一个基于修改的Versoria函数的新型VSS-LMS算法,用于防止干扰
Binghe Tian1, Yongxin Feng1, Fang Liu1
1Key Laboratory of Information Network and Information Countermeasure Technology of Liaoning Province, Shenyang Ligong University, Shenyang 110159, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了一种用于传感器系统的新型变量步骤大小最小平均平方 (VSS-LMS) 算法. 新的VSS-LMS算法通过平衡收率和稳定状态误差来提高弱信号检测的准确性.
科学领域:
- 信号处理 信号处理
- 适应性过是一种自适应性过.
- 机器学习 机器学习
背景情况:
- 精确的弱信号检测在传感器阵列系统中至关重要.
- 传统的固定步骤算法在平衡收率 (CR) 和低稳定状态误差 (SSE) 方面存在局限性.
研究的目的:
- 提出一种新的可变步骤大小最小平均平方 (VSS-LMS) 算法,以克服CR-SSE权衡.
- 为了提高传感器阵列系统中弱信号检测的准确性和性能.
主要方法:
- 开发了一种VSS-LMS算法,利用修改后的versoria函数来改善曲率特征.
- 实现非线性映射,以便在错误统计和步骤大小因子之间进行动态合.
- 使用衍生闭环方程构建了一个适应性反系统,用于实时最佳步骤大小的生成.
主要成果:
- 拟议的算法与现有的VSS-LMS方法相比,证明了加速的融合.
- 保持低稳定状态误差 (SSE),同时实现更快的趋同.
- 在低信号噪声比 (SNR) 条件下,在各种干扰的情况下,展示了强大的信号恢复.
结论:
- 这种新的VSS-LMS算法有效地平衡了收率和稳定状态误差.
- 在弱信号检测方面提供卓越的性能,特别是在具有挑战性的低SNR环境中.
- 为需要高精度的传感器阵列信号接收系统提供了强大的解决方案.
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