Ben Niu, Yongbo Zhao, Mei Zhang, Derui Tang, Tingxiao Zhang, Shuaijie Zhang, Di Gao
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引用次数: 0
摘要
本文提出了一种新型的以能量为重点的慢速多输入多输出(MIMO)雷达和信号处理方案,旨在解决慢速编码和信号处理技术中的关键难题。传统的慢时 MIMO 雷达面临着正交编码的全向发射波束和速度模糊性问题造成的能量浪费等问题。为了克服这些局限性,所提出的雷达系统利用基于多普勒频率偏移分集(DFOD)的方法进行慢时编码设计。这种方法可以调整多普勒偏移参数,在单个相干处理间隔(CPI)内实现任意主波束宽度的矩形发射波束,具有计算复杂度低的优点。通过分析基于 DFOD 编码的模糊函数,我们评估了多普勒和角度分辨率。为了进一步提高多普勒频率分辨率,我们引入了基于脉冲随机排列(PRP)的慢速编码设计。随后,介绍了一种基于匹配滤波的信号处理方案。为了解决与基于 PRP 的编码相关的高多普勒侧叶问题,我们提出了一种利用凸优化的失配滤波器(MMF)设计方法。最后,与现有技术相比,通过仿真分析验证了所提出的慢时多输入多输出雷达的性能提升。
A novel energy-focused slow-time MIMO radar and signal processing scheme
In this paper, we propose a novel energy-focused slow-time MIMO radar and signal processing scheme, aimed at addressing key challenges in slow-time coding and signal processing technology. Conventional slow-time MIMO radar faces issues such as energy waste due to the omnidirectional transmit beampattern of orthogonal coding and the velocity ambiguity problem. To overcome these limitations, the proposed radar system utilizes a method based on Doppler frequency offset diversity (DFOD) for slow-time coding design. This method enables the adjustment of Doppler offset parameters to achieve a rectangular transmit beampattern with any mainlobe width within a single coherent processing interval (CPI), offering the advantage of low computational complexity. Through an analysis of the ambiguity function for DFOD-based coding, we evaluate both Doppler and angular resolution. To further improve Doppler frequency resolution, a slow-time coding design is introduced based on Pulse Random Permutation (PRP). Subsequently, a signal processing scheme based on matched filtering is presented. To tackle the high Doppler sidelobe issue associated with PRP-based coding, we propose a mismatch filter (MMF) design method utilizing convex optimization. Ultimately, the performance enhancement of the proposed slow-time MIMO radar is verified through simulation analysis in comparison to existing technologies.
期刊介绍:
Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing.
Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.