Search

This paper proposes a new method for recognizing weapon control radar signals by using a two-channel deep learning. The proposed method has two main stages. The first stage used Wigner-Ville distribution and Fourier series for extracting features of radar signals in frequency and time-frequency domain. The extracted features are used as input of classifier part. In the classifier part, two-channel deep learning is created for improving accuracy recognition of the above-mentioned signals. The functionality of the proposed method is tested with simulated data in MATLAB environments. The simulation results show that the proposed method outperforms in comparison to single channel network such short time Fourier series, Wigner-Ville distribution and continuous Wavelet transform. The proposed method can recognize 6 types of radar signals with P = 97,5% while existing single channel network methods provide only P ≤ 89%. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition (AMR) of radar signals plays a critical role in electronic reconnaissance. Current AMR algorithms are mainly based on convolutional neural networks (CNN), which can learn the feature hierarchy by establishing high-level features from low-level features. However, for time–frequency analysis-based methods, distinct low-level features in the time–frequency spectrum can already reflect modulation characteristics. Thus, this study develops a novel approach based on low-level shape descriptors via histograms of oriented gradients (HOG) and support vector machine (SVM). Comparison studies with classic CNN-based methods have also been done to reveal the superiority of the designed approach. Experimental results demonstrate that the HOG-SVM approach has a more efficient performance. To further enhance the classification precision under low signal-to-noise ratios, an improved principal component analysis denoising algorithm is developed to improve signal quality under intense noise background. Experiments based on simulated and measured signals demonstrate that the proposed algorithm can accurately distinguish signals under intense noise environments. [ABSTRACT FROM AUTHOR]

To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised training phase, a residual convolutional autoencoder network structure is first constructed to extract the semantic information from a training set consisting solely of known jamming patterns. By incorporating center loss and reconstruction loss into the softmax loss function, a joint loss function is constructed to minimize the intra-class distance and maximize the inter-class distance in the jamming features. Moving to the unsupervised classification phase, a test set containing both known and unknown patterns is fed into the trained encoder, and a distance-based recognition method is utilized to classify the jamming signals. The results demonstrate that the proposed model not only achieves sufficient learning and representation of known jamming patterns but also effectively identifies and classifies unknown jamming signals. When the jamming-to-noise ratio (JNR) exceeds 10 dB, the recognition rate for seven known jamming patterns and two unknown jamming patterns is more than 92%. [ABSTRACT FROM AUTHOR]

To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised training phase, a residual convolutional autoencoder network structure is first constructed to extract the semantic information from a training set consisting solely of known jamming patterns. By incorporating center loss and reconstruction loss into the softmax loss function, a joint loss function is constructed to minimize the intra-class distance and maximize the inter-class distance in the jamming features. Moving to the unsupervised classification phase, a test set containing both known and unknown patterns is fed into the trained encoder, and a distance-based recognition method is utilized to classify the jamming signals. The results demonstrate that the proposed model not only achieves sufficient learning and representation of known jamming patterns but also effectively identifies and classifies unknown jamming signals. When the jamming-to-noise ratio (JNR) exceeds 10 dB, the recognition rate for seven known jamming patterns and two unknown jamming patterns is more than 92%.

Algorithm of binary {-1, 1} group-complementary sequences direct construction for any sequences length N is suggested. On the base of these sequences the signals with very wide frequency bandwidth may be constructed (up to the ultra wideband (UWB) signals). Synthesized sequences may found their application in radar and communication.

This paper is devoted to the improvement of the working methods of the "signal-filter" pair using an iterative procedure to maximize the signal-to-noise ratio, namely, to highlight the useful signals to provide information about the environment during the adaptive radar sensing in the conditions of obstacles. An algorithm for determining the optimal filter for suppressing undesired side lobes is proposed, which allows improving the process of detection and identification the target objects. Qualitative characteristics and the stages of space-time signal processing in the radar were justified under correlation conditions (ССF) between the received and expected signals. The using of probing radar signals, shortened in their duration were proposed in order to reduce the power of reflections from the underlying surface and increase the contrast of the radar image. A simulation test of the developed methodology was carried out in order to confirm the reliability of the proposed algebraic expressions using a mathematical model implemented in the Matlab programming environment, and a conclusion is drawn about the practical quality of the technical solutions developed on the basis of the iteration method.

Sets of waveform and mismatched filter pairs are used. On the contrary with Golays matched waveform filter pair the mismatched waveform filter pair does exist for all N (number pulses in waveform). Using corresponding shapes of filter good Doppler tolerance may be provided. This property together with a good range side-lobs level suppression makes it’s attractable for use in marine radar.

