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Radar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness. [ABSTRACT FROM AUTHOR]

To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios. [ABSTRACT FROM AUTHOR]

Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short‐time Fourier transform time‐varying filtering, short‐time fractional Fourier transform time‐varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms. [ABSTRACT FROM AUTHOR]

Beamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters' outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam. [ABSTRACT FROM AUTHOR]

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use. [ABSTRACT FROM AUTHOR]

Nowadays, modern radar systems increase their target detection capabilities by processing pulses coherently. On the other hand, digital radio frequency memory‐based modern jammers have the ability to work coherently and can deceive radars even with a very low effective radiated power. These jammers, which have the capability of storing the radar's pulse, can use the previous pulses that they have stored in their memory during the electronic deception, without waiting for the last pulse of the radar, in other words, before the new pulse is received. If the radar does not change its parameters from pulse to pulse, such smart jamming techniques applied in this way can be very effective. In this article, the authors propose to use a smart binary phase‐coding method for pulse compression radar as an electronic protection technique against repeater jamming. This approach further improves the target detection capability of modern radar systems, which use coherent integration in the receiver. The proposed method can provide high protection against digital radio frequency memory‐based repetitive range deception techniques without compromising the radar's target detection capability. In the simulations, the traditional approach in which the same code is used without changing from pulse‐to‐pulse, and the approach using code sets obtained by the smart binary phase‐coding method in intra‐pulse modulation are compared. The results show that the proposed method can significantly improve the isolation against deception jamming and the target detection capability simultaneously. [ABSTRACT FROM AUTHOR]

Building on previous radar‐based human activity recognition (HAR), we expand the micro‐Doppler signature to 6 domains and exploit each domain with a set of handcrafted features derived from the literature and our patents. An adaptive thresholding method to isolate the region of interest is employed, which is then applied in other domains. To reduce the computational burden and accelerate the convergence to an optimal solution for classification accuracy, a holistic approach to HAR optimisation is proposed using a surrogate model‐assisted differential evolutionary algorithm (SADEA‐I) to jointly optimise signal processing, adaptive thresholding and classification parameters for HAR. Two distinct classification models are evaluated with holistic optimisation: SADEA‐I with support vector machine classifiers (SVM) and SADEA‐I with AlexNet. They achieve an accuracy of 89.41% and 93.54%, respectively. This is an improvement of ∼11.3% for SVM and ∼2.7% for AlexNet when compared to the performance without SADEA‐I. The effectiveness of our holistic approach is validated using the University of Glasgow human radar signatures dataset. This proof of concept is significant for dimensionality reduction and computational efficiency when facing a multiplication of radar representation domains/feature spaces and transmitting/receiving channels that could be individually tuned in modern radar systems. [ABSTRACT FROM AUTHOR]

Millimetre‐wave radar has been widely used in health monitoring and human activity recognition owing to its improved range resolution and operation in a variety of environmental conditions. With the MIMO antenna array, 4D radar is increasingly employed in autonomous driving, while its application in assisted living is recent and therefore the value added compared to the increase in signal processing and hardware requirements is still an open question. A model for 4D Time‐division multiplexing (TDM) multiple‐input‐multiple‐output (MIMO) frequency‐modulated Continuous wave radar is established using the human activities from the HDM05 motion capture dataset. The simulator produces an end‐to‐end simulation, including four human motions (jumping Jack, kick, punch, and walk), signal time of flight, noise, MIMO signal processing, and classification. Different pre‐processing and point cloud‐based methods are compared to obtain an average classification accuracy of 90% with PointNet. This study simulates a specific 4D TDM MIMO radar configuration to benchmark signal pre‐processing algorithms, which can also assist other researchers to generate range‐Doppler‐time (range‐Doppler time) point cloud data sets for human activities testing different radar configurations, array configurations, and activities saving valuable time in human resources and hardware development before prototyping to assess expected performances. [ABSTRACT FROM AUTHOR]

