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Radio Detection and Ranging (RADAR) equipment is a significant information and navigational system onboard vessels and a critical part of a ship's cyber space. It is an electronic system used not only for detecting surrounding objects, to indicate their positions, and tracking targets using radio waves, but also providing safe navigation by receiving and displaying data from other navigational devices. Therefore, it is concerning to see that marine RADAR systems have various cyber vulnerabilities, including data deletion and data relocation. These systems can be manipulated and penetrated via malicious software, unauthorised remote access, human error, or sabotage by internal and external attackers. This is critical to the cyber hygiene of the ship, which affects its reliability and safety. This study performs a cyber risk assessment using the CORAS framework for RADAR cyber security by developing case-based RADAR cyber scenarios in terms of both its specific information technology subsystems and the cyber security control measures. The output of this study includes a holistic and visual assessment of RADAR's cyber security for both its cyber vulnerabilities and cyber hygiene to better protect shipboard RADAR in the future. [ABSTRACT FROM AUTHOR]

Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder–decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder–decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition. [ABSTRACT FROM AUTHOR]

Radar signal recognition under low signal-to-noise ratio (SNR) conditions is a critical issue in modern electronic reconnaissance systems, which face significant challenges in recognition accuracy due to signal diversity. A novel method for radar signal detection based on the bagging support vector machine (SVM) is proposed in this paper.This method firstly utilizes the Choi–Williams distribution (CWD) and the smooth pseudo Wigner-Ville distribution (SPWVD) to obtain different time–frequency images of radar signals, which effectively leverages CWD's strong time–frequency aggregation and SPWVD's robust cross-interference resistance. Moreover, histograms of oriented gradient (HOG) features are extracted from time–frequency images to train multiple SVM classifiers by bootstrap sampling. Finally, the performance of each SVM classifier is aggregated using plurality voting to reduce the risk of model overfitting and improve recognition accuracy. We evaluate the effectiveness of the proposed method using 12 different types of radar signals. The experimental results demonstrate that its overall identification rate reaches around 79% at an SNR of −10 dB, and it improves the recognition rate by 5% compared with a single classifier. [ABSTRACT FROM AUTHOR]

In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models. [ABSTRACT FROM AUTHOR]

By going through a history of electronic visuality, from fluorescence glows in European laboratories in the nineteenth century to the computer screen in the twentieth century, this paper discusses technical image operations in the interaction between media machines and media people. Examining this set of apparatus from the evolution of a neglected technical object – the vacuum tube, it traces the history of the screen and the temporal-spatial composition of electronic graphics in television, radar, and early computer systems. In doing so, it outlines the entangled history of analog and digital displays and demonstrates the impossibility of neglecting the role of the human observer in the technical invention of visuality. [ABSTRACT FROM AUTHOR]

SAR images are used in many applications such as building detection. Extracting the building is very challenging due to the radar nature of the SAR images. However, due to the advantages of radar images such as day and night imaging, building extraction from SAR images is a hot topic. In this context, one of the main challenges is the effect of building orientation on the profile created in the SAR image. Also, the two geometric distortions of shadow and layover affect the SAR image. In most building extraction methods, shadow and double bounce are used as two main parameters in building detection. In this paper, different morphological profiles for detecting shadow index and double-bounce index (DMPSIDI) method have been developed using its combination with the method based on statistical features for building extraction. The DMPSIDI method is a morphological-based method that extracts buildings from SAR images independent of changing their profile. The proposed method is also robust to different data using weighting in the main parameters of shadow and double bounce. [ABSTRACT FROM AUTHOR]

