International journal of advances in science, engineering and technology最新文献

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Identification of Radar Signals Based on Time-Frequency Agility using Short-Time Fourier Transform 基于短时傅里叶变换的时频捷性雷达信号识别

International journal of advances in science, engineering and technology

Pub Date : 2018-08-14 DOI: 10.37121/JASE.V1I2.18

A. A. Ahmad, A. Saliu, A. Airoboman, U. M. Mahmud, S. Abdullahi

With modern advances in radar technologies and increased complexity in aerial battle, there is need for knowledge acquisition on the abilities and operating characteristics of intercepted hostile systems. The required knowledge obtained through advanced signal processing is necessary for either real time-warning or in order to determine Electronic Order of Battle (EOB) of these systems. An algorithm was therefore developed in this paper based on a joint Time-Frequency Distribution (TFD) in order to identify the time-frequency agility of radar signals based on its changing pulse characteristics. The joint TFD used in this paper was the square magnitude of the Short-Time Fourier Transform (STFT), where power and frequency obtained at instants of time from its Time-Frequency Representation (TFR) was used to estimate the time and frequency parameters of the radar signals respectively. Identification was thereafter done through classification of the signals using a rule-based classifier formed from the estimated time and frequency parameters. The signals considered in this paper were the simple pulsed, pulse repetition interval modulated, frequency hopping and the agile pulsed radar signals, which represent cases of various forms of agility associated with modern radar technologies. Classification accuracy was verified using the Monte Carlo simulation performed at various ranges of Signal-to-Noise Ratios (SNRs) in the presence of noise modelled by the Additive White Gaussian Noise (AWGN). Results obtained showed identification accuracy of 99% irrespective of the signal at a minimum SNR of 0dB where signal and noise power were the same. The obtained minimum SNR at this classification accuracy showed that the developed algorithm can be deployed practically in the electronic warfare field for accurate agility classification of airborne radar signals.

随着现代雷达技术的进步和空战复杂性的增加，需要对被拦截的敌方系统的能力和操作特性进行知识获取。通过高级信号处理获得的所需知识对于实时预警或确定这些系统的电子作战命令(EOB)是必要的。为此，本文提出了一种基于联合时频分布(TFD)的雷达信号时频捷性识别算法。本文使用的联合TFD是短时傅里叶变换(STFT)的平方幅值，其中利用其时频表示(TFR)在瞬间得到的功率和频率分别估计雷达信号的时间和频率参数。然后通过使用由估计的时间和频率参数形成的基于规则的分类器对信号进行分类来进行识别。本文考虑的信号有简单脉冲、脉冲重复间隔调制、跳频和捷变脉冲雷达信号，它们代表了现代雷达技术中各种形式的捷变情况。在加性高斯白噪声(AWGN)建模的噪声存在的情况下，使用蒙特卡罗模拟在各种信噪比(SNRs)范围内进行分类准确性验证。结果表明，在信号和噪声功率相同的情况下，无论信号的最小信噪比为0dB，识别精度均为99%。在该分类精度下得到的最小信噪比表明，该算法可实际应用于电子战领域，对机载雷达信号进行精确的敏捷性分类。

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引用次数: 4

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International journal of advances in science, engineering and technology

Pub Date : 2017-08-01 Epub Date: 2017-10-26

Malcolm J D'Souza, Kristopher P Roeske, Lily S Neff

To develop best practices for laboratory safety and for chemical product and supplies management accountability, the freely-available online platform, Quartzy, was integrated within an interdisciplinary science department at a small Mid-Atlantic liberal-arts college. This was done to ensure the accuracy of purchase records, the appropriate use of storage and handling protocols, and for a continually updated chemical inventory system. Quartzy also facilitated the digital tracking and dispersal of the College's hazardous waste inventory. Since the implementation of the Quartzy platform, the science department achieved significant cost-savings during the procurement of laboratory supplies and equipment, and it developed a sense of ownership towards the common goal of lowering the College's environmental impact as it relates to its managing of laboratory-generated hazardous wastes.

为了开发实验室安全和化学产品和供应管理责任的最佳实践，免费提供的在线平台Quartzy被整合到大西洋中部一所小型文理学院的跨学科科学系中。这样做是为了确保采购记录的准确性，适当使用储存和处理规程，以及不断更新化学品盘存系统。Quartzy还促进了学院危险废物清单的数字化跟踪和分散。自推行Quartzy平台以来，科学系在采购实验室用品和设备方面节省了大量成本，并在管理实验室产生的危险废物时，对降低学院对环境的影响这一共同目标产生了归属感。

引用次数: 0

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