Short‐wave source localisation has an important role in many fields. A two‐stage iterative algorithm is proposed based on the ionospheric virtual height (IVH) model using the direction of arrival (DOA) and time difference of arrival (TDOA) measurements, which is divided into DOA stage and TDOA stage. The optimisation problem of the TDOA stage can be obtained by using the result of DOA stage and TDOA measurements. During the solution process, the non‐convex quadratic equation constraints are converted to linear constraints, which can lead to the algorithm converges to the global optimal solution and the optimal solution can be analytically solved. The theoretical analysis demonstrates that the localisation accuracy of both stages can reach the corresponding constrained Cramér‐Rao lower bound (CCRLB). And the accuracy of TDOA stage achieves the CCRLB of hybrid localisation. Finally, the performance of the proposed method is demonstrated by simulations. [ABSTRACT FROM AUTHOR]

Multiple‐Input‐Multiple‐Output (MIMO) radar has the characteristic of multiple antenna channels, bringing on a big data volume for signal processing. Therefore, the trade‐off between detection ability and computational efficiency is always considered. In this article, an optimisation system is proposed to enhance the real‐time performance of frequency division MIMO radar without compromising accuracy. For the scenario of low‐altitude small target detection, a signal processing acceleration method is proposed and a MIMO radar optimisation system based on graphics processing unit (GPU) architecture is built. The signal model of frequency division MIMO radar is first established, improving the classical signal processing flow efficiency from the perspective of reducing fast Fourier transform (FFT) times and windowing operation times. To achieve an advanced acceleration, the parallel architecture of ArrayFire‐library in GPU is then employed. Distinct minimum parallel units are extracted by analysing the principle of digital beamforming (DBF), pulse compression, moving target detection (MTD), and constant false‐alarm rate (CFAR) algorithms. And the corresponding parallel algorithms are designed to constitute a parallel acceleration system of frequency division MIMO. Simulation results indicate that the proposed method significantly improves the efficiency of MIMO system with a maximum acceleration ratio of 65 times, meeting the real‐time processing requirements. [ABSTRACT FROM AUTHOR]

This letter proposes a novel parallel sparse nested array and then provides a matrix reconstruction‐based method with three steps, to enhance the performance of direction of arrival (DOA) estimation in practice. The spacings between the closest antennas along x‐ and y‐axes, are coprime and much greater than half‐wavelength in the proposed array, which contributes to efficiently eliminate the mutual coupling and achieve larger aperture with limited physical antennas. By using the oblique projection operator with iteration, the proposed method can employ all items of covariance matrix of the received data to construct a virtual covariance matrix with increased degrees of freedom, for effectively improving angle estimation performance. Then the unique and high‐accuracy DOA estimation is obtained via the coprime‐ness. The simulation results show the superiority of the proposed array and method for DOA estimation in the presence of mutual coupling and limited antennas. [ABSTRACT FROM AUTHOR]

Millimetre‐wave radar offers high ranging accuracy and can capture subtle vibration information of the human heart. This study proposes a heartbeat prediction method based on the transformer model using millimetre‐wave radar. Firstly, the millimetre‐wave radar was used to collect the heartbeat data and conduct normalisation processing. Secondly, a position coding was introduced to assign sine or cosine variables to input data and extract their relative position relationship. Subsequently, the transformer encoder was adopted to allocate attention to input data through the multi‐head attention mechanism, using a mask layer before the decoding layer to prevent the leakage of future information. Finally, we employ the fully connected layer was employed in the linear decoder for regression and output the predicted results. Our experimental results demonstrate that the proposed transformer model achieves nearly 30% higher prediction accuracy than traditional long short‐term memory models while improving both the prediction accuracy and convergence rate. The proposed method has great potential in predicting the heartbeat state of elderly and sick patients. [ABSTRACT FROM AUTHOR]

