一种基于LMS自适应滤波的互相关时延估计优化算法

doi:10.16180/j.cnki.issn1007-7820.2020.06.006

摘要/Abstract

摘要：

在多站时差定位系统中使用基于LMS自适应滤波的互相关法进行时延估计时,若采用固定步长因子则会在收敛速度和稳态失调之间存在较大矛盾,从而影响时延估计精度。针对这一问题,文中提出了一种基于分段变步长LMS自适应滤波和希尔伯特差值的互相关时延估计优化算法。该方法首先采用分段变步长LMS自适应滤波对信号进行滤波处理,然后将滤波后的信号作互相关运算,最后通过希尔伯特差值法锐化相关函数的峰值,进一步提高时延估计精度。在相同条件下,文中模拟分析了不同算法的时延估计精度。实验结果表明,新的优化算法时延估计精度更高。在不同信噪比下,新方法相较传统时延估计方法精度提高了2.2%以上,具有良好的抗噪声性能。

关键词: 时差定位, 自适应滤波, 互相关, 变步长LMS, 时延估计, 希尔伯特差值

Abstract:

In multi-station time difference positioning system, when using the cross-correlation method based on LMS adaptive filtering for time delay estimation, the fixed step factor has a large contradiction between convergence speed and steady-state offset, which affects the accuracy of delay estimation. Aiming at this problem, a cross-correlation time delay estimation optimization algorithm based on piecewise variable step size LMS adaptive filtering and Hilbert difference was proposed in this study. Firstly, the piecewise variable step size LMS adaptive filtering was used to filter the signal, then the filtered signal was cross-correlated, and finally the peak of the correlation function was sharpened by the Hilbert difference method to obtain higher delay estimation accuracy. Under the same conditions, the time delay estimation accuracy of different algorithms was simulated and analyzed. The results showed that the proposed optimization algorithm had higher delay estimation accuracy, and the prediction accuracy could be improved by 2.2% compared with the traditional method under different SNR, indicating proposed algorithm presented good anti-noise performance.

Key words: TDOA location, adaptive filtering, CC, VSS-LMS, time delay estimation, Hilbert difference

中图分类号:

TN95

魏文亮,茅玉龙. 一种基于LMS自适应滤波的互相关时延估计优化算法[J]. 电子科技, 2020, 33(6): 29-34.

WEI Wenliang,MAO Yulong. Cross-correlation Time Delay Estimation Optimization Algorithm Based on LMS Adaptive Filtering[J]. Electronic Science and Technology, 2020, 33(6): 29-34.

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SNR	算法1	算法2	算法3
20 dB	1.035×10^-6 s	1.030×10^-6 s	1.015×10^-6 s
10 dB	1.040×10^-6 s	1.025×10^-6 s	1.020×10^-6 s
0 dB	1.060×10^-6 s	1.030×10^-6 s	1.025×10^-6 s
-5 dB	-	1.085×10^-6 s	1.040×10^-6 s

时延估计方法	真实时延	估计均值	估计均值标准差	估计误差
算法1	1	1.034	0.034	3.4%
算法2	1	1.030	0.030	3.0%
算法3	1	1.012	0.012	1.2%

时延估计方法	真实时延	估计均值	估计均值标准差	估计误差
算法1	1	1.037 75	0.037 75	3.775%
算法2	1	1.031 00	0.031 00	3.100%
算法3	1	1.013 25	0.013 25	1.325%

时延估计方法	真实时延	估计均值	估计均值标准差	估计误差
算法1	1	1.053 00	0.053 00	5.300%
算法2	1	1.033 25	0.033 25	3.325%
算法3	1	1.020 75	0.020 75	2.075%

时延估计方法	真实时延	估计均值	估计均值标准差	估计误差
算法1	1	-	-	-
算法2	1	1.061 25	0.061 25	6.125%
算法3	1	1.031 00	0.031 00	3.100%