电子测量与仪器学报

吴涵,行鸿彦,李梦杰,杭陈雨.基于AWE-NRBO-BiLSTM的海面微弱目标检测[J].电子测量与仪器学报,2025,39(6):184-194

基于AWE-NRBO-BiLSTM的海面微弱目标检测

Weak target detection based on AWE-NRBO-BiLSTMin sea clutter background

DOI：

英文关键词:sea clutter week signal detection BiLSTM Newton-Raphson-based optimizer

基金项目:国家自然科学基金（62171228）项目资助

作者	单位
吴涵	1.南京信息工程大学电子与信息工程学院南京210044；2.南通理工学院电气与能源工程学院南通226001
行鸿彦	1.南京信息工程大学电子与信息工程学院南京210044；2.南通理工学院电气与能源工程学院南通226001
李梦杰	1.南京信息工程大学电子与信息工程学院南京210044；2.南通理工学院电气与能源工程学院南通226001
杭陈雨	1.南京信息工程大学电子与信息工程学院南京210044；2.南通理工学院电气与能源工程学院南通226001

Author	Institution
Wu Han	1.School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.School of Electrical and Energy Engineering, Nantong Institute of Technology, Nantong 226001, China
Xing Hongyan	1.School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.School of Electrical and Energy Engineering, Nantong Institute of Technology, Nantong 226001, China
Li Mengjie	1.School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.School of Electrical and Energy Engineering, Nantong Institute of Technology, Nantong 226001, China
Hang Chenyu	1.School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.School of Electrical and Energy Engineering, Nantong Institute of Technology, Nantong 226001, China

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中文摘要:

针对强海杂波背景下传统方法难以检测海面微弱目标信号的问题，研究了混沌相空间重构理论和改进牛顿-拉夫逊优化算法(NRBO)，提出了一种基于优化双向长短时记忆网络（BiLSTM）的混沌背景下微弱信号检测方法。将重构的相空间信号作为BiLSTM网络的输入，通过嵌入维度和延迟时间确定训练数据的长度，利用改进牛顿-拉夫逊优化算法优化BiLSTM模型的参数，使用自适应加权误差（AWE）损失函数训练模型，提高模型预测精度与运行速度，降低目标检测门限，结合BiLSTM模型进行单步预测，根据预测误差从强混沌背景噪声下检测微弱目标信号。以Lorenz混沌系统作为混沌背景设计仿真实验，对叠加的微弱信号进行检测，结果表明所提方法能有效检测微弱信号。使用IPIX实测数据和烟台对海探测数据进行预测实验，进一步证明了其有效性。

英文摘要:

To address the challenge of detecting weak target signals on the ocean surface under strong sea clutter backgrounds, this study investigates the theory of chaotic phase space reconstruction and the improved Newton-Raphson optimization algorithm. A novel method for weak signal detection in chaotic backgrounds is proposed, based on an optimized bidirectional long short-term memory network (BiLSTM). The reconstructed phase space signal is used as the input to the BiLSTM network, with the length of the training data determined by the embedding dimension and delay time. The parameters of the BiLSTM model are optimized using the improved Newton-Raphson optimization algorithm, and the model is trained with an adaptive weighted error (AWE) loss function. Both approaches work together to enhance prediction accuracy, improve runtime speed, and reduce the detection threshold. A single-step prediction is performed using the BiLSTM model, and weak target signals are detected from strong chaotic background noise by analyzing the prediction errors. Simulation experiments were conducted using the Lorenz chaotic system as the chaotic background to detect superimposed weak signals. The results demonstrate that the proposed method effectively detects weak signals. Further validation was carried out using the IPIX radar dataset and sea surface detection data from Yantai, confirming the method’s robustness and effectiveness.

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