Research on a forward-looking scanning imaging algorithm for a high-speed radar platform

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IET Signal Processing Pub Date : 2023-05-25 DOI:10.1049/sil2.12221
Sijia Liu, Minghai Pan
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Abstract

The range and azimuth information of a target can be obtained after coherent pulse accumulation of the traditional multiframe stepped-frequency (SF) synthesis wideband echo and spectrum analysis, and high-resolution two-dimensional imaging of the target can be achieved. However, the accumulation of a certain number of pulses requires a long beam dwell time, which cannot meet real-time imaging requirements for high-speed radar moving platforms. To solve the above problems, a scanning imaging mode is proposed by combining forward-looking imaging and scanning imaging, and a target echo signal model with the structure of scanning stepped-frequency is constructed. The SF pulses are grouped and transmitted according to the scanning order, and the echo pulses are sorted and reorganised. After the timing compensation and range Doppler coupling compensation are completed, the target is located and projected. The proposed imaging mode can achieve high-resolution scanning forward-looking imaging and can basically attain an azimuth resolution of approximately 0.1° within the forward-looking scanning range. This imaging mode has higher real-time performance and a larger target imaging range than the traditional methods. Moreover, the simulation results showed good performance via the scanning imaging method.

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高速雷达平台前瞻性扫描成像算法研究
通过对传统的多帧步进频率(SF)合成宽带回波的相干脉冲积累和频谱分析,可以获得目标的距离和方位信息,实现目标的高分辨率二维成像。然而,积累一定数量的脉冲需要较长的波束停留时间,无法满足高速雷达移动平台的实时成像要求。为了解决上述问题,提出了一种将前瞻成像和扫描成像相结合的扫描成像模式,并构建了具有扫描步进频率结构的目标回波信号模型。根据扫描顺序对SF脉冲进行分组和传输,并对回波脉冲进行排序和重组。在完成定时补偿和距离多普勒耦合补偿之后,定位并投影目标。所提出的成像模式可以实现高分辨率扫描前瞻成像,并且在前瞻扫描范围内基本上可以达到约0.1°的方位分辨率。这种成像模式比传统方法具有更高的实时性和更大的目标成像范围。此外,通过扫描成像方法,仿真结果显示了良好的性能。
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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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