Pourya Yaghoubi Aliabad, Hossein Soleimani, Mohammad Soleimani
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引用次数: 0
摘要
合成脉冲和孔径雷达(SIAR)是频率多样化阵列(FDA)雷达在实践中可以采用的一种技术,从而克服了其面临的一些挑战。SIAR 雷达可用于交通和国防等多个领域,通过其 4D 匹配滤波器和单个接收器探测目标的距离、方位角、仰角和多普勒。然而,高振幅侧摆是研究人员非常关注的难题。他们试图通过频率编码、测距-角度耦合和测距-多普勒耦合等各种方法来减少高幅侧音,从而准确识别目标特征。本文介绍了天线位置编码(AP 编码)参数,它是最小化边瓣振幅的一个重要因素。该参数规定,在每个脉冲重复间隔(PRI)内,不是让所有天线都处于工作状态,而是让一定数量的天线处于工作状态,以获得较低的边瓣。研究人员发现,使用 AP 代码可以有效降低测距角边音、测距-多普勒边音、误差耦合、边音股重复以及不同目标角度的角度误差输出的幅度。为简单起见,所有研究均在线性阵列上进行。各种 AP 代码的输出与之前常用的均匀阵列进行了比较。
The Effect of Antenna Place Codes for Reducing Sidelobes of SIAR and Frequency Diverse Array Sensors
Synthetic impulse and aperture radar (SIAR) is a technique that frequency diverse array (FDA) radars can imply in practice, thus overcoming some of their challenges. SIAR radars, used in various fields like transportation and defense, can detect the range, azimuth angle, elevation angle, and Doppler of the target with their 4D-matched filter and a single receiver. However, the challenge of high-amplitude sidelobes is a significant concern for researchers. They have attempted to reduce it through various approaches, including frequency code, range–angle coupling, and range–Doppler coupling, to accurately identify target characteristics. This paper presents the antenna place code (AP code) parameter as a significant factor in minimizing sidelobe amplitudes. The parameter specifies that, rather than having all antennas active, a certain number of antennas are active in each pulse repetition interval (PRI) to achieve a lower sidelobe. Researchers have found that using AP codes can effectively lower the amplitude of the range–angle sidelobe, the range–Doppler sidelobe, error coupling, the repetition of sidelobe strands, and the output of angle error for different target angles. All studies are conducted on a linear array for simplicity. The output of various AP codes is compared to the previously common uniform array.
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