Belligerent sides of current military conflicts are first and foremost counting on intelligence, air supremacy, and electromagnetic spectrum dominance. Actual concepts of conducting military operations can be characterised by the massive employment of air defence systems, radars, and electronic warfare systems. An approach is aimed to strengthen air‐defence potential by improving the resilience of pulse radar against electronic warfare threats, such as an electronic attack. To cope with the above‐mentioned situation, a radar intrapulse modulation was designed, where a combination of Costas codes and Walsh‐Hadamard codes is applied. Using a particular waveform together with the proposed model for radar signal processing results in increased resilience against an electronic attack, especially against repeater jamming methods. Furthermore, it is also possible to process radar echoes at low levels of Signal‐to‐Noise Ratio (SNR), which enhances radar electronic counter–countermeasure capability. The main scientific contribution of the paper is in the unique design of the Pulse Compression Filter for Combined Intrapulse Modulation (PCF‐CIM) and in its modelling. Based on the statistical evaluation of results, using the proposed radar waveform together with PCF‐CIM, it is possible to detect radar signals with combined intrapulse modulation in the case of SNR ≥ −14 dB with TPR ≥0.9999 and FPR ≤10−6, where SNR stands for Signal‐to‐Noise Ratio, TPR stands for True Positive Rate, and FPR stands for False Positive Rate. [ABSTRACT FROM AUTHOR]

The radome can protect the enclosed antenna from harsh weather and environmental conditions; however, it will cause a non‐negligible effect particularly transmission loss on the wideband signal which has been widely used in electronic systems. For this, a novel transmission loss compensation method for radome based on non‐linear frequency modulation (NLFM) waveform design is proposed. First, the transmission coefficient calculation process is given, Then, the detailed NLFM waveform design method is described in detail. Finally, the simulation experiments are demonstrated to verify the effectiveness of the proposed compensation method. Based on the designed NLFM waveform, the transmission loss can be compensated and a flat frequency response can be obtained, which can achieve excellent performance of pulse compression in the range direction. The proposed NLFM design method has great potential application value in the electronic system. [ABSTRACT FROM AUTHOR]

Due to the technical barriers between radars and jammers and the poor performance of the traditional detection‐jamming shared signal in integrated radar‐electronic warfare systems, a new detection‐jamming shared signal waveform based on the virtual force field algorithm (VFFA) is proposed in this article. First, a multiobjective and multidimensional characteristic parameter optimization model, based on a virtual force field, is established, and then the design principle of the shared signal is presented in detail. The simulation results show that the detection‐jamming shared signal based on the VFFA presents the deceptive jamming of multiple false targets in a noncollaborative target's radar screen. Further, there is better detection performance with the advantages of multiple pulse repetition frequency (PRF) and pulse accumulation numbers, which are highly sensitive to the multijagged PRF signals emitted by the noncollaborative target. According to the VFFA described in this article, the optimum detection‐jamming shared signal waveform can be output in real time for specific free space targets, to improve the efficiency of integrated radar and electronic warfare systems. [ABSTRACT FROM AUTHOR]

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of noncooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This article proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). First, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e., to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the nonadjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly nonideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multivariate input conditions. [ABSTRACT FROM AUTHOR]