The non‐stationary clutter phenomenon and the severe range ambiguity make space‐based early warning radar as an intricate model of received ground and sea clutter. The received clutter is severely broadened in Doppler dimension, and the degrees of freedom (DOFs) of the received clutter is severely increased. Because the DOFs of the system is always lower than the theoretical DOFs of the received clutter, the adaptive processing capability of the system is insufficient and the performance of clutter suppression is seriously degraded. A multi‐domain cascaded adaptive processing method is proposed in this study. The proposed method takes full advantage of the digital antenna array to suppress a portion of the clutter energy by introducing the transmit pulse coding domain, and the remaining influential clutter energy is suppressed by introducing the elevation‐dimensional receive channel domain. Finally, the clutter after the two‐dimensional (2D) pre‐processed is suppressed by the 2D adaptive processing in the azimuthal receive beam‐receive Doppler domain. This method pre‐processes the clutter data by extending the processing dimension to meet the freedom requirements of the adaptive signal processing, and the capability of clutter suppression is significantly improved. Simulation examples show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to quantitatively evaluate the influence of the aberration of the antenna radiation pattern of airborne radar on clutter feature, the range‐azimuth two dimensional (2D) model of the airborne radar clutter is established. Then the theoretic modelling and simulation analysis are proposed in three aspects, including the Doppler, power spectrum and space‐time 2D characteristics of the clutter. Finally, the processing of moving target indication, pulse Doppler and space‐time adaptive processing are used to suppress the clutter of the echo signals before and after the aberration. The results of the simulations indicate the aberration would elevate the sidelobe level, extend the power spectrum and bend the space‐time trajectory of the clutter Doppler spectra. And the residual clutter would be elevated nearly 5 dB after STAP processing. Then the pattern preserving and fast time cancel combined space‐time adaptive processing method is proposed, which can obtain narrow clutter notch and large improvement factor, and effectively suppress clutter. The modelling and quantitative analysis study have important theoretical and engineering significance for the detection capability of the airborne radar installed on the fuselage of the aircraft platform, the tactical and technical performance index, and improving the signal processing ability of airborne radar. [ABSTRACT FROM AUTHOR]

This letter formulates a high‐dimensional multi‐objective optimization model to minimize all correlation function values for multiple input multiple output (MIMO) radar waveforms. Then, to solve this problem, this letter proposes an adaptive weighted cyclic algorithm‐new (CAN), named p‐norm weighted cyclic algorithm‐new (p‐WeCAN). Specifically, the adaptive weight updating function is constructed using p‐norm to minimize peak side‐lobe level (PSL) and integrated side‐lobe level (ISL) simultaneously. Numerical results show that the p‐norm weighted cyclic algorithm‐new obtains similar PSL values as the best PSL minimization algorithm and low integrated side‐lobe level values close to its lower bound at the same time. [ABSTRACT FROM AUTHOR]

Digital programmable metasurfaces (DPMs) can dynamically modulate electromagnetic waves in space, time, and frequency domains with powerful capabilities. In contrast, joint radar and communication (JRC) is an important avenue to use the electromagnetic waves universally and intelligently. Hence, combining DPM and JRC may produce a promising way to achieve universal and intelligent electromagnetic terminals. Herein, a mechanism of extremely flexible JRC based on DPM is proposed, in which DPM can improve the flexibility of JRC in processing multidimensional information, and JRC endows DPM with the capabilities of perception, expression, and cognition to promote its intelligent level. Three kinds of functions are explored to interpret the mechanism. First, encryption and decryption of wireless secure communications are realized through DPM; second, range profile imaging is accomplished by using DPM‐based biphase‐coded signals; and third, wireless communication and radar detection are simultaneously presented using DPM. The combination of DPM and JRC greatly decreases the complexity of signal processing and simplifies the architecture of the JRC system, and promotes the intelligent level of DPM. [ABSTRACT FROM AUTHOR]

In the past few decades, the target location methods in transmit/receive‐receive (T/R‐R) synergetic High Frequency Surface Wave Radar (HFSWR) have received extensive attention from the signal processing community. However, there are few studies on the method of parameter pair‐matching for multi‐target scene. In this paper, a computationally efficient multi‐target pair‐matching method based on angle information only in T/R‐R synergetic HFSWR is developed. The proposed method is realised by dealing with three vectors composed of the first column, the first row and diagonal entries of the correlation matrix, instead of performing the singular value decomposition (SVD) of the correlation matrix. Simultaneously, the proposed method is a computationally efficient subspace algorithm, which only uses the angle information to complete the purpose of multi‐target pair‐matching. The simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Software defined radar (SDRadar) systems have become an important area for future radar development and are based on similar concepts to Software defined radio (SDR). Most of the processing like filtering, frequency conversion and signal generation are implemented in software. Currently, radar systems tend to have complex signal processing and operate at wider bandwidth, which means that limits on the available computational power must be considered when designing a SDRadar system. This paper presents a feasible solution to this potential limitation by accelerating the signal processing using a GPU to enable the development of a high speed SDRadar system. The developed system overcomes the limitation on the processing speed by CPU‐only, and has been tested on three different SDR devices. Results show that, with GPU accelerator, the processing rate can achieve up to 80 MHz compared to 20 MHz with the CPU‐only. The high speed processing makes it possible to run in real‐time and process full bandwidth across the WiFi signal acquired by multiple channels. The gains made through porting the processing to the GPU moves the technology towards real‐world application in various scenarios ranging from healthcare to IoT, and other applications that required significant computational processing. [ABSTRACT FROM AUTHOR]

