Research on the micromotion characteristics of a chaff cloud

Li, Ran; Hao, Xinhong; Li, Ping

doi:10.1186/s13634-019-0648-y

Cited by 3 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Figure 5, σ 2 kc and σ 2 kt , respectively, represent the variance of the Doppler frequency distribution of the chaff and target, that is, the spectral width. Figure 5 verifies the previous chaff identification method: it is feasible to judge P i with a large Doppler spectrum width as a chaff cloud, and this figure gives the accurate detection probability [11,13]. The larger the spectrum width of the chaff, the higher the detection probability, and the detection probability is also limited by the target spectrum width.…”

Section: Resultssupporting

confidence: 71%

“…The target is a rigid body, so we make the following assumptions: The scattering points are uniformly and continuously distributed within the target size range for distance [13]. For Doppler frequency, each scattering point has the same speed but is accompanied by Gaussian disturbance [11, 13]. …”

Section: Problem Formulationmentioning

confidence: 99%

“…For Doppler frequency, each scattering point has the same speed but is accompanied by Gaussian disturbance [11, 13].…”

Section: Problem Formulationmentioning

confidence: 99%

“…The Doppler spectrum of the chaff likewise differs from that of the target. Li analysed the micromotion characteristics of chaff dipoles and a chaff cloud [11]. Michael used a multiple moving target indicator (M 2 ) filter and a clutter map recursive filter to suppress the chaff signal by about 20 dB based on the Doppler frequency difference between the chaff cloud and the target [12].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Chaff identification method based on Range‐Doppler imaging feature

Wang

Chen

Zhu

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

Chaff is widely used in electronic countermeasures as an effective passive jamming method. This study proposes a chaff identification method that integrates the distribution of distance, Doppler frequency, and power in Range-Doppler imaging. The traditional radar signal processing method estimates the distance and Doppler frequency, and the mean-shift algorithm is combined to complete the clustering after the constant false alarm detection. Then, the target and chaff cloud models are modelled in three dimensions: radar cross-section, range, and Doppler frequency. Using the Neyman-Pearson criterion, three chaff likelihood ratio test detectors are designed based on these assumptions. The three detectors can effectively identify the chaff, and then the theoretical detection probability and influencing factors are analysed. At the same time, the Kullback-Leibler divergence is used to describe the distribution difference between the data to be detected and the theoretical target and chaff. Besides, the different classifiers are used to identify the features of the differences in Kullback-Leibler divergence in three dimensions. Finally, the real-life chaff datasets verified the excellent recognition rate of the authors' method for the chaff.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Resultssupporting

confidence: 71%

Section: Problem Formulationmentioning

confidence: 99%

“…For Doppler frequency, each scattering point has the same speed but is accompanied by Gaussian disturbance [11, 13].…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Chaff identification method based on Range‐Doppler imaging feature

Wang

Chen

Zhu

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…Figure 1 shows the third (S3) and fourth (S4) heart sounds that appear in the diastolic segment. The S3 sound is produced by a large amount of blood striking a very compliant left ventricle [8,9]. The fourth (S4) heart sound, also known as the "atrial gallop," occurs just before S1 when the atria contract to force blood into the left ventricle.…”

Section: Introductionmentioning

confidence: 99%

Heart Sound Signal Analysis for Digital Auscultation

Busono¹,

Karim²,

Kamaruddin³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Cardiac auscultation is the examination of the heart by listening to the sound produced by the heart through a stethoscope. Heart sounds can provide information about the functioning of the heart valve condition as well as information about the structural abnormalities of the heart. However, it needs intensive training for mastering. The objective of the work was to develop an algorithm for heart sound signal classification applied to computer-assisted digital auscultation in order to identify pathological events. The method can be described as follow: data collection, pre-processing, segmentation, feature extraction, and classification. The data were collected from volunteers using an electronic stethoscope and from a database available in the internet. The heart sound data were then extracted and split into training, validation, and testing datasets. In the training process the dataset was labeled as normal and abnormal (aortic stenosis, mitral stenosis, aortic regurgitation, pulmonic regurgitation, tricuspid stenosis, flow murmur, and patent ductus arteriosus). The convolution neural network is used as a classifier in the learning process to obtain the learning model. The model was validated and tested using the available datasets. The experimental results show that the algorithm has the capability to classify the heart sound into normal and abnormal with a high detection rate.

show abstract