Subband STAP Based on the Sparse Frequency Waveform for Airborne Radar in Dense Unintentional Jamming Environment

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

IEEE Sensors Journal Pub Date : 2025-03-21 DOI:10.1109/JSEN.2025.3550325

Ming Hou;Wenchong Xie;Wei Chen;Yuanyi Xiong;Lingyan Dai

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引用次数: 0

Abstract

Compared with ground-based radar, airborne radar has excellent low-altitude moving target detection performance. With the development of economics, ground radiation sources are widely distributed, and the low-frequency spectrum is extremely crowded. When airborne radar operates over land, dense unintentional jamming can have a severe impact on target detection. The effects are mainly reflected in the following three aspects: 1) it is easy for jamming to enter from the main lobe and the main-lobe jamming is formed, which is difficult to be suppressed by traditional anti-jamming methods; 2) since the jamming distribution is dense, the degrees of freedom (DOFs) of jamming exceed the spatial DOFs of the system; and 3) jamming and clutter exist simultaneously and strong clutter is difficult to be suppressed effectively. In this article, a subband space-time adaptive processing (STAP) method for airborne radar based on the sparse frequency waveform is proposed. First, the jamming frequency and azimuth angle are obtained in the passive mode, and accordingly, the sparse frequency waveform is designed to suppress main-lobe jamming. Then, the echo data are divided into subbands, and each subband is processed by subband sidelobe cancellation processing to achieve the suppression of dense sidelobe jamming. Finally, the strong clutter is suppressed by STAP. The simulation results demonstrate that the proposed method has excellent performance in suppressing dense unintentional jamming and clutter.

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基于稀疏频率波形的机载雷达密集非故意干扰子带STAP

与地面雷达相比，机载雷达具有优良的低空运动目标探测性能。随着经济的发展，地辐射源分布广泛，低频频谱极为拥挤。当机载雷达在陆地上工作时，密集的无意干扰会对目标探测产生严重影响。其影响主要体现在以下三个方面：1)干扰容易从主瓣进入，形成主瓣干扰，传统的抗干扰方法难以抑制；2)由于干扰分布较密，干扰的自由度大于系统的空间自由度；3)干扰和杂波同时存在，强杂波难以有效抑制。提出了一种基于稀疏频率波形的机载雷达子带空时自适应处理方法。首先，在无源模式下获取干扰频率和方位角，并据此设计稀疏频率波形抑制主瓣干扰；然后，将回波数据分成子带，对每个子带进行子带旁瓣对消处理，实现对密集旁瓣干扰的抑制。最后，通过STAP抑制强杂波。仿真结果表明，该方法在抑制密集非故意干扰和杂波方面具有良好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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