Improvements on deinterleaving of radar pulses in dynamically varying signal environments

Gençol, Kenan; Kara, Ali; At, Nuray

doi:10.1016/j.dsp.2017.06.010

Cited by 32 publications

(25 citation statements)

References 28 publications

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“…In the following section, a comparative study of SOFM [15] and SANT-SOFM is accomplished to evaluate the radar pulse de-interleaving performance of the two algorithms. The performance evaluation involves the network topology, the number of sample hits and the clustering result.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…The interval [0,3] with a step size of 1 is used for searching optimal initial neighborhood radius. The 2-dimensional grid search [15] based on the principal component analysis (PCA) is used to initialize the SOFM network.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…Granger and Savaria [13] introduced SOFM into the de-interleaving process and evaluated the performance of SOFM. Based on [13], Ata'a [14] and Gencol [15] further improved this process, achieving intelligent and automatic de-interleaving of radar signals. Ata'a [14] used three radar features, including angleof-arrival (AOA), pulse width (PW) and radio frequency (RF), while Gencol [15] added estimated pulse amplitude (PA) as the fourth information source to assist the clustering process.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

Jiang

Chang

et al. 2020

J. of Syst. Eng. Electron.

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As a core part of the electronic warfare (EW) system, de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map (SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology (SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then, structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process, constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.

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Section: Simulation and Discussionmentioning

confidence: 99%

Section: Simulation and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

Jiang

Chang

et al. 2020

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

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“…When there are signals transmitted by multiple radars simultaneously, an interleaved stream of pulses is received by an electronic support system. The following task is to separate these signals [8], [9] and thus to identify the source emissions [10].…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Radar PRI Modulation Recognition With Recurrent Neural Networks

Liu

Huang

2020

IEEE Access

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Analyzing radar signals is a critical task in modern Electronic Warfare (EW) environments. However, the pulse streams emitted by radars have flexible features and complex patterns which are difficult to be identified from a statistical perspective. To solve this problem, pulse repetition interval (PRI) is used as a distinguishing parameter of emitters to be identified. However, traditional PRI modulation recognition methods can only deal with simple PRI modulations and their performance will further degrade with the increasing number of emitters or noisy environments. In this paper, we introduce an attention-based recognition framework based on recurrent neural network (RNN) to categorize pulse streams with complex PRI modulations and in environments with high ratios of missing and spurious pulses. Simulation results show that our model is robust to noisy environments and has a better performance than conventional methods. INDEX TERMS Attention mechanism, electronic warfare, PRI modulation, recurrent neural network (RNN).

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“…The classical PRI transform is optimised in [5, 6]. The algorithms of [7, 8] are based on neural networks. In [9], a decision method based on the variance of the difference in amplitude peak‐to‐peak intervals was introduced.…”

Section: Introductionmentioning

confidence: 99%