Radar emitter classification (REC) plays an increasingly significant role in the electronic reconnaissance system. Due to there are many unreliable factors in traditional feature-based methods, this article focuses on improving the accuracy and stability of REC based on multipaths of original radar signals. A novel aggregated multipath extreme gradient boosting (AMP-XGBoost) is therefore put forward. Multi-path, including multi-scale, multi-domain transformations and their concatenations, is developed to exploit more information of the original signal, which contributes to providing more distinguishable features. XGBoost is used to automatically extract the features contained in these paths and complete recognition. Finally, multiple paths are sifted and aggregated according to certain weights to obtain a higher accuracy. Experiments are carried out based on signals from 6 different intra-pulse modulation mode radars with the same radio frequency (RF) range. It is demonstrated that the proposed method has higher REC accuracy than the methods only based on a single scale or domain. In the meantime, experimental results under different low SNRs show that the proposed method has higher stability. Finally, experiments based on the measured aviation radar data proves the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Radar radio source (RRS) recognition plays an important role in the fields of military electronic support systems (ESM) and civilian autonomous driving. The rapid development of machine learning technology, especially deep learning, has effectively and efficiently improved RRS intelligent recognition performances when operating in the increasingly complex electromagnetic environment. However, the data sampling limitation and computation cost are still severe challenges in real RRS recognition scenarios. In this paper, we propose a novel network based on meta-transfer learning, called RRSARNet, to achieve effective adaptive RRS recognition in the context of low signal-to-noise ratio (SNR). First, by using the short-time Fourier transform, a six-type small samples RRS simulation dataset with different SNR levels is constructed. Then, a novel RRSARNet, based on metric learning, is proposed, which consists of a four-layer embedding module and a four-layer relational module. Finally, the RRS dataset is divided into training, supporting and testing subsets, which are used to train and test the RRSARNet in a meta-transfer learning method. Experiments on the RRS dataset show that the proposed RRSARNet can achieve an overall accuracy (OA) above 96% and 99% when the SNR is above −15 dB and −10 dB, respectively. Even when the SNR is -30 dB, OA can reach more than 70%. For 5-way 1-shot and 5-way 5-shot experiments, the inference time of an image is about 0.043 and 0.140 milliseconds, respectively. Besides, experiments on the RRS simulation dataset and the two benchmark datasets, the RRSARNet performs better or more competitive than many existing state-of-the-art technologies in terms of recognition accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to its low hardware complexity, small volume and simple structure, time‐division‐multiplexing multiple‐input‐multiple‐output (TDM MIMO) radar has been widely applied in automotive applications. The transmitting antennas of TDM MIMO automotive radar are usually switched according to a sequential‐TDM pattern. However, moving targets can introduce a motion‐induced phase into the sequential‐TDM pattern, which is coupled to the spatial phase, resulting in errors in velocity and azimuth estimation. Herein, a random‐TDM pattern with the matrix completion (MC)‐based estimation method is proposed to address these issues. In the proposed method, the random‐TDM pattern, which means randomly activating the transmitting antenna instead of sequentially, can decouple the linear temporal‐spatial coupling phase, avoiding the coupling problem in velocity and azimuth estimation. Then, by reshaping the echo data into a matrix form of sparse sampling, the inexact augmented Lagrange multiplier algorithm is adopted to recover this matrix, solving the underdetermined estimation problem caused by sparse sampling. Finally, the high‐accuracy velocity and azimuth can be jointly estimated by applying the estimation of signal parameters via rotational invariance technique algorithm to the recovered full sampling data. Moreover, compared with the compressed sensing‐based method, the proposed method overcomes the grid mismatch problem. The results of comparative simulations and real‐data experiments demonstrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Waveforms used in multiple‐input multiple‐output (MIMO) systems must be considered in such a way that individual transceiver (T‐R) pairs can be separated from the composite received signal. An algorithm called variational mode decomposition of coded beat‐frequency shifted signals (VMD‐CBFSS) is presented for waveform diversity. First, transmitters are divided into several groups. A small shift is applied to the carrier frequency of the transmitters of different groups. Also, signals within each group are encoded by assigning a binary phase in a time block. On the receiver side, by using a multiresolution analysis technique, the combined signal is decomposed into its main frequency components and then the composite signals of each group are decoded to retrieve each T‐R pair. VMD‐CBFSS provides an efficient solution in terms of overall transmitting time and sampling rate in addition to robustness against channel noise. Moreover, the algorithm is employed in a near‐field MIMO millimetre‐wave imaging system, and results are presented in the form of numerical simulations. [ABSTRACT FROM AUTHOR]

To improve the space‐time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN) covariance matrix of cell under test (CUT) data is estimated by the selected target‐free training samples around the CUT with the sample covariance matrix method. Then, with the component decompositions of the selected training samples and the assumption of approximately equal subspace, the IPN covariance matrix of CUT data is reformulated by the eigenvector matrix, eigenvalue matrix, and the eigenvalue reweighting vector. Subsequently, based on the modified covariance matching estimation criterion, the eigenvalue reweighting vector is estimated by solving the redesigned convex optimisation problem with the Lagrange dual method. Finally, the STAP weight vector is calculated to process the CUT data. The proposed algorithm can obtain a relatively accurate IPN covariance matrix of CUT data by sufficiently utilising the non‐homogeneous training samples and can effectively protect the moving targets in CUT data, which can be applied to airborne radar with arbitrary array structure and antenna configuration. Simulation results and performance analyses based on the multi‐channel airborne radar measurement data demonstrate the effectiveness of the proposed GERCM algorithm. [ABSTRACT FROM AUTHOR]

A multistatic passive radar network originally presented in Ptak et al. for long‐distance aircraft tracking is sensitive to track initialisation errors. When these errors exceed 50 km, target tracking often diverges. Initialisation can be improved by even coarse directional information. The impact of binary directional information was studied, which allows flights to be assigned to start from east or west of the multistatic network. Substantial improvement to the performance of flight tracking is observed on reference data based on Automatic Dependent Surveillance–Broadcast information. [ABSTRACT FROM AUTHOR]

