基于带限信号压缩的高效软件无线电卫星双向时间比对

程龙; 董绍武; 武文俊; 弓剑军; 王威雄; 高喆

doi:10.11999/JEIT250705

基于带限信号压缩的高效软件无线电卫星双向时间比对

doi: 10.11999/JEIT250705 cstr: 32379.14.JEIT250705

程龙^{1, 2},
董绍武^{1, 3, 4, ,},
武文俊^{1, 3, 4},
弓剑军^{1, 3},
王威雄^{1, 3},
高喆^{1, 3}

1.
中国科学院国家授时中心西安 710600
2.
中国科学院大学北京 100049
3.
中国科学院时间基准及应用重点实验室西安 710600
4.
中国科学院大学天文与空间科学学院北京 100049

详细信息

作者简介:
程龙：男，博士生，研究方向为高精度时间频率传递

董绍武：男，研究员，研究方向为守时技术、卫星导航系统时间

武文俊：男，研究员，研究方向为时间与频率、卫星导航

弓剑军：男，工程师，研究方向为守时技术

王威雄：男，助理研究员，研究方向为守时技术、高精度时间频率传递

高喆：女，助理研究员，研究方向为高精度时间频率传递

通讯作者:
董绍武　sdong@ntsc.ac.cn

中图分类号: TN927
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- 被引次数: 0
出版历程
- 修回日期: 2025-12-06
- 录用日期: 2025-12-06
- 网络出版日期: 2025-12-15

Band-Limited Signal Compression Enabled Computationally Efficient Software-Defined Radio for Two-Way Satellite Time and Frequency Transfer

CHENG Long^{1, 2},
DONG Shaowu^{1, 3, 4
, ,},
WU Wenjun^{1, 3, 4},
GONG Jianjun^{1, 3},
WANG Weixiong^{1, 3},
GAO Zhe^{1, 3}

1.
National Time Service Center, Chinese Academy of Sciences, Xi’an 710600, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Key Laboratory of Time Reference and Applications, Chinese Academy of Sciences, Xi’an 710600, China
4.
School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China

