基于EM-VB的分布式接收运动目标直接符号检测方法

doi:10.12305/j.issn.1001-506X.2024.04.31

摘要/Abstract

摘要：

相比于传统分布式组阵接收采用的参数差异估计、信号校准合成以及符号检测的逐级处理结构, 直接利用多个观测信号进行符号检测能够抑制信号间校准精度不佳带来的性能损失问题, 但现有方法主要针对收发均静止或收发理想同步的情形。研究了一种最大似然准则下的分布式接收运动目标直接符号检测方法, 首先给出了直接符号检测求解模型, 针对模型中多组未知参数的优化问题, 推导分析了各参数近似闭式解, 采用基于迭代重估的闭环处理结构, 利用多个未知参数和信息符号进行联合寻优。仿真实验结果表明, 所提方法性能明显优于传统合成处理方法, 与现有联合处理结构相比, 在观测站数目较多时具有明显优势。

关键词: 分布式接收, 运动目标, 符号检测, 最大似然, 期望最大化

Abstract:

Compared with the hierarchical processing structure of parameter difference estimation, signal calibration synthesis and symbol detection in traditional distributed array reception, direct symbol detection using multiple observation signals can suppress the performance loss caused by poor calibration accuracy between signals, but the existing methods are mainly aimed at the situation where both the transmitter and the receiver are stationary or ideal synchronization. A direct symbol detection method for distributed receiving moving targets under the maximum likelihood criterion is studied. Firstly, a direct symbol detection solution model is given. Aiming at the optimization problem of multiple groups of unknown parameters in the model, the approximate closed form solutions of each parameter are derived and analyzed. A closed-loop processing structure based on iterative re-evaluation is adopted, and multiple unknown parameters and information symbols are used for joint optimization. Simulation results show that the performance of the proposed method is obviously superior than that of the traditional synthetic processing methods. Compared with the existing joint processing structure, the proposed method has greater advantages when the number of observation stations is large.

Key words: distributed receiving, moving target, symbol detection, maximum likelihood, expectation-maximization

中图分类号:

TN911.5

张凯, 田瑶. 基于EM-VB的分布式接收运动目标直接符号检测方法[J]. 系统工程与电子技术, 2024, 46(4): 1422-1430.

Kai ZHANG, Yao TIAN. Direct symbol detection method for distributed receiving moving targets based on EM-VB[J]. Systems Engineering and Electronics, 2024, 46(4): 1422-1430.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

