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J4  2011, Vol. 45 Issue (3): 515-519    DOI: 10.3785/j.issn.1008-973X.2011.03.019
电气工程     
声纳强脉冲干扰的自适应抵消方法
刘辉涛1,汪李明1,李建龙2
1.杭州应用声学研究所,浙江 杭州 310012; 2.浙江大学 信息与通信工程研究所,浙江 杭州 310027
Approach to adaptive cancellation of strong interference pulse in sonar
LIU Hui-tao1, WANG Li-ming1, LI Jian-long2
1.Hangzhou Research Institute of Applied Acoustics, Hangzhou 310012, China;
2. Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China
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摘要:

为了解决声纳实际使用中强脉冲干扰影响目标检测和跟踪的问题,提出一种在阵元域内实现的自适应干扰抵消方法.该方法基于最小均方(LMS)误差准则的自适应滤波来实现,引入频域批处理方法大大降低了运算量,采用可变步长的自适应叠代方法实现了对强脉冲的快速抑制.提出一种自适应干扰抵消参考信号的提取方法,同时在自适应算法中施加梯度约束计算,从而实现强干扰背景下的目标稳定跟踪.经多次水上试验表明:该方法能够不损失目标信息对强脉冲干扰进行有效抑制,达到检测被强干扰掩盖的目标信号的目的,同时也能实现强干扰背景下的目标稳定跟踪.

Abstract:

For sonar detection and tracking in the presence of strong interference pulses, an adaptive interference cancellation method in the element domain was proposed, which is based on least mean square (LMS) adaptive filtering. The work reduces the computation burden by frequency domain batch processing, and achieves fast suppression of strong pulse via varied step adaptive iteration. An adaptive interference cancellation method for reference signal extraction was presented, and constrained gradient computation in the adaptive algorithm was implemented. Therefore, stable target tracking in strong interference environments can be accomplished. Experiments during sea trial indicated that strong interference pulses can be suppressed effectively using this method without any loss of target information. Stable target tracking in such environments are accomplished while the signal corrupted by strong interference pulses is detected.

出版日期: 2012-03-16
:  TB 566  
作者简介: 刘辉涛(1968-),女,浙江临海人,高级工程师,从事声纳总体和信号处理等研究.E-mail:liuht501@sina.com
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引用本文:

刘辉涛,汪李明,李建龙. 声纳强脉冲干扰的自适应抵消方法[J]. J4, 2011, 45(3): 515-519.

LIU Hui-tao, WANG Li-ming, LI Jian-long. Approach to adaptive cancellation of strong interference pulse in sonar. J4, 2011, 45(3): 515-519.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2011.03.019        http://www.zjujournals.com/eng/CN/Y2011/V45/I3/515

[1] FAN H. Analysis of a frequencydomain adaptive IIR [J]. IEEE Transactions on Acoustics, Speech, and Filter Signal Processing, 1990, 38(5): 864-870.

[2] WEI Y B, GELFAND S B. Noiseconstrained least mean squares algorithm [J]. IEE Transactions on Signal Processing, 2001,49(9): 1961-1970.
[3] VALIN J M,COLLINGS I B.  Interference normalized least mean square algorithm [J]. IEEE Signal Processing Letters, 2007,14(12): 988-991.
[4] RAFAELY B. A computationally efficient frequencydomain LMS algorithm with constrains on the adaptive filter [J]. IEE Transactions on Signal Processing, 2000, 48(6): 1649-1655.
[5] GHOGHO M. Analytic hehavior of the LMS adaptive line enhancer for sinusoids crrupted by multiplicative and additive noise [J]. IEEE Transactions on Signal Processing, 1998, 46(9): 2386-2393.
[6] YANG Ho. Subarray design for adapitive interference cancellation [J]. IEEE Transactions on Antennas and Propagation, 2002, 50(10): 1453-1459.
[7] OGUNFUNMI A O. On the implementation of the frequencydomain LMS adaptive filter [J]. IEEE Transactions on Circuits and Systems, 1992, 39(5B): 318-322.
[8] WIDROW B, KAMENETSKY M. Statistical efficiency of adaptive algorithms [J]. Neural Networks, 2003, 16(5): 735-744.
[9] ANG W P. A new class of gradient adaptive stepsize LMS algorithms [J]. IEEE Transactions on Signal Processing, 2001, 49(4): 805-810.

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