摘要

摘要: 卫星双向时间比对(TWSTFT)技术因其高精度特性在时间同步领域具有重要应用价值，其中实时性是衡量系统性能的关键指标。传统硬件实现的TWSTFT存在显著的周日效应问题，而基于开环架构和高分辨率多相关器的软件定义无线电(SDR)实现方法虽能有效抑制该效应，却因计算复杂度高而面临实时性挑战。为提升SDR接收机的运算效率并改善其短期稳定性，本研究在传统信号压缩法基础上提出了一种基于带限信号压缩的高效SDR实现方法。该方法创新性地采用伪随机噪声(PRN)码整数倍抽取序列与接收信号进行相关运算以获得压缩值，并通过建立抽取序列采样频率与信号带宽的定量关系实现相关结果的高效重构。该机制通过消除传统算法的冗余计算环节，在保证测量精度的同时显著提升了运算效率并降低了系统资源开销。为验证方法有效性，该文设计了不同带宽和基线长度的对比实验，结果表明：相较于TWSTFT SDR中常用的多相关器法，本方法的运算速度提升7-8倍，资源消耗量降低了85～90%，且信号预处理阶段的滤波操作能有效抑制带外噪声干扰。这种效率提升不仅增加了单位拟合周期内的有效测量数据量，通过统计平均效应降低了随机噪声影响，还显著提高了比对结果的短期稳定性，为高精度时间比对提供了新的技术途径。
- 卫星双向时间比对 /
- 软件定义无线电 /
- 信号压缩 /
- 多相关器 /
- 伪随机噪声码
Abstract: Objective This study addresses the critical challenges in Two-Way Satellite Time and Frequency Transfer (TWSTFT) systems, particularly focusing on the computational inefficiency and excessive resource consumption of Software-Defined Radio (SDR) receivers. Despite TWSTFT's reputation for exceptional long-term stability and precision in time transfer, traditional hardware-based implementations suffer from significant diurnal effects. Current mitigation strategies, such as data fusion with GPS Precise Point Positioning (PPP), are limited by their reliance on auxiliary link performance and lack of standardized algorithms across international networks. While SDR receivers have proven effective in reducing diurnal effects and improving accuracy, their high sampling rates and complex multi-correlator algorithms impose prohibitive computational demands, hindering real-time multi-station operations. To overcome these limitations, this research introduces an innovative band-limited signal compression method specifically designed for TWSTFT signals. The primary objective is to develop a solution that maintains high measurement resolution while dramatically enhancing computational efficiency, thereby enabling scalable and high-performance time transfer across global timing laboratories. This work holds significant potential to advance TWSTFT technology by addressing current bottlenecks and facilitating more robust international time and frequency transfer. Methods The proposed band-limited signal compression method adapts traditional signal compression techniques to TWSTFT by addressing the distortion of pseudo-random noise (PRN) code square-wave characteristics under bandwidth constraints. The method employs the following key technical approach: First, bandwidth-matched filtering is applied to the local PRN code replica to precisely align its spectral characteristics with the effective bandwidth of the received signal, thereby effectively suppressing out-of-band noise interference. For received signals with different bandwidths, the system employs n groups (e.g., n=1, 2, or 20) of phase-diversified, equally-spaced PRN code subsequences. The number of subsequence groups n is strictly determined by the constraint n×R_chip≥ 2×Band_signal, where R_chip denotes the sampling rate of the subsequences and Band_signal represents the signal bandwidth. During signal processing, the received signal - after being bandpass-filtered to suppress out-of-band noise - undergoes parallel correlation operations with these phase-diversified PRN code subsequences. The complete correlation function is reconstructed through a linear combination of the n independent correlation results, where each result is scaled by the normalization factor N_chip/n, with N_chip defined as the number of samples per PRN chip. This integrated approach, combined with dynamic optimization through adaptive sampling rate adjustment and efficient resource allocation algorithms, achieves optimal performance while maintaining computational efficiency. Results and Discussions The experimental validation was conducted on an advanced TWSTFT platform at the National Time Service Center (NTSC), utilizing data from TWSTFT links (NTSC-NIM, NTSC-SU, NTSC-PTB) and SATRE local-loop tests. Measurement data from MJD 60742 to MJD 60749 were collected following ITU-R TF.1153.4 standards. Results demonstrate significant improvements in both precision and computational efficiency. In local-loop tests, the method achieved the most stable Time of Arrival (TOA) measurements while maintaining high signal-to-noise ratio (Table 2), with time deviation (TDEV) outperforming traditional multi-correlator and conventional compression methods across all averaging periods (Fig. 9). For TWSTFT links, the proposed method demonstrated superior short-term stability in comparisons across different baseline lengths (Fig. 10 and Fig. 11). Configurations with PRN subsequence numbers n=1 and n=2 showed remarkable efficiency improvements, increasing processing speed by 795% and 707% respectively while reducing GPU memory usage by 89.77% and 84.65% (Table 4). The method supports up to 102 concurrent channels (n=1), significantly exceeding the 11-channel capacity of conventional techniques (Table 5). Analysis confirms the optimal balance between performance and efficiency, as increasing the number of PRN subsequences (n) provides no additional precision benefits but increases resource consumption. These advancements are attributed to the parallel processing of short correlations and bandwidth-aware sampling while maintaining measurement accuracy. Conclusions This study presents an innovative band-limited signal compression method designed to overcome the computational limitations of traditional TWSTFT systems. The proposed approach integrates parallel short-correlation processing with adaptive bandwidth-aware sampling, achieving significant improvements in both precision and efficiency. Experimental validation confirms the method's superior performance, demonstrating enhanced short-term stability across various signal bandwidths and baseline lengths compared to conventional multi-correlator techniques. Notably, the solution achieves remarkable computational efficiency, with processing speeds increased by 795% (n=1) and 707% (n=2) while reducing GPU memory requirements by 89.77% and 84.65% respectively. The system’s scalability is substantially improved, supporting up to 102 concurrent channels - a nine-fold increase over traditional implementations. These advancements validate the method's optimized balance between performance and resource utilization. Future research directions include adaptation to more complex operational scenarios to further enhance the technology's applicability in global time and frequency transfer networks.
- two-way satellite time and frequency transfer (TWSTFT) /
- software-defined radio (SDR) /
- signal compression /
- multi-correlator /
- pseudo-random noise (PRN) code

