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南京理工大学电子工程与光电技术学院,江苏南京 210094
Received:30 December 2025,
Accepted:09 February 2026,
Published:25 February 2026
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李诗琴, 赵兆, 许志勇. 面向户外多声源增强的鲁棒节点特定分布式广义旁瓣对消[J]. 电子学报, 2026, 54(02): 578-588.
LI Shiqin, ZHAO Zhao, XU Zhiyong. Robust Node-Specific Distributed Generalized Sidelobe Canceler for Outdoor Multi-Source Enhancement[J]. Acta Electronica Sinica, 2026, 54(02): 578-588.
李诗琴, 赵兆, 许志勇. 面向户外多声源增强的鲁棒节点特定分布式广义旁瓣对消[J]. 电子学报, 2026, 54(02): 578-588. DOI:10.12263/DZXB.20251058
LI Shiqin, ZHAO Zhao, XU Zhiyong. Robust Node-Specific Distributed Generalized Sidelobe Canceler for Outdoor Multi-Source Enhancement[J]. Acta Electronica Sinica, 2026, 54(02): 578-588. DOI:10.12263/DZXB.20251058
随着自组织网络技术的快速发展,基于多个麦克风阵列节点的无线声传感网(Wireless Acoustic Sensor Networks,WASNs)已成为户外开放空间中实现持续监测的重要技术手段。针对户外分布式声学监测中的多声源同时增强需求,现有节点特定分布式广义旁瓣对消(Node-Specific Distributed Generalized Sidelobe Canceler,NS-DGSC)算法具有低通信开销、低先验知识需求、低目标失真三大优势。然而,实际户外部署的WASN通常会面临阵列节点数多于目标声源数的场景,并且在期望目标中还包含信号持续时间较长的非间歇性声源(如无人机、履带车辆),NS-DGSC在此类节点数量冗余且非间歇性声源并发的条件下存在目标信号自消、性能恶化等局限性。为解决此问题,本文提出了一种鲁棒节点特定分布式广义旁瓣对消(Robust Node-Specific Distributed Generalized Sidelobe Canceler,RNS-DGSC)算法。该算法的各节点在将初级广义旁瓣对消(Generalized Sidelobe Canceler,GSC)增强后的本地目标声源压缩信号广播至网络之后,不再将所有非本地压缩信号无差别纳入次级GSC辅助通道,而是通过引入基于最小均方误差准则的相关性检测模块,对来自网络的目标主导与干扰主导两类压缩信号进行自适应判决与区分,从而规避节点冗余导致的信号融合架构冲突;再通过设计双策略时延对齐模块,对目标主导与干扰主导两类压缩信号实施差异化时延补偿,从而保障后续次级GSC中目标信号相干增强且算法快速收敛。所有时延对齐后的目标主导压缩信号与本地初级GSC主通道输出一起融合完成多节点目标信号累加增强,作为次级GSC的主通道,而所有时延对齐后的干扰主导压缩信号则一起构建次级GSC的辅助通道,进而得到各节点最终的网络融合增强输出。实验结果表明,在节点数量冗余且多个非间歇性声源并发场景下,所提RNS-DGSC不仅继承了NS-DGSC的优势,还能实现优越的多声源增强性能。具体而言,在不同网络规模与输入信干噪比(Signal-to-Interference-plus-Noise Ratio,SINR)条件下,RNS-DGSC可达到与集中式处理相当的SINR增益;同时,相较于现有方法,所提算法在信号失真比方面的改善超过50%,并对导向矢量估计误差表现出更强的鲁棒性。因此,该算法可为复杂开放空间中的持续声学监测提供一种通信高效、性能可靠的方案。
With the rapid advances in ad-hoc network technique
wireless acoustic sensor networks (WASNs) with multiple microphone array nodes have emerged as a key technology for continuous monitoring in outdoor environments. For the task of simultaneous multi-source enhancement in such distributed systems
existing node-specific distributed generalized sidelobe canceler (NS-DGSC) possesses notable advantages including low communication overhead
low prior knowledge requirements
and low target distortion. However
practical outdoor WASNs often encounter node-redundant scenarios where nodes outnumber target sources. Moreover
corresponding targets of interest may include non-intermittent signals (e.g.
drones and tracked vehicles). Under such scenarios
the NS-DGSC suffers from target self-cancellation and severe performance degradation will arise. To address this issue
this paper proposes a robust node-specific distributed generalized sidelobe canceler (RNS-DGSC). First
microphone signals at each node are pre-filtered by a local generalized sidelobe canceler (GSC) to produce preliminary enhancement for individual desired sources as the compressed signal. Then
for each node
compressed signals exchanged from other nodes are adaptively distinguished into target-dominant and interference-dominant categories by introducing a correlation check module based on minimum mean square error criterion
which can mitigate the node redundancy-induced fusion conflicts existing in the NS-DGSC. Afterwards
a temporal alignment module is designed at each node to address time delay compensation for these two categories of compressed signals using two different strategies
which enhances fusion quality of the desired signals and accelerates convergence in the subsequent secondary GSC. Finally
a secondary GSC is performed at each node
where all temporally aligned target-dominant compressed signals are integrated into the primary branch and the aligned interference-dominant components constitute the auxiliary branch. Experimental results reveal that in node-redundant scenarios with multiple concurrent non-intermittent sources
the proposed RNS-DGSC not only retains the benefits of the NS-DGSC
but also delivers superior multi-source enhancement performance. Specifically
the RNS-DGSC achieves signal-to-interference-plus-noise ratio (SINR) improvement comparable to that of the centralized scheme across various network scales and SINR input conditions. Meanwhile
our algorithm presents over 50% improvement in signal-to-distortion ratio and exhibits superior robustness to steering vector estimation errors in comparison with existing methods. The RNS-DGSC thus provides a communication-efficient and reliable solution for continuous acoustic monitoring in complex open spaces.
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