实时跟踪系统[J]. Journal of Systems Engineering and Electronics, 2009, 31(12): 3002-3006.