表1

参考文献 32

1	DIVSALAR D. Symbol stream combining versus baseband combining for telemetry arraying[R]. The Telecommunications and Data Acquisition Progress Report, 1983: 12-28.
2	DUAN C W , ZHAN Y F , KONG Q . A frequency domain based signal combining method for distributed antenna arraying[J]. China Communications, 2019, 16 (8): 176- 184. doi: 10.23919/JCC.2019.08.015
3	焦义文, 马宏, 刘燕都, 等. 天线组阵频域合成方法最佳子带划分数分析[J]. 系统工程与电子技术, 2020, 42 (10): 2156- 2163. doi: 10.3969/j.issn.1001-506X.2020.10.02
	JIAO Y W , MA H , LIU Y D , et al. Analysis on the optimal sub-band partition number in frequency domain combining for antenna arraying[J]. Systems Engineering and Electronics, 2020, 42 (10): 2156- 2163. doi: 10.3969/j.issn.1001-506X.2020.10.02
4	姚飞, 匡麟玲, 詹亚锋, 等. 深空通信天线组阵关键技术及其发展趋势[J]. 宇航学报, 2010, 31 (10): 2231- 2238. doi: 10.3873/j.issn.1000-1328.2010.10.001
	YAO F , KUANG L L , ZHAN Y F , et al. Key techniques and development trend of antenna arraying for deep space communication[J]. Journal of Astronautics, 2010, 31 (10): 2231- 2238. doi: 10.3873/j.issn.1000-1328.2010.10.001
5	KNAPP C H , CARTER G C . The generalized correlation method for estimation of time delay[J]. IEEE Trans.on Acoustic, Speech and Signal Processing, 1976, 24 (4): 320- 327. doi: 10.1109/TASSP.1976.1162830
6	SHEN C Y , YU H Y . Time-delay alignment technique for a randomly distributed sensor array[J]. IET Communications, 2011, 5 (8): 1068- 1072. doi: 10.1049/iet-com.2010.0671
7	ROGSTAD D H. The sumple algorithm for aligning arrays of receiving radio antennas: coherence achieved with less hardware and lower combining loss[R]. Interplanetary Network Progress Report, 2005: 1-29.
8	SAVAZZI P , GAMBA P . Iterative symbol timing recovery for short burst transmission schemes[J]. IEEE Trans.on Communications, 2008, 56 (10): 1729- 1736. doi: 10.1109/TCOMM.2008.060489
9	ZHANG L , BURR A G . Iterative carrier phase recovery suited to turbo-coded systems[J]. IEEE Trans.on Wireless Communications, 2004, 3 (6): 2267- 2276. doi: 10.1109/TWC.2004.837407
10	ZHOU M D , FENG Z , HUANG X M , et al. Maximum a posteriori probability (MAP) joint fine frequency offset and channel estimation for MIMO systems with channels of arbitrary correlation[J]. IEEE Trans.on Signal Processing, 2021, 69 (1): 4357- 4370.
11	KUMARI S , SRINIVAS K , KUMAR P . Channel and carrier frequency offset equalization for OFDM based UAV communications using deep learning[J]. IEEE Communications Letters, 2021, 25 (3): 850- 853. doi: 10.1109/LCOMM.2020.3036493
12	WAN L , ZHU J , CHENG E , et al. Joint CFO, gridless channel estimation and data detection for underwater acoustic OFDM systems[J]. IEEE Journal of Oceanic Engineering, 2022, 47 (4): 1215- 1230. doi: 10.1109/JOE.2022.3162025
13	YI X M , ZHONG C J . Deep learning for joint channel estimation and signal detection in OFDM systems[J]. IEEE Communications Letters, 2020, 24 (12): 2780- 2784. doi: 10.1109/LCOMM.2020.3014382
14	ZHAO Z Y , VURAN M C , GUO F J , et al. Deep-waveform: a learned OFDM receiver based on deep complex-valued convolutional networks[J]. IEEE Journal on Selected Areas in Communications, 2021, 39 (8): 2407- 2420. doi: 10.1109/JSAC.2021.3087241
15	ZHU Z R , YU H Y , SHEN C Y . Waveform level intelligent multi-task receiver with BiLSTM[J]. IEEE Communications Letters, 2022, 26 (3): 597- 601. doi: 10.1109/LCOMM.2021.3136508
16	ZHU Z R , YU H Y , SHEN C Y . End-to-end waveform level receiver with deep learning[J]. IET Communications, 2022, 16 (11): 1315- 1324. doi: 10.1049/cmu2.12424
17	ZHENG S L , CHEN S C , YANG X N . Deepreceiver: a deep learning-based intelligent receiver for wireless communications in the physical layer[J]. IEEE Trans.on Cognitive Communications and Networking, 2021, 7 (1): 5- 20. doi: 10.1109/TCCN.2020.3018736
18	NARIMAN F , ANDREA G . Neural network detection of data sequences in communication systems[J]. IEEE Trans.on Signal Processing, 2018, 66 (21): 5663- 5678. doi: 10.1109/TSP.2018.2868322
19	CHEN Z Y, HU Y P, SHEN C Y. Joint estimation and detection algorithm for co-channel signals in randomly distributed sensor array[C]//Proc. of the IEEE 20th International Conference on Computer Supported Cooperative Work in Design, 2016: 446-450.
20	SHEN Z X , YU H Y , HU Y P , et al. Joint symbol detection for multi-receiver without signal synchronization and array alignment[J]. IEEE Communications Letters, 2014, 18 (10): 1755- 1758. doi: 10.1109/LCOMM.2014.2352644
21	DMITRIY S , THOMAS B , SANJEEV R K , et al. Fast variational sparse Bayesian learning with automatic relevance determination for superimposed signals[J]. IEEE Trans.on Signal Processing, 2011, 59 (12): 6257- 6261. doi: 10.1109/TSP.2011.2168217
22	BISHOP C M . Pattern recognition and machine learning[M]. New York: Springer, 2006: 113- 120.
23	RAMOS P L , LOUZADA F , RAMOS E . Posterior properties of the Nakagami-m distribution using noninformative priors and applications in reliability[J]. IEEE Trans.on Reliability, 2018, 67 (1): 105- 117. doi: 10.1109/TR.2017.2778139
24	RADEMACHER P, DOROSLOVACKI M. Bayesian learning for classification using a uniform dirichlet prior[C]//Proc. of the IEEE Global Conference on Signal and Information Processing, 2019.
25	SALARI S , CHAN F . Joint CFO and channel estimation in OFDM systems using sparse Bayesian learning[J]. IEEE Communications Letters, 2021, 25 (1): 166- 170. doi: 10.1109/LCOMM.2020.3024817
26	ZHANG K , YU H Y , HU Y P , et al. Iterative decision feedback equalization for SC-FDE systems without fine timing synchronization[J]. IEEE Signal Processing Letters, 2017, 24 (6): 833- 837. doi: 10.1109/LSP.2017.2694889
27	DEMPSTER A . Maximum likelihood from incomplete data via the EM algorithm[J]. Elearn, 1977, 39 (1): 1- 22.
28	BEAL M J. Variational algorithms for approximate Bayesian inference[D]. London: University of London, 2003: 50-85.
29	THEMELIS K E , RONTOGIANNIS A A , KOUTROUMBAS K D . A variational Bayesian framework for sparse adaptive estimation[J]. IEEE Trans.on Signal Processing, 2014, 62 (18): 4723- 4736. doi: 10.1109/TSP.2014.2338839
30	NAKAGAWA T, MATSUI M, KOBAYASHI T, et al. Non-data-aided wide-range frequency offset estimator for QAM optical coherent receivers[C]//Proc. of the Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference, 2011.
31	MASMOUDI A , BELLILI F , AFFES S , et al. A non-data-aided maximum likelihood time delay estimator using importance sampling[J]. IEEE Trans.on Signal Processing, 2011, 59 (10): 4505- 4515. doi: 10.1109/TSP.2011.2161293
32	IJAZ A , AWOSEYILA A B , EVANS B G . Improved SNR estimation for BPSK and QPSK signals[J]. Electronics Letters, 2009, 45 (16): 858- 859. doi: 10.1049/el.2009.1759