The target classification of unknown classes using radar sensor data is discussed. The neural network‐based classifier shows high classification accuracy for the learned class targets. However, there is a risk of false decision for the untrained class target owing to an overconfidence problem. The output confidence of the classifier is calibrated using the deep convolutional neural network ensemble structure to propose a method to set the proper threshold for output confidence to decide unknown class targets. When using the proposed method, the accuracy of the learned target is maintained similar to that of the existing single neural network‐based classifier, whereas the unknown class target is better identified. Further analysis verifies the effectiveness of the proposed method using commercial automotive radar. The proposed method can classify learned targets with an accuracy of 95% and distinguish unknown class targets with an accuracy of at least 85%. Based on the interaction with other sensors, individual sensors need to make reserved decisions about uncertain information. It is expected that the proposed ensemble network will be efficient in designing the classifier to perform target classification including unknown class decision. [ABSTRACT FROM AUTHOR]

Sparse recovery (SR) based space‐time adaptive processing (STAP) methods have received much attention recently due to their dramatically reduced requirements of training samples. However, most of the existing SR‐STAP algorithms suffer from the off‐grid effect induced by the discretisation of angle‐Doppler plane. To eliminate this effect and improve the performance of clutter suppression, a novel gridless mixed‐norm minimisation (MNM) based STAP algorithm is proposed. Unlike previous grid‐based SR‐STAP methods, the clutter covariance matrix (CCM) is estimated in continuous domain by solving a semidefinite programme (SDP), which is established by utilising the properties of the CCM and the compact formulation of MNM. Furthermore, to reduce the computational burden of solving this SDP, a fast iterative algorithm via the framework of the alternating direction method of multipliers (ADMM) is also derived, where the unknown parameters are iteratively updated with closed‐form expressions. Simulation results show that our proposed algorithm achieves better performance of clutter suppression and target detection with lower computational complexity. [ABSTRACT FROM AUTHOR]

This study addresses a signal processing technique in a search and rescue system. An international disaster rescue communications support programme such as COSPAS‐ search and rescue satellite‐aided tracking (SARSAT) estimates the position of a beacon based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) techniques. To backtrack signals from encrypted beacons without knowing the encryption codes, the cross ambiguity function (CAF) is proposed to estimate the TDOA and FDOA, in which high sampling rates are needed to increase the positioning accuracy of existing CAF algorithms, which entail high calculation loads. To overcome this, an interpolation method is proposed for peaks of Fourier transform capable of maintaining high accuracy at low calculation loads after a well‐performed initial acquisition. The numerical simulation shows an improved estimation accuracy of the proposed algorithm compared with existing peak detection methods. [ABSTRACT FROM AUTHOR]

A large number of initialised false tracks in clutter environments will cause a heavy burden on the detection and track‐processing system. The quantity of initialised false tracks in clutter used to be estimated by experience or simulation, which lacks a fundamental explanation in theory. In order to estimate the amount of linear initialised false tracks with analytical expressions, statistical estimators are proposed based on prior knowledge of the clutter in a two‐dimensional scenario. Meanwhile, the differential Hough transform is presented to verify the estimators. To be specific, firstly, a one‐dimensional scenario is illustrated to present the statistical estimators of the number of initialised linear and equal‐length false tracks, which is based on statistical estimation theory. Secondly, regarding the clutter map as a uniform distributed binary image, the estimators described at first are extended to a two‐dimensional situation. Finally, to verify the validity of the proposed estimators, a differential Hough transform algorithm is utilised to obtain the number of linear and equal‐length tracks in the simulation. The results show that the estimators proposed herein are reasonable in a certain clutter environment. [ABSTRACT FROM AUTHOR]

A micro‐Doppler frequency estimation and association algorithm of the space target based on the time‐varying autoregressive (TVAR) model is proposed herein. The calculation time of the existing algorithms grows significantly with the problem size, and micro‐Doppler frequencies of different scattering centres are associated incorrectly where the frequencies intersect. The echo of the space target is modelled by the TVAR model, then the TVAR coefficients and instantaneous frequencies are estimated through the fast sparse Bayes learning (SBL) algorithm, finally, the micro‐Doppler frequencies of different scattering centres are associated in the frequency–frequency change rate domain. The effectiveness and rapidity of the proposed algorithm is demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