In through-the-wall radar imaging, compressed sensing techniques have been applicable for small amount of observations to reconstruction walls or targets imaging in the scenes. The authors consider building walls imaging. The proposed approach exploits a low-rank and sparse model to extract the wall returns from radar signals, and then minimises the total variation regularisation problem for reconstructing building walls imaging using the sparsity of their spatial variations. The results of simulation exhibit that the proposed approach effectively enhances the imaging quality of building walls.

Orthogonal frequency division multiplexing which is called OFDM for short is not only a popular modulation technique in communication systems but also a good method to generate radar signals. A joint radar and communication system could be realised by an OFDM system according to some off-the-shelf works. The radar functionality is mainly considered here, which requires the system to equip with the ability to suppress interference. The typical radar signal, frequency-modulated continuous wave, can be viewed as narrowband interference for a large bandwidth OFDM radar with comparably short duration of OFDM symbols. Here, an interference suppression algorithm suitable for any type of narrowband interference is proposed for OFDM radar. The atomic norm minimisation (ANM) method involved in compressed sensing is introduced to obviate the interference. Then, the data with little interference can be reconstructed by reformulating the ANM as a semi-definite program. Meanwhile, the level of noise is quelled effectively in terms of the atomic norm soft-thresholding method and the gridless version of SPICE. Finally, the numerical simulation is performed to verify the effectiveness of the proposed method.

In this study, an inverse synthetic aperture radar (ISAR) image resolution enhancement algorithm based on joint dictionary learning is proposed, by which two special sets of sparse signals called dictionaries are solved by exploiting numerous high-resolution (HR) and low-resolution (LR) ISAR images. Herein a new coupled dictionary learning algorithm based on restricted Boltzmann machine (RBM) is designed to learn a LR and a HR dictionary using LR and HR image patches. Since the echoes are equivalent to similar scattering-centre models when an object is illuminated by radar signals with same centre frequency and different bandwidth, respectively, it is reasonable to assume the object's LR ISAR image shares the same sparse representation coefficients with its HR ISAR image. When a LR ISAR image is represented sparsely with a LR dictionary, a HR ISAR images can be reconstructed based on a HR dictionary owing to the similar sparse representation coefficients. Experiment results with simulation data demonstrate the superior performance of the proposed method over other classical dictionary training algorithms.

The development of radar signal simulator in specific environment can effectively accelerate the debugging process of radar system and reduce the consumption of human, material, and financial resources. This study analyses the mathematical model of the IF radar signal in aerial distributed semi-active radar system and deduces the parameters of radar signals based on direct digital synthesiser (DDS) technology. After solving the problem of multi-channel synchronisation, delay precision control, and envelope delay compensation, an IF radar signal simulator system was designed and implemented, which can generate three direct-path-wave signals and three echo signals.

Orthogonal frequency division multiplexing which is called OFDM for short is not only a popular modulation technique in communication systems but also a good method to generate radar signals. A joint radar and communication system could be realised by an OFDM system according to some off-the-shelf works. The radar functionality is mainly considered here, which requires the system to equip with the ability to suppress interference. The typical radar signal, frequency-modulated continuous wave, can be viewed as narrowband interference for a large bandwidth OFDM radar with comparably short duration of OFDM symbols. Here, an interference suppression algorithm suitable for any type of narrowband interference is proposed for OFDM radar. The atomic norm minimisation (ANM) method involved in compressed sensing is introduced to obviate the interference. Then, the data with little interference can be reconstructed by reformulating the ANM as a semi-definite program. Meanwhile, the level of noise is quelled effectively in terms of the atomic norm soft-thresholding method and the gridless version of SPICE. Finally, the numerical simulation is performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, an inverse synthetic aperture radar (ISAR) image resolution enhancement algorithm based on joint dictionary learning is proposed, by which two special sets of sparse signals called dictionaries are solved by exploiting numerous high-resolution (HR) and low-resolution (LR) ISAR images. Herein a new coupled dictionary learning algorithm based on restricted Boltzmann machine (RBM) is designed to learn a LR and a HR dictionary using LR and HR image patches. Since the echoes are equivalent to similar scattering-centre models when an object is illuminated by radar signals with same centre frequency and different bandwidth, respectively, it is reasonable to assume the object's LR ISAR image shares the same sparse representation coefficients with its HR ISAR image. When a LR ISAR image is represented sparsely with a LR dictionary, a HR ISAR images can be reconstructed based on a HR dictionary owing to the similar sparse representation coefficients. Experiment results with simulation data demonstrate the superior performance of the proposed method over other classical dictionary training algorithms. [ABSTRACT FROM AUTHOR]

