Performance Comparison of Blackman, Bartlett, Hanning, and Kaiser Window for Radar Digital Signal Processing

Sulistyaningsih, Sulistyaningsih; Putranto, Prasetyo; qurrachman, Taufiq; Desvasari, Winy; Daud, Pamungkas; Wijayanto, Yusuf Nur; Mahmudin, Dadin; Kurniadi, Deni Permana; Rahman, Arief Nur; Hardiati, Sri; Setiawan, Arie; Darwis, Fajri; Pristianto, Eko Joni

doi:10.1109/icitisee48480.2019.9003791

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…To derive this assumption, a window function i.e., a function whose ends converge at zero, is used. Representative include Hanning and Kaiser windows [51]. The discrete-time STFT to be applied to the signal captured from this equation is as follows:…”

Section: A Spectrogram Transformationmentioning

confidence: 99%

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

et al. 2022

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Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of -15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of -15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable. INDEX TERMSAnti-drone systems, convolutional neural networks (CNNs), noise-tolerant, spectrograms, unmanned aerial vehicles (UAVs), UAV classification I. INTRODUCTION U NMANNED aerial vehicles (UAVs), including drones, are used for various purposes, such as delivery, agriculture, transportation, and communication [1]. The potential uses of such vehicles are continually increasing [2]. Additionally, social and commercial demands for remote technology have increased owing to the recent COVID-19 outbreak, and drones are being proposed as a noncontact solution for numerous applications. The approach in [3] shows how backup transportation systems based on existing drone infrastructure can play an important role during the COVID-19 pandemic and similar situations. Additionally, the authors in [4] proposed the application of drones for spraying disinfectants to combat the COVID-19 pandemic. However,

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Section: A Spectrogram Transformationmentioning

confidence: 99%

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

et al. 2022

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“…The energy in the main lobe of the spectral function will be increased by adjusting the window function, which will decrease the passband and stopband fluctuations and increase the stopband attenuation. Based on the above description and the comparison of the advantages and disadvantages of each window function, the Kaiser window is chosen to extract transient signals [20,21].…”

Section: Extraction Of Transient Characteristic Quantitiesmentioning

confidence: 99%

An improved Hausdorff distance method for locating single phase to ground fault in neutral non‐effectively grounded system

Zhang

et al. 2021

IET Generation Trans & Dist

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Neutral non-effectively grounded mode is widely used in medium-voltage distribution networks in China. When a single-phase grounding fault occurs in distribution networks, abundant transient signals will be generated. Here, a fault location method based on improved Hausdorff distance is proposed with transient zero-sequence current and transient zero-sequence voltage of bus. First, according to the impedance characteristic analysis of the zero-sequence network, the selected frequency band (SFB) is determined, and the transient signals in the SFB are extracted by the digital filter. Second, the projection components of transient zero-sequence currents at each monitoring point of faulty feeder are obtained by the principle of orthogonalization. Finally, the projection component values of each section are calculated by the improved Hausdorff distance, and the maximum value is selected. If the maximum value is much larger than the sum of the values of other sections excluding the maximum value, the section where the maximum value is located is judged as a faulty section, otherwise, the downstream section of the last monitoring point is determined as the faulty section. Matlab/Simulink simulation shows that the proposed method can locate accurately under different fault conditions.

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“…The windowing technique involves multiplying the digital output of the ADC with a suitable window function to mitigate the leakage of fundamental power. Common window functions include the Hanning, Blackman, Nuttall, and maximum attenuation (MSD) windows [ 12 , 13 , 14 , 15 ]. The spectrum of the window function includes a main lobe and multiple side lobes.…”

Section: Introductionmentioning

confidence: 99%

High-Precision ADC Spectrum Testing under Non-Coherent Sampling Conditions

Peng

Li²,

Zhang³

et al. 2022

Sensors

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Realizing coherent sampling is one of the major bottlenecks in high-precision ADC spectrum testing. In spectrum analysis, if coherent sampling is not implemented, spectral leakage will result, which in turn leads to inaccurate test results. In this paper, a combined four-parameter sine-curve-fitting algorithm is proposed incorporating non-coherent sampling, with the amplitude, initial phase, and frequency parameters of the sine wave being obtained by fitting. The corresponding coherent sine wave is then calculated and replaced according to the obtained sine wave to reconstruct the new test data, eliminating the requirement of coherent sampling. Numerous simulations demonstrated the functionality and robustness of the algorithm, which was then used to process and analyze the measured data of two commercial high-precision ADCs. The results show that our algorithm can achieve accurate testing of ADC parameters under relaxed test conditions, which verifies the effectiveness and superiority of the scheme.

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Performance Comparison of Blackman, Bartlett, Hanning, and Kaiser Window for Radar Digital Signal Processing

Cited by 7 publications

References 9 publications

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

An improved Hausdorff distance method for locating single phase to ground fault in neutral non‐effectively grounded system

High-Precision ADC Spectrum Testing under Non-Coherent Sampling Conditions

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