The optimal implementation for a biologically inspired coupling structure to overcome the limitation of short baseline direction‐finding is determined. This approach is inspired by the Ormia ochracea, a parasitoid insect living in North America. It can locate the crickets' call accurately with the very small distance between its ears far beyond the accuracy of an interferometer with the same baseline. This outstanding performance depends on the mechanical coupling in its auditory system. The first research focus is on the mechanism of the coupling structure, considering not only the amplification on phase difference but also the effect on output power, which leads to the performance improvement in comparison with the traditional method. Then, the biologically inspired coupling structure is optimised to achieve the best direction‐finding performance in crickets' sound frequency, reducing the estimation error by 75% when the signal incident at boresight. To implement the actual coupling structure with optimal direction‐finding performance, both the analogue circuit and the digital filter implementation are discussed, and the latter attains the theoretical optimal performance. Finally, a direction‐finding system prototype is carried out to verify the advantage of digitally implemented coupling structure, and the measurement result approximates the corresponding Cramér–Rao lower bound. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) with digital beam‐forming (DBF) in elevation uses very sharp time‐variant antenna beams to improve the signal‐to‐noise ratio (SNR) and range ambiguity‐to‐signal ratio. In acquisition scenarios of imprecisely known Earth topography, however, antenna pointing might be mismatched with surface geometry. To handle this issue, it is suggested that preprocessing is applied to DBF to determine the angle of the incident echo signal on the planar antenna array. The authors derive a method that estimates the direction of arrival on the acquired raw data with high accuracy in real time. The method is based on the matrix pencil, which is a common approach in mathematics for solving linear equation systems. As a key innovation, this paper describes further improvements and adaptions to SAR scenarios and provides a possible concept for practical implementation. For proof of concept, a series of different simulation tasks is performed. [ABSTRACT FROM AUTHOR]

Deep‐learning‐based radar imaging is developed with distributed frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radars in which a deep‐learning approach based on the convolutional neural network (CNN) is proposed to achieve radar images robust to adverse circumstances. Differently from the existing deep‐learning methods applied to radar object recognition, the deramped radar signal is exploited as the input of the proposed deep CNN (DCNN) without any processing related to the spectrogram transform and the subspace decomposition. To effectively train the proposed DCNN, the received signal is reformulated in terms of the reflection gain values in the (azimuth, range) patches in the image region of interest such that the output vector of the DCNN is composed of the reflection gain values in the associated patches. Furthermore, to overcome the limitations on the amount of training data and training time, the transfer learning approach is effectively applied to the distributed FMCW MIMO radar imaging. The proposed radar imaging is assessed with synthetic simulation data. Specifically, by transferring the pretrained DCNN model for a given reference radar to other distributed radars, the distributed radars can save about 52.4 % in training time compared with a DCNN having the same architecture but without transfer learning. [ABSTRACT FROM AUTHOR]

The issue of aircraft tracking using passive sensors with poor measurement precision and a variable update rate is investigated. First, the need for a time‐dependent Markov chain transition matrix for the Interacting Multiple Model (IMM) is thoroughly evaluated using simulation and real data from an experimental multilateration system. Three different methods for computing the time‐dependent matrix are compared, showing their superiority for highly aperiodic sensors. Second, the Variable Structure Interacting Multiple Model (VS‐IMM) for passive sensors with poor geometry is proposed, in which the mode set update is based on local dilution of precision or distance root mean square error. The performance of the estimator is compared with the IMM using real data. The VS‐IMM results in the best performance for areas of both high and low measurement precision. [ABSTRACT FROM AUTHOR]

Two‐dimensional (2‐D) transmit arrays offer many advantages over traditional over‐the‐horizon radar (OTHR) arrays, including the capability to steer and mitigate auroral clutter, a clutter type prevalent in polar operation, in all azimuth and elevation directions. The approaches of previous studies on OTHR 2‐D transmit arrays are considered suboptimal, but it can be shown that the 2‐D array is an effective OTHR transmitter when operated in an appropriate manner. A reference system derived from publicly available information on an operational OTHR array is used as a benchmark to compare the performance of various 2‐D OTHR transmit systems with respect to effective isotropic radiated power on target, sidelobe levels, and elevation selectivity. [ABSTRACT FROM AUTHOR]

