Radar Signal Sorting Algorithm Based on PRI for Large Jitter

Wang, B.; Gao, Bin; Wang, L.; Xin, Fengming; Song, Xin

doi:10.1088/1757-899x/466/1/012042

Cited by 3 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Curve fitting is a widely used technique in the domain of radar signal processing such as estimation of radar's locations, target recognition and signal sorting etc. [40–42], and here we are introducing it for the first time in the domain of radar deinterleaving. Least square curve fitting of deinterleaved pulses: A second degree polynomial is used to describe the morphology of the ASP as given in the equation below to denote the transition between two pulses at TOAs

TO A_{i}

and

TO A_{i + 1}

as follows:

y false(t false) = C_{1} \cdot t^{2} + C_{2} \cdot t + C_{3},

where y ( t ) is the estimated magnitude received by an antenna at a time t and

C_{1}

C_{2}

and

C_{3}

are constants derived from the PA information. The values of

C_{1}

C_{2}

and

C_{3}

are determined in the least square sense by using the polyfit function in MATLAB.…”

Section: Proposed Methodologymentioning

confidence: 99%

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

Dutt

Baloria

Prasad

et al. 2019

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

TO A_{i}

and

TO A_{i + 1}

as follows:

y false(t false) = C_{1} \cdot t^{2} + C_{2} \cdot t + C_{3},

where y ( t ) is the estimated magnitude received by an antenna at a time t and

C_{1}

C_{2}

and

C_{3}

are constants derived from the PA information. The values of

C_{1}

C_{2}

and

C_{3}

are determined in the least square sense by using the polyfit function in MATLAB.…”

Section: Proposed Methodologymentioning

confidence: 99%

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

Dutt

Baloria

Prasad

et al. 2019

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…The similarity coefficient is used to measure the degree of consistency between the source signal and the separated signal [ 43 ]. In order to avoid the influence of inversion and facilitate comparison, the absolute value of the similarity system is generally calculated; that is,

where

is the i -th component of the source signal, and

is the corresponding j -th component after separation, where

.…”

Section: Simulation Verificationmentioning

confidence: 99%

SMSP Mainlobe Jamming Suppression with FDA-MIMO Radar Based on FastICA Algorithm

Wan

Liao

et al. 2023

Sensors

View full text Add to dashboard Cite

In the electronic warfare environment, the performance of ground-based radar target search is seriously degraded due to the existence of smeared spectrum (SMSP) jamming. SMSP jamming is generated by the self-defense jammer on the platform, playing an important role in electronic warfare, making traditional radars based on linear frequency modulation (LFM) waveforms face great challenges in searching for targets. To solve this problem, an SMSP mainlobe jamming suppression method based on a frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar is proposed. The proposed method first uses the maximum entropy algorithm to estimate the target angle and eliminate the interference signals from the sidelobe. Then, the range-angle dependence of the FDA-MIMO radar signal is utilized, and the blind source separation (BSS) algorithm is used to separate the mainlobe interference signal and the target signal, avoiding the impact of mainlobe interference on target search. The simulation verifies that the target echo signal can be effectively separated, the similarity coefficient can reach more than 90% and the detection probability of the radar is significantly enhanced at a low signal-to-noise ratio.

show abstract

“…However, only the contents of the snapshot configuration were reported, and no information was presented on how to estimate the PRI. In addition, another study proposed a method of processing a large jitter rate signal through gradient analysis of the input signal [20]. A method of extracting features by converting the collected signal into a 2D image and estimating the PRI using a rough transform [21] was also proposed.…”

Section: A Deinterleavingmentioning

confidence: 99%

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

Han

Park

2021

IEEE Access

View full text Add to dashboard Cite

In the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for deinterleaving pulse trains. The PRI is modulated in various forms depending on the purpose of the radar operation, and analyzing the mean PRI and the modulation type of PRI is the core of electronic warfare signal processing. Many existing papers have tried separate independent approaches for deinterleaving and for PRI modulation recognition. However, many distortions are unintentionally generated in the process of extracting the pulse train using the PRI estimated through deinterleaving for the PRI modulation recognition. This degrades the the modulation recognition performance. In this paper, we propose a unified method for the deinterleaving and PRI modulation recognition of radar pulses using deep learning-based multitasking learning. The simulation results demonstrate the good performance of the proposed method for deinterleaving and modulation recognition, compared to the conventional method, and prove that the proposed method is robust in noisy radar signal environments.

show abstract

Radar Signal Sorting Algorithm Based on PRI for Large Jitter

Cited by 3 publications

References 2 publications

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

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

SMSP Mainlobe Jamming Suppression with FDA-MIMO Radar Based on FastICA Algorithm

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

Contact Info

Product

Resources

About