Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

Dai, Jian; Hao, Xinhong; Li, Ze; Li, Ping; Yan, Xiaopeng

doi:10.1016/j.dt.2021.02.008

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…Naïve Bayes Classifier (NBC) Naive Bayes Classifier (NBC) adalah metode klasifikasi yang bersifat terawasi yang didasarkan pada teorema Bayes yang berasal dari probabilitas bersyarat [18], Pengklasifikasi Naive Bayes sangat digunakan dan telah berkembang ke dalam lima kategori tingkat lanjut. Ini mencakup pengembangan struktur untuk mempertimbangkan ketergantungan atribut, perluasan struktur, seleksi contoh, pemberian bobot pada contoh, pemilihan atribut, dan pemberian bobot pada atribut untuk meningkatkan daya diskriminasi [19]. Metode yang digunakan adalah menghitung himpunan kemungkinan dengan menjumlahkan kombinasi dan frekuensi dari nilai record yang digunakan.…”

Section: 4unclassified

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Implementasi Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor untuk Klasifikasi Penyakit Ginjal Kronik

Wulandari,

Sari,

Alfian

et al. 2024

MALCOM

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Ginjal adalah salah satu organ vital yang memiliki peranan sangat penting dalam tubuh dan memiliki fungsi untuk menjaga keseimbangan metabolishme tubuh dengan mengeluarkan racun dari dalam tubuh dan limbah metabolisme dalam bentuk urine. Penyakit ginjal kronik ialah kondisi di mana ginjal mengalami penurunan fungsi yang berlangsung dalam jangka waktu yang lama. Jumlah nilai prelevansi penderita PGK di Indonesia yang terbilang besar. Oleh karena itu dilakukan klasifikasi Penyakit ginjal kronik dengan algoritma Naïve Bayes Classifier (NBC) dan K- nearest Neighbor (KNN) yang mempunyai nilai akurasi yang baik. Berdasarkan Hasil penelitian yang diperoleh klasifikasi PGK menggunakan algoritma NBC memiliki akurasi sebesar 94,25%, rata-rata nilai recall 94,23%, presisi 98,40% dan AUC 0,961, Sedangkan klasifikasi menggunakan algoritma KNN memiliki akurasi sebesar 77,79%, recall 95,06%, presisi 80,20% dan AUC sebesar 0,627. Dari kedua hasil menunjukan bahwa klasifikasi menggunakan algoritma NBC lebih baik dibanding menggunakan algoritma KNN.

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Section: 4unclassified

“…Kelebihan dari supervised learning adalah memverifikasi kelas data berdasarkan sampel pelatihan dan akurasi klasifikasi. Sedangkan pada unsupervised learning kelebihannya adalah kesalahan operasi kelas yang diminimalkan dan unik diperlakukan sebagai entitas terpisah [19].…”

Section: 7unclassified

Implementasi Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor untuk Klasifikasi Penyakit Ginjal Kronik

Wulandari,

Sari,

Alfian

et al. 2024

MALCOM

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“…In the field of view and listening, if the source of the oscillator and the object to which these fluctuations are touched, either one or the other is moving, we will have the difference between the frequency of the received waves and the frequency of the received waves, which is called the Doppler effect. A Doppler radar that is based on pulse modulation is called the pulse Doppler radar [27]. The advantage of combining Doppler processing with pulsed radars is that it provides accurate speed information.…”

Section: Radar Signal Processingmentioning

confidence: 99%

Implementation of Digital Signal Processor for Pulse Doppler Radar

Parhizgar,

Parhizgar

et al. 2023

Comput. Networks Commun.

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This paper presents the design and implementation of a digital signal processor (DSP) board, utilizing a TMS320C50 family DSP chip, for pulse-Doppler radar systems. Pulse-Doppler radar systems often encounter challenges such as strong clutter, noise, and jamming in dealing with echoes. To overcome these challenges, advanced digital signal processing techniques are employed. The main objective of this paper is to introduce a cost-effective signal processing solution that significantly enhances the performance of the radar system and brings it up to speed with modern radar technologies. The hardware described in this work can also be effectively utilized for implementing various types of signal processing algorithms. Additionally, as a secondary objective, the paper presents the digital realization of a radar detector. Traditionally, this detector was constructed using an analog Doppler filter bank. However, in this work, it is digitally implemented using N digital filters in place of the analog bank. By utilizing the designed DSP board and implementing the radar detector digitally, this paper demonstrates the potential for improved performance and efficiency in pulse-Doppler radar systems. The advancements made in this work contribute to the development of cost-effective and technologically advanced radar systems. The research results are presented for four different wind conditions, showcasing the effectiveness of the proposed approach. Furthermore, the paper suggests an algorithm that combines parametric and non-parametric techniques and provides a detailed explanation of its implementation. Finally, using the non-parametric technique, the probability of detection curves (PD) are simulated with respect to the signal-to-clutter ratio (SCR) for each wind condition, and the simulation results are depicted and compared. The achievements of this paper include the proposal of an efficient approach for clutter suppression in ground surveillance pulse Doppler radar, the comparison of different clutter suppression algorithms, and the development of an algorithm that combines parametric and non-parametric techniques. The simulation results provide valuable insights into the performance of the proposed algorithms under different wind conditions.

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“…At present, radar jamming technology, including active and passive jamming methods, mainly reduces the detection ability of radar by forming a strong clutter background or a large number of false target echoes on the radar display [ 4 , 5 ]. However, with the development of radar filtering technology, radar can analyze the amplitude, frequency, waveform, space, polarization and other angles of echoes to obtain target information to avoid interference [ [6] , [7] , [8] , [9] , [10] , [11] , [12] ]. Thus, the false-echo-dependent interference mode is easy to be failed.…”

Section: Introductionmentioning

confidence: 99%

An Innovative model of magnetically intercalated expanded graphite for calculating radar attenuation performance at 2–18 GHz

Zhang

Tang

Chen

et al. 2023

Heliyon

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Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

Cited by 12 publications

References 20 publications

Implementasi Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor untuk Klasifikasi Penyakit Ginjal Kronik

Implementasi Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor untuk Klasifikasi Penyakit Ginjal Kronik

Implementation of Digital Signal Processor for Pulse Doppler Radar

An Innovative model of magnetically intercalated expanded graphite for calculating radar attenuation performance at 2–18 GHz

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