As one of the key technologies of electromagnetic spectrum operations, radar waveform recognition is an important basis for judging the threat degree of enemy's weapons. At present, it is difficult to recognize the waveform at low signal‐to‐noise ratio (SNR), and the theoretical simulation is used in most research studies, which leads to the problem that the engineering practicality of the algorithm is poor. In this paper, a two‐stream convolutional network radar waveform recognition (TCNRWR) algorithm that fuses the original time–frequency image and the continuous frames autocorrelation time–frequency image is proposed, and an experimental platform is built by using software defined radio technology. Compared with traditional theoretical simulations, this paper focusses on the influence of spatial background interference and signal incompleteness on the recognition of intra‐pulse signals, and proposes an automatic removal method of invalid signal in the time–frequency domain. In order to further improve the recognition rate at low SNR, a deeper feature of short‐time autocorrelation flow is excavated, and a two‐stream time–frequency images fusion convolutional network (TTIFCNet) is constructed. The network effectively fuses the pre‐classification results of the two streams through Softmax layer and different weights. Finally, the hardware platform HackRF is used to realize the communication and processing of signals in real space, which proves the effectiveness of the TCNRWR algorithm. [ABSTRACT FROM AUTHOR]

The main‐lobe jamming causes serious degradation of radar system performance in practical applications. In recent years, the Blind Source Separation (BSS) method has been applied to suppress the main‐lobe jamming. However, the received echoes of target and jamming cannot be effectively identified while the robustness of the conventional BSS method is poor due to the influence of system noise. To tackle these problems, a radar main‐lobe jamming suppression and identification method is proposed based on robust whitening BSS and Convolutional Neural Networks methods. First, the target echoes and jamming are separated by the proposed robust whitening BSS method. An iterative optimisation method is proposed to obtain the robust whitening matrix, and sufficient conditions are provided for solvability. Then, a Target echo and Jamming Identification Network (TJINet) is proposed to identify the signals after the robust whitening BSS. The proposed TJINet method uses residual blocks and branch structures to improve classification performance and utilises asymmetric convolution blocks to extract subtle features between the target echo and deceptive jamming. Simulation and experimental results show that the proposed method has better jamming suppression performance than the traditional methods under low signal‐to‐noise ratio (SNR) conditions, the algorithm converges faster, and also can stably identify the target echo and jamming signals. [ABSTRACT FROM AUTHOR]

Radar has been shown potentially to be used for non‐contact sensing of biosignals in a more comfortable and easier way than wearable and contact devices. By detecting and extracting body motions linked to physiological activities, accurate estimation of the heart rate is possible. However, the detection and estimation of heartbeat signal in practical applications is disturbed seriously by noise and respiration harmonic signal. In this study, a multi‐level coarse‐to‐fine method is proposed for heartbeat rate (HR) estimation with a frequency‐modulated continuous‐wave radar operating at 60 GHz utilised to extract the heartbeat information of human body under low signal‐to‐noise ratio conditions. First, the frequencies, which are likely to be the HR, are extracted in the coarse estimation part. Then, evaluations about the extracted frequencies are conducted in the fine estimation part. Finally, the multiple signal classification algorithm is utilised to separate the HR frequency from the respiration harmonics, which solves the frequency resolution issue caused by the observation window size, and achieves high accuracy estimation of HR. The experiments on measured data demonstrate the effectiveness of the proposed method. Compared with the state of the art, the proposed method has superiority in terms of estimation accuracy of HR. [ABSTRACT FROM AUTHOR]

