Radar Emitter Recognition Based on SIFT Position and Scale Features

Liu, Shaokun; Yan, Xiaopeng; Li, Ping; Hao, Xinhong; Wang, Ke

doi:10.1109/tcsii.2018.2819666

Cited by 46 publications

(32 citation statements)

References 13 publications

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“…, k = i, j = 1, 2, and φ ij is a random number in [0, 1]. The correlation between the original honey source location x ij and the random honey source location x kj affects the new honey source location v ij , f it i is the fitness value of x i , R is a random number in [0, 1], and x j max and x j min are upper and lower bounds of the jth dimension, respectively.…”

Section: Abc Algorithm For Vmdmentioning

confidence: 99%

“…With the increasing complexity of the electromagnetic environment, the existing radar signal recognition techniques cannot meet the needs of practical application. At the same time, recognition of radar signals arriving at the receiving device has become a key technique in the field of electronic countermeasures [1]. In a real electromagnetic environment, there are usually many radars detecting transmitter signals to steal information from transmitters.…”

Section: Introductionmentioning

confidence: 99%

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A Rapid Accurate Recognition System for Radar Emitter Signals

et al. 2019

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Radar signal recognition is an indispensable part of an electronic countermeasure system. In order to solve the problem that the current techniques have, which is a low recognition rate and a slow recognition speed for radar signals, a rapid accurate recognition system is proposed, especially for when multiple signals arrive at the receiver. The proposed system can recognize eight types of radar signals while separating signals: binary phase shift keying (BPSK), linear frequency modulation (LFM), Costas, Frank code, and P1–P4 codes. Regression variational mode decomposition (RVMD) is explored to separate the received signals, which saves time for parameter optimization of variational mode decomposition (VMD). Furthermore, signal separation and a noise removal technique based on VMD and the first component recognition technique based on a deep belief network (DBN) are proposed. In addition, in order to overcome the loss of the secondary component caused by signal separation, a fusion network is explored to increase the recognition rate of the secondary component in a short time. The simulation results show that the recognition system achieves an overall recognition rate of 99.5% and 94% at a signal-to-noise ratio (SNR) of 0 dB when receiving single signals and double signals, while spending 0.8 s and 2.23 s, respectively. The proposed system can also be used to recognize medical and mechanical signals.

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Section: Abc Algorithm For Vmdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Rapid Accurate Recognition System for Radar Emitter Signals

et al. 2019

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“…For example, in [16] and [25], Choi-Williams time-frequency distribution (CWD) image processing was used to extract radar signal features and successfully recognition several modulations at low SNR. In [26], the signal features were extracted based on STFT image processing, and then a binary decision tree was exploited to obtain an accuracy over 90% at low SNR for four modulations. Note that image processing, including image binarization, image enhancement, and image opening operation, is crucial for image features extractions, Nevertheless, most expert features that rely on the analysis of a mathematical model of the signal leads to fail when applied to various modulations because they focus on several specific modulations only.…”

Section: Related Work Of Modulation Recognition a Feature-based mentioning

confidence: 99%

Automatic Modulation Recognition of Radar Signals Based on Manhattan Distance-Based Features

Huang

Jin

et al. 2019

IEEE Access

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Feature-based (FB) algorithms for automatic modulation recognition of radar signals have received much attention since they are usually simple to realize. However, existing FB approaches usually focus on several specific modulations and fail when applied to various modulations. To overcome this issue, we propose a simple and effective FB algorithm based on Manhattan distance-based features (MDBFs) in this paper. MDBFs are new features for radar signals that can be applied for recognition of different modulations. The main contributions of this paper are as follows. First, radar signals are represented as wavelet ridges, which includes important information that can distinguish different modulations, and the piecewise aggregate approximation algorithm is introduced to reduce signal dimensions. Then, the dynamic time warping averaging is employed instead of the traditional k-means algorithm to extract realistic centroids for each class. Finally, the Manhattan distances between each data sample and each centroid are used to construct MDBFs, and decisions are made using the k-nearest neighbor. In addition, we prove that MDBFs have better class separability power than the Euclidean-based features. MDBFs contain information about the correlations between different classes, which means that these features suitable for discriminating various modulations when their class distributions do not overlap badly in representation space. The extensive experiments on a synthetic dataset demonstrate the outstanding performance of our proposed method and are hardly affected by the pulse width of the signal. Thus, the proposed method with the effectiveness and robustness could be a promising modulation recognition method of the radar signal. INDEX TERMS Modulation recognition, Manhattan distance-based feature, wavelet ridge, dynamic time warping averaging, class centroid.

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“…The registration problem is to find the correct correspondence between two sets of points extracted from the input data and restore the underlying spatial mapping at the same time, which can also be considered a point set registration problem. The widely used solutions, such as scale invariant feature transform (SIFT) [19,20], speeded up robust features (SURF) [2], and shape context (SC) [3], are still hot spots in image registration. The second is intensity-based registration method [7,27].…”

Section: Introductionmentioning

confidence: 99%

Differential evolution algorithm with population knowledge fusion strategy for image registration

Sun

Yang

et al. 2021

Complex Intell. Syst.

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Image registration is a challenging NP-hard problem within the computer vision field. The differential evolutionary algorithm is a simple and efficient method to find the best among all the possible common parts of images. To improve the efficiency and accuracy of the registration, a knowledge-fusion-based differential evolution algorithm is proposed, which combines segmentation, gradient descent method, and hybrid selection strategy to enhance the exploration ability in the early stage and the exploitation ability in the later stage. The proposed algorithms have been implemented and tested with CEC2013 benchmark and real image data. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of solution quality, convergence speed, and solution success rate.

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Radar Emitter Recognition Based on SIFT Position and Scale Features

Cited by 46 publications

References 13 publications

A Rapid Accurate Recognition System for Radar Emitter Signals

A Rapid Accurate Recognition System for Radar Emitter Signals

Automatic Modulation Recognition of Radar Signals Based on Manhattan Distance-Based Features

Differential evolution algorithm with population knowledge fusion strategy for image registration

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