非高斯环境下的GPS自适应多径抑制技术
收稿日期: 2013-09-29
修回日期: 2014-04-21
网络出版日期: 2014-05-05
基金资助
国家自然科学基金（61304234，61273081）；中央高校基本科研业务费专项资金（HEUCFX041403，HEUCFR1114）
GPS Adaptive Multipath Mitigation Technique in Non-Gaussian Environment
Received date: 2013-09-29
Revised date: 2014-04-21
Online published: 2014-05-05
Supported by
National Natural Science Foundation of China (61304234, 61273081); Fundamental Research Funds for the Central Universities (HEUCFX041403, HEUCFR1114)
为解决非高斯环境下的全球定位系统（GPS）信号多径抑制，尤其是短延迟多径抑制问题，推导出一种具有权值修正更新能力的改进最小平均p范数（LMP）算法。该算法通过对权值变化趋势项的预测可有效减小算法的收敛时间，通过对其参数变化影响的理论分析和仿真，证实在所设实验条件下算法的收敛时间最大可减少50%。为在自适应多径参数估计时应对非高斯噪声环境的影响，同时获得更大的处理增益，设计了改进的自适应多径估计方案，将算法应用于方案的权值更新，并对更新后的权值引入均值滤波，仿真证明所提多径抑制方案在非高斯和高斯环境下均能表现出很好的稳定性和有效性。
丁继成 , 黄卫权 , 王野 . 非高斯环境下的GPS自适应多径抑制技术[J]. 航空学报, 2014 , 35(8) : 2234 -2242 . DOI: 10.7527/S1000-6893.2014.0066
In order to mitigate global positioning system (GPS) signal multipath in non-Gaussian environment, especially for short multipath, the paper derives a modified least mean p-norm (LMP) algorithm. It can effectively reduce the convergence time through predicting the update trend of weights. By theoretically analyzing and simulating the parameters changing effect of the modified algorithm, its convergence speed advantage is verified. The convergence time can be shortened by 50%. In the non-Gaussian environment, in order to achieve multipath parameter estimation and obtain greater processing gain, an improved multipath mitigation scheme is designed. The improved algorithm mentioned previously is applied to the adaptive weight update, and a mean filtering algorithm for updated weights. Simulation verifies that the proposed multipath mitigation method has shown good stability and effectiveness in the non-Gaussian and Gaussian environments.
[1] Yedukondalu K, Sarma A D, Srinivas V S. Estimation and mitigation of GPS multipath interference using adaptive filtering[J]. Progress in Electromagnetics Research M, 2011, 21: 133-148.
[2] Liu L, Amin M G. Performance analysis of GPS receivers in non-Gaussian noise incorporating precorrelation filter and sampling rate[J]. IEEE Transactions on Signal Processing, 2008, 56(3): 990-1004.
[3] Petovello M. GNSS solutions: Gaussian vs non-Gaussian noise in inertial/GNSS integration[J]. Inside GNSS Magazine, 2012(November/December): 32-39.
[4] Sleewaegen J M, Boon F. Mitigating short-delay multipath: a promising new technique//Proceedings of ION GPS, 2001: 11-14.
[5] Rlinami M, Morikawa H, Aoyama T. An adaptive multipath mitigation technique for GPS signal reception//2000 IEEE 51st Vehicular Technology Conference Proceedings, 2000, 2: 1625-1629.
[6] Chang C L, Juang J C. An adaptive multipath mitigation filter for GNSS applications[J]. EURASIP Journal on Advances in Signal Processing, 2008, 2008: 1-10.
[7] Mao W L, Du J S, Sheen J, et al. Adaptive multipath mitigation tracking system for GPS receiver[J]. Aerospace Science and Technology, 2013, 30(1): 66-78.
[8] Wang J A, Liu P J, Li S S. An adaptive genetic algorithm for complex close-in Galileo BOC(1,1) multipath mitigation[J]. Journal of Central South University: Science and Technology, 2012, 43(12): 4757-4763. (in Chinese) 王江安, 刘鹏举, 李珊珊. 自适应遗传算法抑制复杂短时延Galileo BOC(1,1)多径[J]. 中南大学学报: 自然科学版, 2012, 43(12): 4757-4763.
[9] Yao Y X, Yang D K, Zhang Q S. GPS multipath signal estimation method utilizing adaptive filtering[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(10): 2004-2009. (in Chinese) 姚彦鑫, 杨东凯, 张其善. GPS多径信号的自适应滤波估计方法[J]. 航空学报, 2010, 31(10): 2004-2009.
[10] Benesty J, Rey H, Vega L R, et al. A nonparametric VSS NLMS algorithm[J]. IEEE Signal Processing Letters, 2006, 13(10): 581-584.
[11] Vega L R, Rey H, Benesty J, et al. A new robust variable step-size NLMS algorithm[J]. IEEE Transactions on Signal Processing, 2008, 56(5): 1878-1893.
[12] Zheng Y R, Shao T. A variable step-size lmp algorithm for heavy-tailed interference suppression in phased array radar//2009 IEEE Aerospace Conference, 2009: 1-6.
[13] Shin H C, Sayed A H, Song W J. Variable step-size NLMS and affine projection algorithms[J]. IEEE Signal Processing Letters, 2004, 11(2): 132-135.
[14] Shao M, Nikias C L. Signal processing with fractional lower order moments: stable processes and their applications[J]. Proceedings of the IEEE, 1993, 81(7): 986-1010.
[15] Masry E, Bullo F. Convergence analysis of the sign algorithm for adaptive filtering[J]. IEEE Transactions on Information Theory, 1995, 41(2): 489-495.
[16] Qiu T S, Yang Z C, Li X B, et al. A weighted average least p-norm algorithm under alpha stable noise conditions[J]. Journal of Electronics & Information Technology, 2007, 29(2): 410-413. (in Chinese) 邱天爽, 杨志春, 李小兵, 等. α稳定分布下的加权平均最小p-范数算法[J]. 电子与信息学报, 2007, 29(2): 410-413.
[17] Guo Y, Qiu T S, Zhang Y L, et al. Novel adaptive time delay estimation method based on the fractional lower order cyclic correlation in impulsive noise environment[J]. Journal on Communications, 2007, 28(3): 8-14. (in Chinese) 郭莹, 邱天爽, 张艳丽, 等. 脉冲噪声环境下基于分数低阶循环相关的自适应时延估计方法[J]. 通信学报, 2007, 28(3): 8-14.
[18] Zheng Y R, Chen G, Blasch E. A normalized fractionally lower-order moment algorithm for space-time adaptive processing//IEEE Military Communications Conference, 2007: 1-6.
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