Airborne multiple‐input multiple‐output (MIMO) radar can transmit orthogonal waveforms to increase the range of the detection target. However, in a multipath environment, airborne MIMO radar also has energy radiated to the target through multipath directions, which makes the multipath phenomenon of airborne MIMO radar complicated. Because the direct path signal and the multipath signals of the airborne radar are at different range gates, the characteristics of data between the direct path signal and the multipath signals of the airborne MIMO radar can be used to propose a method for estimating the reflection coefficient amplitude (RCA). Considering that the target echoes data at different range gates can be used to estimate the elevation angle of the target, a direction of arrival (DOA) estimation method using data fusion at low signal‐to‐noise ratio (SNR) is proposed. Moreover, at high SNR, the signal path corresponding to the maximum target echo power is selected for DOA estimation based on the estimated multipath RCA. The proposed method makes full use of all target echo data of airborne MIMO radar in a multipath environment and is suitable for practical applications. Finally, simulation experiments verify that the proposed method can effectively improve the DOA estimation accuracy. [ABSTRACT FROM AUTHOR]

Mutual interference in automotive radar is expected to become a major issue owing to the rapid increase in the number of vehicles on the road equipped with radar. The phenomenology of interference in frequency modulated continuous wave radar is presented. Interference is empirically analysed at every signal processing stage in the victim radar by means of experimentally verified simulation modelling. Knowledge of how interference manifests in different domains provides a useful tool to develop algorithms for interference detection, mitigation and/or avoidance. The receiver's filter response is analysed to minimise the interference duration and increase the effectiveness of time‐domain mitigation techniques. A innovative method of interference parameter extraction by using spectrograms is also introduced. [ABSTRACT FROM AUTHOR]

Radar Emitter Individual Identification is a key technology in modern electronic radar systems. This paper will focus on Radar Emitter Individual Identification (REII). Based on the advantages of Empirical Mode Decomposition (EMD) and bispectrum in signal processing, we propose an REII method based on the CNN. Firstly, the radar emitter signal is preprocessed. Secondly, the Hilbert–Huang Transform (HHT) spectrum and bispectrum are combined to form an image of the signal. Finally, in order to avoid loss of information and achieve the potential identification performance improvement, the signal image obtained is identified by the optimized CNN. Experimental results based on the measured signals show that the proposed method has high identification accuracy and is capable of meeting real-time identification requirements. The deep-learning-based identification method proposed in this paper has strong generalization ability and adaptability, which provides a new way for REII. [ABSTRACT FROM AUTHOR]

Existing algorithms for radar waveform classification currently exhibit the lower recognition accuracy, especially at the lower signal to noise ratio (SNR) environment. To remedy these flaws, this paper proposes an accurate automatic modulation classification algorithm based on dense convolutional neural networks (AAMC-DCNN). The algorithm owns the competitive advantages of strengthening the feature reuse and extracting the detailed feature, for improving the recognition performance of radar waveform at the lower SNR. First, the dense convolutional neural networks (CNN) are designed, which connects each layer to every other layer in a feed-forward pattern. In the latter, 8 types of signals are converted into time-frequency images by choi-williams distribution (CWD), and the large training and testing datasets are fabricated. Then, the transfer learning and Adam optimization are introduced. Finally, the experimental analyses are carried out to evaluate the recognition performance. It is worth mentioning that the classification accuracy can be up to 93.4% when the SNR is −8 dB, and even reach to 100% at 0 dB, which demonstrates the superior performance over others. The present work provides a sound experimental basis for further studying automatic modulation classification for the sake of future field application in electronic warfare systems. [ABSTRACT FROM AUTHOR]

This research note identifies the precursors for development of shipborne radar for commercial and military applications. It comprises three main sections: first, the evolution of radar starting from the first practical demonstrations provided by Russian professor Aleksandar Stepanovich Popov in 1897; second, the invention of radar in eight nations; and third, early experiments with shipborne radars up to 1939. [ABSTRACT FROM AUTHOR]

