Improved Algorithm of Radar Pulse Repetition Interval Deinterleaving Based on Pulse Correlation

Ge, Zhipeng; Sun, Xiaojun; Ren, Wenjuan; Chen, Wenbin; Xu, Guangluan

doi:10.1109/access.2019.2901013

Cited by 50 publications

(32 citation statements)

References 15 publications

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“…Based on the validation results, the threshold in ( 6) is set to be µ T = 0.5, and the threshold in ( 7) is set to be 5 times the root mean square (RMS) of the probability p t (j) i+1 |∆ i+1 ; s i along the time axis excluding the pulse locations (This threshold is set like this because in normal Gaussian distributions, the variables has an amplitude smaller than 5 times the RMS with approximate 100% probability.). When training the pulse group autoencoder, the training dataset is expanded based on the observed pulse groups according to (14). Assume that a particular observed pulse group contains J pulses, the inputoutput pair of the autoencoder of (x, y) is first duplicated by J times.…”

Section: Simulation and Analyses A Model Establishing And Simulation Parameter Settingmentioning

confidence: 99%

Pulse Deinterleaving for Multifunction Radars With Hierarchical Deep Neural Networks

Liu

2021

IEEE Trans. Aerosp. Electron. Syst.

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Multi-function radars (MFR) work with pulse groups, and pulses in different groups are weakly correlated, which greatly increases the difficulty for MFR pulse deinterleaving, especially in cases of significant data noises. At present, no relevant research results have been reported to address this problem. In this paper, a hierarchical deep learning model will be established to describe the sequential patterns of pulse trains from MFR's. The bottom layer of the model represents discrete pulse groups with continuous vectors, which describes the temporal pattern of pulse groups and makes them machine readable. The top layer uses a recursive neural network (RNN) to mine the sequential pattern between consecutive pulse groups. The two layers are then synthesized to form a hierarchical model, which is able to describe the semantic correlations between different pulses within MFR pulse trains, and the parameters of subsequent pulse groups and their inner pulses can be predicted based on preceding pulses. Based on the hierarchical model, this paper proposes an iterative pulse deinterleaving method and a parallel one for MFR's. Simulation results demonstrate that, the proposed methods perform satisfyingly in separating interleaved pulses from radars of the same or different types, and they are robust to significant pulse noises.

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Section: Simulation and Analyses A Model Establishing And Simulation Parameter Settingmentioning

confidence: 99%

Pulse Deinterleaving for Multifunction Radars With Hierarchical Deep Neural Networks

Liu

2021

IEEE Trans. Aerosp. Electron. Syst.

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“…cal operations required by this algorithm in the modern real-time systems will be extremely difficult. The computational complexity of the algorithm described in [20] is lower, but, according to its authors, the performance of this algorithm rapidly degrades with the increasing proportion of the missed pulses. Thus, even at 5 % of the omissions, the algorithm incorrectly classifies more than 10 % of the pulses.…”

Section: Probability Of Pulse Overlap As a Quantitative Indicator Of mentioning

confidence: 99%

Probability of Pulse Overlap as a Quantitative Indicator of Signal Environment Complexity

Podstrigaev

Smolyakov

Maslov³

2020

Izv. vysš. učebn. zaved. Ross., Radioèlektron.

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Introduction. Simultaneous operation of numerous sources of radio emission form complex signal environment. Different devices with the common name "wideband analyzers" (WBA) are widely used to analyze and to control such environment. There is currently a need for developing the quantitative characteristics of a complex signal environment, which will make it possible to predict the stability of the WBA operation. Aim. The development of the indicator of the signal environment complexity, which will make possible the quantitative assessment of such environment. Materials and methods. To provide the desired indicator, simulation and mathematical tools for random events description are used. All calculations are performed using MatLab. Results. The principles of disturbances in the WBA receiver and algorithmic errors in the processing of overlapped signals are described. To quantify the "complexity" of the signal environment it is proposed to use the probability that pulses from several sources overlap in time. This allows one to compare signal environments with each other. The new analytical expression for estimating the pulse overlap probability is proposed. Functions of the pulse overlap probability from the complex signal environment parameters were obtained. Conclusion. According to the comparative analysis of the calculations using proposed analytical expression and simulation, the new expression allows one to achieve the calculation speed up to 6 orders of magnitude higher with an error below 7% compared to the simulation. The high performance of the calculations using the proposed expression allows one to simulate the complex signal environment in dynamics more efficiently.

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“…Signal sorting technology is mainly divided into pulse repetition interval (PRI) analysis and feature clustering [2]- [5]. Among them, the sorting algorithm based on PRI is mainly divided into two types: statistical histogram algorithm (cumulative difference histogram CDIF, sequence difference histogram SDIF) and sequence search method.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

Wang

Dong

et al. 2021

IEEE Access

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Radar signal sorting is the core part of electronic support measures, which is responsible for deinterleaving the overlapping pulse sequences received by the receiver from the complex environment, separating different radiation source signals, and providing support for radiation source identification. Particle swarm optimization (PSO) is a population-based global optimization algorithm with great advantages in intelligent sorting of complex signals, which can adapt to the electromagnetic environment with complex and variable radiation source signals and high pulse stream density. However, the PSO-based sorting method is prone to premature convergence and cannot adaptively adjust particle swarm parameters and positions. In this paper, a dynamic modified chaotic PSO algorithm (DMCPSO) is proposed. Chaotic search is used to increase the diversity of particle swarm in the later iteration to avoid premature convergence and falling into local optimum. Adaptive adjustment parameters related to the particle fitness value are adopted to balance the ability of global search and local search. A new fitness function is proposed and the particle position is dynamically corrected by clustering analysis to improve the accuracy of particle position optimization and avoid the influence from the distribution of feature parameters. The simulation results show that the DMCPSO algorithm provides stable and fast performance with excellent sorting indexes in complex, variable, and dense signal environment.

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Improved Algorithm of Radar Pulse Repetition Interval Deinterleaving Based on Pulse Correlation

Cited by 50 publications

References 15 publications

Pulse Deinterleaving for Multifunction Radars With Hierarchical Deep Neural Networks

Pulse Deinterleaving for Multifunction Radars With Hierarchical Deep Neural Networks

Probability of Pulse Overlap as a Quantitative Indicator of Signal Environment Complexity

Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

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