The development of radar signal simulator in specific environment can effectively accelerate the debugging process of radar system and reduce the consumption of human, material, and financial resources. This study analyses the mathematical model of the IF radar signal in aerial distributed semi-active radar system and deduces the parameters of radar signals based on direct digital synthesiser (DDS) technology. After solving the problem of multi-channel synchronisation, delay precision control, and envelope delay compensation, an IF radar signal simulator system was designed and implemented, which can generate three direct-path-wave signals and three echo signals. [ABSTRACT FROM AUTHOR]

To remove the restrictive condition of v (the speed of moving objects) being far less than c (the spread speed of emission signals), the analysis on the echo signals of the moving objects by the narrowband model must satisfy the restrictive condition. Using Doppler Principle, this paper presents the broadband model of the echo signals of moving objects. The results of research indicate that it can quickly get the speed of moving objects and the distance between the motional object and the wave source when the emission wave transmits by self wavelet transform of the wideband model of the echo signals of motional objects. The simulations by using Matlab software show that this method removes the restrictive condition, and it’s feasible on common radar signals. [ABSTRACT FROM AUTHOR]

Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

In this research the order selection of autoregressive model parameters for classification of signals from the ground surveillance radar audio-output is considered. For this purpose, a measure based on maximal separability between used radar classes (clutter, person walking, person running, group of persons walking, group of persons running, vehicle) is suggested. Determined order of the autoregressive model is compared to the information criterion proposed by Akaike for different used windows. After the feature reduction of used real radar Doppler echo signals, it is showed that the proposed measure determines as optimal significantly lower order with a higher separability between used classes of radar targets.

Pulse compression technique allows a radar to achieve the resolution of a short pulse and the energy of a long pulse simultaneously, without the requirement of high-power transmission. Therefore, pulse compression radars have a low probability of intercept capability. The common types of pulse compression signals are frequency modulated waveforms and phase-coded waveforms, which have different properties. The optimum radar signal should have good immunity against deceptive jamming, good Doppler tolerance to detect high-speed targets, and low time-sidelobe level to detect weak targets nearby the strong ones. This paper reviews the current research in the commonly used radar signals, and presents their pros and cons, and compares between them in terms of Doppler tolerance, time-sidelobe level, as well as immunity against jamming in order to provide a reference for the researchers in the field of radar systems and electronic warfare. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic characterization of a multilevel (ML) Chireix outphasing (ML-CO) power amplifier (PA) with modulated signals. The ML-CO technique combines the advantages of envelope tracking and outphasing architectures by limiting the supply modulation to discrete levels with an efficient power-DAC modulator and using outphasing for fine amplitude control. We describe an experimental test bench that supplies the required phase- and time-aligned modulated signals for outphasing and supply modulation simultaneously. Pulsed characterization is used to design an ML memoryless polynomial DPD. The linearized ML-CO GaN X-band MMIC PA is demonstrated with fixed input drive levels for 9.3-dB peak-to-average power ratio (PAPR), 1.4-MHz and 11.3-dB PAPR, 10-MHz LTE signals with a 9.7-GHz carrier. For both signals, the average total power consumption is reduced by a factor of two when supply modulation is used. An investigation of control signal generation strategies is presented, including combinations of discrete supply and continuous input modulation. A simple exponential function tracks the optimal efficiency trajectory of the ML-CO PA, enabling overall efficiency improvements within 9 and 27 percentage points. This control strategy is applied to radar pulses with amplitude shaping and frequency chirp, with the efficiency improvement of up to 11.8 percentage points compared with constant-supply operation, with 29-dB improvement in spectral confinement. [ABSTRACT FROM PUBLISHER]

In through-the-wall radar imaging, compressed sensing techniques have been applicable for small amount of observations to reconstruction walls or targets imaging in the scenes. The authors consider building walls imaging. The proposed approach exploits a low-rank and sparse model to extract the wall returns from radar signals, and then minimises the total variation regularisation problem for reconstructing building walls imaging using the sparsity of their spatial variations. The results of simulation exhibit that the proposed approach effectively enhances the imaging quality of building walls. [ABSTRACT FROM AUTHOR]

This paper studies probability density estimation on the Siegel space. The Siegel space is a generalization of the hyperbolic space. Its Riemannian metric provides an interesting structure to the Toeplitz block Toeplitz matrices that appear in the covariance estimation of radar signals. The main techniques of probability density estimation on Riemannian manifolds are reviewed. For computational reasons, we chose to focus on the kernel density estimation. The main result of the paper is the expression of Pelletier's kernel density estimator. The computation of the kernels is made possible by the symmetric structure of the Siegel space. The method is applied to density estimation of reflection coefficients from radar observations. [ABSTRACT FROM AUTHOR]