The multipath effect deteriorates the performance of low elevation target altitude estimation with a ubiquitous radar strongly, especially in complex terrain environments in which multiple reflection paths exist simultaneously. This work shows how the authors reconstructed a multipath model for ubiquitous radar with a known waveform and took advantage of it to improve the altitude measurement performance when locating low‐altitude small targets in complex terrain environments. The authors first present a multipath propagation model for target elevation angle estimation in which not only received multipaths but also transmitted multipaths are taken into consideration. Then, a reconstructed elevation angle estimation model in which the waveform is known and the target elevation information is as same as the original model is derived. Finally, an innovative low elevation angle estimation algorithm based on a maximum likelihood estimator and a sparse representation is proposed. Simulations and experiment results verify that the proposed algorithm has a much more robust low elevation angle estimation performance than existing methods in complex terrain environments. Index Terms—Target altitude measurement, complex multipath effect, known transmitted waveform, sparse representation, ubiquitous radar. [ABSTRACT FROM AUTHOR]

The micro‐Doppler (m‐D) effect depends on the rotation of rotor blades in addition to the translation of the platform. Thus it is a characteristic for identifying small unmanned aerial vehicles (UAVs). However, compared with the Doppler signal induced by the translation of the platform, the m‐D signal is weak. In this article, a highly localised data‐association method, intrinsic synchrosqueezing analysis (ISA), is proposed for estimating m‐D characteristics from the returned signal of small UAVs with a dual‐channel radar. Employing synchrosqueezing transform on intrinsic mode functions derived from noise‐assisted multivariate empirical mode decomposition, the proposed ISA method separates the Doppler signal and enables denoising and sharpening time‐frequency representation of the m‐D signal. Simulation results confirm the theoretical analysis, showing the feasibility of estimating m‐D features in a noisy environment. Applications on field data illustrate brighter prospects for identifying small UAVs. [ABSTRACT FROM AUTHOR]

Harmonic radar transponders are passive RF tags that produce a harmonic response when illuminated with an RF signal. With a Schottky diode as the main component, the tag current is usually modelled as an exponential function of antenna voltage, resulting in an approximate polynomial function for small signals. The authors introduce the Lambert function that accounts for the resistive load and leads to a two-region tag model: a power series representation for small signals and an asymptotic linear large-signal model. Furthermore, they provide the relationship between the modulated bandpass signals at the input and output of the tag in the two regions by expressing the tag signals with complex envelopes. The derived expressions offer deeper insight of the transponder's impact on the operation of harmonic radar systems. [ABSTRACT FROM AUTHOR]

To improve the RadCom system of performing both data transmission and radar-sensing functions, here a waveform design method for orthogonal frequency division multiplexing (OFDM) signal is proposed to solve the problem of high range sidelobe level in radar detection process while maintaining a reasonable channel capacity. The method only transmits data in the sub-channels with preferable channel quality. Then, calculate the weight of the chosen subcarriers through the convex optimisation method to traditionally minimise the peak-to-sidelobe (PSL) ratio. Simulation results prove that, compared with traditional range sidelobe suppression method, this algorithm is more suitable for RadCom system. [ABSTRACT FROM AUTHOR]

Since many modern multi-functional radars emitting dense complex signals are applied in the complex electromagnetic environment, it is challenge to classify and identify the radar emitters in the electronic reconnaissance field. In order to classify and identify radar emitters accurately and rapidly, a kind of standard adaptive extended radar emitter modelling structure and the complete process of obtaining radar emitter model parameters are proposed in this study. The simulation analysis results show that the proposed standard modelling structure and complete process can rapidly realise the accurate classification of many multi-functional radar emitters; hence can further benefit to accurate identification of emitters. [ABSTRACT FROM AUTHOR]

