一种有效的无源雷达稀疏表示方法

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-06-02 DOI:10.1109/JSEN.2025.3573568

Quande Sun;Yuan Feng;Tao Shan;Juan Zhao;Xia Bai;Tianrun Wang;Zhi Wang

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引用次数: 0

摘要

无源雷达（PR）通常通过计算交叉模糊函数（CAF）来估计目标参数，这种方法容易产生较宽的主瓣和较高的副瓣，从而导致弱目标被遮挡和相邻目标难以区分等问题。针对上述问题，提出了一种基于稀疏表示（SR）的PR参数估计方法。首先，提出了一种基于信号分割和傅里叶变换的SR模型，利用快速傅里叶变换解决了字典矩阵过大的问题。然后，提出了一种基于检测阈值的正交匹配追踪（OMP）算法（DT-OMP），根据预设阈值自适应确定要选择的原子数。在此基础上，提出了一种模型失配校正方法（MMC-SR），以实现离网情况下目标参数的准确估计。仿真和实际实验表明，该方法可以有效地缓解CAF的宽主瓣和高副瓣的影响，从而提高分辨率并提供目标参数的精细化估计，具有重要的实际应用价值。

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An Efficient Sparse Representation Method for Passive Radar

Passive radar (PR) commonly estimates target parameters by calculating the cross-ambiguity function (CAF), which is prone to generating a wider main lobe and higher sidelobes, leading to issues such as weak targets being masked and adjacent targets being difficult to distinguish. A parameter estimation method for PR based on sparse representation (SR) is proposed to address the above challenges. First, an SR model based on signal segmentation and Fourier transform is proposed to address the issue of excessively large dictionary matrix (DM) by using the fast Fourier transform (FFT). Then, an orthogonal matching pursuit (OMP) algorithm based on detection threshold (DT-OMP) is proposed to adaptively determine the number of atoms to be selected by a preset threshold. Furthermore, a model mismatch correction method for SR (MMC-SR) is proposed to achieve accurate estimation of target parameters in off-grid situations. Simulations and practical experiments have shown that the proposed method can effectively mitigate the influence of wider main lobe and higher sidelobes of CAF, thereby improving resolution and providing a refined estimation of target parameters, showcasing significant practical application value.

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice