Discrete wavelet transform based unsupervised underdetermined blind source separation methodology for radar pulse deinterleaving using antenna scan pattern

Dutt, Rashi; Baloria, Anuradha; Prasad, V Ram Chandra; Esmp, Radhakrishna; Acharyya, Amit

doi:10.1049/iet-rsn.2018.5525

Cited by 6 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is difficult to deinterleave staggered signals and extract the jittered signal from the electromagnetic space under complex conditions. Incomplete extraction of pulses is prone to false alarms, and incorrect removal of other pulses will lead to missed detection. (D) Based on the discrete wavelet transform deinterleaving method [18] is not good at deinterleaving more than four complex radar signals. The antenna scan pattern needs to be obtained from the DOA of the main radar lobe, and in the actual process, ESM may not capture the main radar lobe; more is the sidelobe.…”

Section: Simulation Results and Performance Analysismentioning

confidence: 99%

“…The improved algorithm using clustering and PRI transform [17] can estimate the PRI value for various jittered signals with high accuracy, but it cannot separate the staggered signals. The antenna scan pattern is applied to deinterleaving [18], but the relevant information needs to be obtained by capturing the main lobe of the radar emitter. Dudczyk and Kawalec [19] used a graphical representation of the PRI distribution to identify specific radiation sources.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Synthetic algorithm for deinterleaving radar signals in a complex environment

Zhang

Liu

2020

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Section: Simulation Results and Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthetic algorithm for deinterleaving radar signals in a complex environment

Zhang

Liu

2020

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Object detection based deinterleaving of radar signals using deep learning for cognitive EW

Kocamış,

Orduyılmaz,

Taşcıoğlu

2024

SIViP

View full text Add to dashboard Cite

In a real-world environment, multifunction radars (MFRs) pose major challenges to the electronic support system that is a part of cognitive electronic warfare. One of the main problems is deinterleaving of signals belonging to MFRs. The ability of MFRs to change their carrier frequency, pulse width, and pulse repetition interval (PRI) from pulse to pulse makes the deinterleaving task challenging. In this paper, an object detection based deinterleaving approach exploiting amplitude patterns caused by radar beam motions, which are determined based on radar antenna scan types, is proposed to deinterleave MFR signals. Amplitude patterns are created in two-dimensional images using amplitude (AMP) and time of arrival (TOA) parameters obtained from radar pulses. Deinterleaving is performed using a deep learning algorithm applied to these images. To the best of the authors’ knowledge, object detection based deinterleaving of radar signals using AMP-TOA images has not been considered so far. Contrary to the common approaches, in which searching for PRI patterns is required, amplitude patterns formed by the radar beam motions on the electronic support system are used in the proposed method. This enables robust identification of signals of MFRs having PRI, carrier frequency, and pulse width agility. With the proposed method, the intention of the radar, i.e., search or tracking, can be acquired in the deinterleaving stage, which ensures earlier situational awareness. The performance of the method is evaluated for varying number of radar signals from one to five through simulations, in which scenarios with missing pulses at different rates are considered. Simulation results demonstrate that, on the average, more than 0.98 mean average precision (mAP50) is achieved at 30% missing pulse rate. The performance of the method for search radar signals is slightly lower compared to that achieved for tracking radar signals due to the lower number of pulses received by the system. However, more than 0.93 AP50 is achieved for search radar signals in the presence of five radar signals with different missing pulse rates. Besides, real-time performance can be achieved using the proposed method on the GPU platform.

show abstract

An end-to-end radar pulse deinterleaving structure based on point cloud mapping

Chen,

Qiu,

et al. 2024

Digital Signal Processing

View full text Add to dashboard Cite

Discrete wavelet transform based unsupervised underdetermined blind source separation methodology for radar pulse deinterleaving using antenna scan pattern

Cited by 6 publications

References 34 publications

Synthetic algorithm for deinterleaving radar signals in a complex environment

Synthetic algorithm for deinterleaving radar signals in a complex environment

Object detection based deinterleaving of radar signals using deep learning for cognitive EW

An end-to-end radar pulse deinterleaving structure based on point cloud mapping

Contact Info

Product

Resources

About