HTML全文

图 1 TWSTFT SDR比对原理。其中红色箭头为站点1的上行链路，蓝色箭头为站点2的上行链路。

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图 2 信号压缩中的本地PRN码副本

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图 3 传统信号压缩法相关结果计算原理

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图 4 接收信号与本地PRN码副本频谱

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图 5 不同滤波带宽时本地PRN码副本的码片形状

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图 6 各起始相位ε_i,k与接收信号的互相关压缩结果。(a) 滤波前的接收信号；(b) 滤波后的接收信号。

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图 7 ε_i,k等效采样率等于信号带宽时(n=2)相关值的计算过程。 (上)混叠导致的失真压缩信号，其中上半部分为i=0,1,2,···,N_chip/2-1时的压缩信号，下半部分为i=N_chip/2,N_chip/2+1,···,N_chip-1时的压缩信号；(下)互补信号累加后的重构中间结果。

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图 8 实验平台接收设备

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图 9 本地回路中传统信号压缩法、多相关器法以及带限信号压缩法的时间偏差

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图 10 NTSC-NIM链路卫星双向比对结果及时间偏差

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图 11 NTSC-SU和NTSC-PTB链路的时间偏差

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表 1 实验参数配置表

接收机通道	有效信号带宽	比对方法	压缩信号组数n	动态范围(码片)	本地PRN码副本滤波带宽	接收信号是否滤波	接收信号滤波带宽/ 中心频率
R0	1.25 MHz	多相关器法	-	±0.7	1.25 MHz	否	-
R0	1.25 MHz	传统信号压缩法	-	±1.0	-	否	-
R0	1.25 MHz	带限信号压缩法	20, 2, 1	±1.0	1.25 MHz	是	1.25 MHz / 70 MHz
R1	2.5 MHz	多相关器法	-	±0.7	2.5 MHz	否	-
R1	2.5 MHz	传统信号压缩法	-	±1.0	-	否	-
R1	2.5 MHz	带限信号压缩法	20, 2	±1.0	2.5 MHz	是	2.5 MHz / 70.02 MHz

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表 2 本地回路中到达时间以及信噪比的统计

方法	到达时间		信噪比
方法	均值(ns)	标准差(ps)	均值(dB)	标准差(dB)
传统信号压缩	1453.37	162.56	9.702	0.0186
多相关器	1441.45	22.57	14.308	0.0229
带限信号压缩(n=20)	1441.44	21.98	14.274	0.0309
带限信号压缩(n=2)	1441.45	22.03	14.276	0.0313

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表 3 NTSC-NIM、NTSC-SU和NTSC-PTB链路中传统信号压缩法、多相关器法和带限信号压缩法单向结果统计(ps)

方法	NTSC-NIM	NTSC-SU	NTSC-PTB
传统信号压缩	14.57	10.66	8.40
多相关器	13.56	10.41	7.98
带限信号压缩(n=20)	12.84	9.89	7.36
带限信号压缩(n=2)	12.84	9.91	7.33
带限信号压缩(n=1)	12.82	9.90	7.36

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表 4 SATRE本地回路和TWSTFT链路中多相关器法和带限信号压缩法的时间以及资源消耗统计情况

方法	SATRE本地回路 (ms)	NTSC-NIM (ms)	NTSC-SU (ms)	NTSC-PTB (ms)	平均时耗 (ms)	加速比	资源占用 (MiB)
多相关器	81.99	80.54	81.90	85.32	82.43	1	430
带限信号压缩(n=20)	32.94	28.21	32.82	27.47	30.36	2.72	410
带限信号压缩(n=2)	8.21	8.65	8.07	15.95	10.22	8.07	66
带限信号压缩(n=1)	-	14.91	6.37	6.32	9.20	8.95	44

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表 5 多相关器法和带限信号压缩法所支持的最大比对通道数的统计结果

方法		时间限制比对通道数	GPU内存限制比对通道数	最大比对通道数
信号未经滤波操作	多相关器	11.8	16.3	11
信号未经滤波操作	带限信号压缩(n=20)	31.9	17.1	17
信号经过滤波操作	带限信号压缩(n=20)	30.9	15.3	15
	带限信号压缩(n=2)	91.9	95.3	91
	带限信号压缩(n=1)	102.2	143.0	102

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