计算步骤		运算量	迭代次数
计算$\tilde{a}_n^{i+1}$		$\mathcal{O}$(NML)	—
联合参数联合估计	计算μ_l	$\mathcal{O}$(NL)	I_VB
	计算Σ_l	$\mathcal{O}$(NL)
	计算β_l	$\mathcal{O}$(MNL)
	计算α_l	$\mathcal{O}$(L)
	计算Δf_l	$\mathcal{O}$(NML)

[1]	刘俊, 崔宁, 谢佳昕, 行坤. 基于NSGA-Ⅲ的机载雷达空空射频隐身探测参数设计[J]. 系统工程与电子技术, 2024, 46(1): 97-104.
[2]	胡瑞贤, 张昭, 骆成. 一种分布式卫星系统基线优化设计方法[J]. 系统工程与电子技术, 2023, 45(8): 2423-2437.
[3]	潘子豪, 谢琛, 王桁, 魏以民, 郭道省. 面向物联网无人机通信的短突发CPM信号盲均衡算法[J]. 系统工程与电子技术, 2023, 45(6): 1847-1855.
[4]	李相海, 杨志伟, 贺顺, 廖桂生, 韩超垒, 姜岩. 基于路网信息辅助的多星编队系统SAR-GMTI动目标径向速度估计与重定位方法[J]. 系统工程与电子技术, 2023, 45(3): 629-637.
[5]	汪海波, 黄文华, 巴涛, 邵浩, 姜悦. 一种基于最大似然估计的宽带单脉冲测角改进方法[J]. 系统工程与电子技术, 2023, 45(12): 3845-3851.
[6]	刘业民, 李永祯, 黄大通, 邢世其, 余晓伟. 基于侦察干扰一体化的双干扰机系统对抗SAR-GMTI方法研究[J]. 系统工程与电子技术, 2023, 45(10): 3098-3107.
[7]	祝昇翔, 何岷, 贺志毅, 王嘉欣. 联合最小方差与最大似然谱估计的前视成像方法[J]. 系统工程与电子技术, 2023, 45(10): 3108-3115.
[8]	贾天一, 高婧洁, 申晓红, 刘宏伟. 声速不确定条件下的运动水下航行器自定位[J]. 系统工程与电子技术, 2022, 44(9): 2699-2706.
[9]	纪朋徽, 代大海, 邢世其, 冯德军. 密集虚假运动目标生成方法[J]. 系统工程与电子技术, 2022, 44(5): 1502-1511.
[10]	陈胜, 赵永波, 庞晓娇, 胡毅立, 曹成虎. 米波MIMO雷达波束空间精确最大似然算法[J]. 系统工程与电子技术, 2022, 44(5): 1520-1526.
[11]	范保华, 左乐, 唐勇, 胡泽华. 基于最大期望算法的多时变信号DOA估计方法[J]. 系统工程与电子技术, 2022, 44(2): 420-426.
[12]	刘艺, 周晓雄, 程广俊. 高动态跳频载波跟踪技术[J]. 系统工程与电子技术, 2022, 44(2): 677-683.
[13]	胡耀金, 卞鸿巍, 王荣颖, 马恒. 基于高斯混合模型的光纤罗经误差概率分布建模[J]. 系统工程与电子技术, 2021, 43(6): 1644-1650.
[14]	方安然, 李旦, 张建秋. 异常值和未知观测噪声鲁棒的卡尔曼滤波器[J]. 系统工程与电子技术, 2021, 43(3): 593-602.
[15]	庄陵, 戴蕾, 刘胜珠, 王光宇. 多模索引调制OFDM频谱及能量效率优化方案[J]. 系统工程与电子技术, 2020, 42(3): 719-726.