This research note outlines advances in the development of shipborne radar in Britain, Germany, the US and the Soviet Union. It focuses on the inventions and innovations in electronic and radars techniques for military and commercial applications on the eve of the Second World War, during the war and in the post-war period. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is−6 dB. [ABSTRACT FROM AUTHOR]

In order to investigate the buried landscape at the Fauresmith locality of Bestwood 1, outside the town of Kathu in the Northern Cape Province, we performed ground-penetrating radar and magnetometry surveys across the sand-filled central portion of the valley. The radar images a strong continuous reflector which we can assign to the boundary between the Kalahari sands and underlying Banded Ironstone Formation gravels. Moreover, the thickness of the sand delineates a buried depression in the centre of the valley with flat plateaus at the sides. Subtracting the sand thickness from the current topography produces a map of a small stream channel in the northern part of the valley. Analysis of the magnetic gradient data allows us to extend this buried channel further to the south. Our geophysical survey provides a valuable contribution towards understanding the context of hominin occupation along the banks of a small stream in the Kathu Complex. [ABSTRACT FROM AUTHOR]

The topic of designing sequence set with good correlation properties has received considerable attention due to its numerous applications in many active sensing and communication systems. For example, a multiple-input multiple-output radar system transmits orthogonal waveforms or zero correlation zone sequences to achieve a long virtual aperture, which results in enhanced parameter identifiability and improved resolution. However, it is difficult to design sequence set with accurately controlled correlation properties due to numerous nonconvex and highly nonlinear inequality constraints. In this paper, new sequence set design methods, based on the local integrated sidelobe level and the local peak sidelobe level metrics, are proposed to tackle the optimization problems under the nonconvex and nonlinear inequality constraints to meet the correlation requirements. Numerical examples are provided to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In a low-angle target parameter estimation scenario, the backscattered signals from targets are often distorted due to clutter and multipath, which significantly degrades the performance of direction-of-arrival (DOA) estimation. In general, the backscattered multipaths are modeled as coherent signals with respect to the direct path. Decorrelation algorithms such as spatial smoothing and matrix reconstruction can mitigate the effect of multipath. However, most of these methods will perform poorly or even fail for solving parameter estimation problem of low-angle targets in complex terrain where there are rich multipath scatterings. This paper presents a novel method that combines time reversal (TR) technique, coherent signal-subspace method (CSM), and multiple signal classification algorithm to perform DOA estimation in a low-angle target scenario. The TR technique takes advantage of multipath echoes recorded by a sensor array and adaptively adjusts TR probing waveforms to increase the signal-to-noise ratio in a rich scattering environment. Moreover, the CSM method compresses the energy of a signal into a predefined subspace to exploit the full time-bandwidth product of signal sources and cope with coherent wavefronts. As a result, the proposed new DOA estimation algorithm outperforms significantly the conventional methods. The Cramer–Rao bounds analysis and numerical simulations commendably validate the accuracy and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Mutual interference between automotive radar sensors is becoming a major concern due to the rapid increase of vehicles equipped with such systems. While there has been a plenty of studies on the interference of frequency modulated continuous wave (FMCW) radars, no work on chirp sequence (CS) radars has been reported in the literature in spite of their growing popularity in the automotive field. In this regard, this work presents an investigation of mutual interference for automotive CS radars. We analytically derive formulas describing the probability of ghost target appearance, and the signal-to-interference mitigation gain for interference of different waveforms, including continuous wave, FMCW, and CS waveforms, with comparison to an equivalent FMCW radar model. The derived formulas on the signal-to-interference mitigation gain are also verified by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, the design of concentric circular ring slot patch antenna with defected ground structure (DGS) is presented. The rectangular patch antenna is slotted with two concentric circular rings on radiating patch. The two diamond shaped patch is added to the microstrip-feed-line. The DGS is considered for obtain dual bands at 6.6 and 9.4 GHz with return loss − 14.7 and − 20.9 dB to operate for satellite communication and radar applications. The VSWR is less than 2 and axial ration is less than 3 dB for these two bands are obtained of proposed antenna. The surface current distribution is presented for analyzing the effect of the slots. It is observed that the simulation and measured values are having good agreement. [ABSTRACT FROM AUTHOR]

Classical stretch processing (SP) obtains high range resolution by compressing large bandwidth signals with narrowband receivers using lower rate analogue-to-digital converters. SP achieves the resolution of the large bandwidth signal by focusing into a limited range window, and by deramping in the analogue domain. SP offers moderate data rate for signal processing for high bandwidth waveforms. Furthermore, if the scene in the examined window is sparse, compressive sensing (CS)-based techniques have the potential to further decrease the required number of measurements. However, CS-based reconstructions are highly affected by model mismatches such as targets that are off-the-grid. This study proposes a sparsitybased iterative parameter perturbation technique for SP that is robust to targets off-the-grid in range or Doppler. The error between reconstructed and actual scenes is measured using Earth mover's distance metric. Performance analyses of the proposed technique are compared with classical CS and SP techniques in terms of data rate, resolution and signal-to-noise ratio. It is shown through simulations that the proposed technique offers robust and high-resolution reconstructions for the same data rate compared with both classical SP- and CS-based techniques. [ABSTRACT FROM AUTHOR]

Radar tracking of aircraft targets at low elevation angles can be complicated by clutter from wind turbines, which we define to be clutter. In this paper, we study detection in a staring pulse-Doppler radar. By exploiting the second-order correlation structure of this wind-turbine clutter we derive a target detector that uses a sequence of adaptive coherence scores. The detector uses a multipulse coherence statistic consisting of an incoherent geometric average of coherently computed adaptive coherence scores. The detector compares favorably to a multipulse coherence detector that uses no modeling or estimation of the clutter. [ABSTRACT FROM PUBLISHER]

The paper proposes a cooperative scheme for the coexistence of a multiple-input-multiple-output (MIMO) communication system and a matrix completion (MC) based, collocated MIMO (MIMO-MC) radar. To facilitate the coexistence, and also deal with clutter, both the radar and the communication systems use transmit precoding. For waveform flexibility, the radar uses a random unitary waveform matrix. We prove that for such waveforms and any precoding matrix, the error performance of MC is guaranteed. The radar transmit precoder, the radar subsampling scheme, and the communication transmit covariance matrix are jointly designed in order to maximize the radar SINR, while meeting certain communication rate and power constraints. The joint design is implemented at a control center, which is a node with whom both systems share physical layer information, and which also performs data fusion for the radar. We provide efficient algorithms for the proposed optimization problem, along with insight on the feasibility and properties of the proposed design. Simulation results show that the proposed scheme significantly improves the spectrum sharing performance in various scenarios. [ABSTRACT FROM AUTHOR]

Extended binary phase shift keying/M-ary position phase shift keying (EBPSK/MPPSK)-MODEM provides radar and communication functions on a single hardware platform with a single waveform. However, its range estimation accuracy is worse than continuous-wave (CW) radar because of the imbalance of power in two carrier frequencies. In this article, the power allocation method for dual-frequency EBPSK/MPPSK modulated systems is presented. The power of two signal transmitters is adequately allocated to ensure that the power in two carrier frequencies is equal. The power allocation ratios for two types of modulation systems are obtained. Moreover, considerations regarding the range of operation of the dual-frequency system are analysed. In addition to theoretical considerations, computer simulations are provided to illustrate the performance. [ABSTRACT FROM AUTHOR]

The micromotion feature of space target provides an effective approach for target recognition. The existing micromotion feature extraction is implemented after target detection and tracking; thus the radar resources need to be allocated for target detection, tracking, and feature extraction, successively. If the feature extraction can be implemented by utilizing the target detecting and tracking pulses, the radar efficiency can be improved. In this paper, by establishing a feedback loop between micromotion feature extraction and track-before-detect (TBD) of target, a novel feature extraction method for space target is proposed. The TBD technology is utilized to obtain the range-slow-time curves of target scatterers. Then, micromotion feature parameters are estimated from the acquired curve information. In return, the state transition set of TBD is updated adaptively according to these extracted feature parameters. As a result, the micromotion feature parameters of space target can be extracted concurrently with implementing the target detecting and tracking. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An algorithm based on a support vector machine (SVM) is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively) and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather. [ABSTRACT FROM AUTHOR]

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface. [ABSTRACT FROM AUTHOR]