Radars operating in the high-frequency (HF) band can exploit propagation modes other than line-of-sight and are hence widely employed to provide over-the-horizon surveillance and remote sensing. Most HF radars operating today rely on surface wave propagation; while opportunities for enhancing their performance by adding cognitive features exist, these have not been widely seized. In contrast, designers of HF skywave radars, which exploit ionospheric reflection to achieve ranges in excess of 4000 km, have long sought to counter the enormous challenges of the geophysical environment by implementing schemes to raise radar intelligence quotient (IQ) and autonomy. Measures that have been explored relate to resource allocation, operating procedures, signal processing, target classification and remote sensing. The perception–action cycle constitutes the structural element of these developments, accessing prior knowledge and input from auxiliary sensors, exactly as proposed by those who later coined the term cognitive radar. Here, the roles of cognition in HF skywave radars are reviewed, illustrating the needs and benefits with examples including detection in sea clutter, ionospheric channel compensation and multiple-input and multiple-output HF radar. [ABSTRACT FROM AUTHOR]

Aimed at the problem of automatically identifying pulse repetition interval (PRI) patterns of complex radar signals, a novel method is proposed in this paper. The roughness, normalised mean jump energy, and Sp-curve characteristic are extracted as feature set based on the analysis of PRI sequences in time domain. Finally, the decision tree is employed to recognise different PRI patterns. The simulation results show that the algorithm has high recognition accuracy and good robustness to spurious pulses and pulse missing. [ABSTRACT FROM AUTHOR]

The compressed sensing (CS) radar receiver based on the theory of CS has a great advantage in receiving wide-band signal and can receive high-frequency radar signal at a sampling rate far lower than the traditional receiver. However, in order to obtain the information of the target signal, the parameters of the reconstructed signal still need to be measured by conventional methods. In order to make full use of the compressed sampling and the characteristics of radar signals, this study proposed a cascading dictionary matching pursuit algorithm based on block-sparsity (BS-CDMP) to achieve fast identification and reconstruction of received signals. First, a signal-level redundant dictionary is constructed for quickly identifying the signal in which the authors are interested in. Then, they study the feasibility of cascading the signal-level redundant dictionary and the DFT basis as a whole sparsity dictionary. Finally, the block sparsity is introduced for the selection of different parts of the dictionary during greedy iteration. Experimental results show that their proposed algorithm has considerable and universal reconfiguration performance and can directly perform fast reconstruction and recognition of the target signal. [ABSTRACT FROM AUTHOR]

Based on the purpose of proactive anti-reconnaissance, the essential characteristics of several typical pulse repetition interval (PRI) sorting algorithms were analysed and the feasibility of counter-deinterleaving was discussed. A method for disturbing arrival time sequence by using interference pulse was proposed, which changes the statistical properties of the radar PRI to disturb the deinterleaving results. The simulation results show that the pulse jamming method proposed in this paper can influence PRI sorting process effectively, which let signal processer make a misjudgment on PRI pattern of radar. [ABSTRACT FROM AUTHOR]

Long-time coherent pulse integration method has been extensively used in detecting weak targets, but the detection performance is affected by range migration (RM) due to high radial velocity. This study proposes a new modulation format of chirp radars with stepped frequency and a corresponding coherent integration method based on adjacent cross-correlation operation (ACCO) to compensate RMs of moving targets with, respectively, arbitrary unknown constant velocity. Simulation results show that the method has higher range resolution compared with bandwidth variation modulation and shorter blind distance compared with duration variation modulation. [ABSTRACT FROM AUTHOR]

This study considers beam-shape loss (BSL) for pencil-beam digital array radars (DARs), when multiple simultaneous receive beams are formed to cover the volume illuminated by each transmit beam. A general and valid computation procedure for the BSL is derived, and curves of BSL versus average detection probability and transmit beam spacing are given, for various kinds of receive beam arrangements and target types. The results show that the BSL for multiple-beam mode is commonly less than that for single-beam mode. The computation procedure and results are extensions of conventional ones for single-beam scanning radars and would be useful for range calculation and optimum beam spacing determination for modern multiple-beam DARs. [ABSTRACT FROM AUTHOR]

Signal representation is to transform the intercepted original signal into feature parameters, which is the basis for the electronic reconnaissance system to analyse and identify a radar. The traditional method is to manually design a limited number of features with prior knowledge, which can only achieve the desired results in specific applications. Therefore, it is necessary to study signal representation methods that are more adaptable, reasonable, and effective. This study proposes a feature-level design scheme that extracts multi-level signal feature parameters to obtain deep essential feature representations, which is consistent with deep-learning frameworks. Through the decomposition through wavelet packet transform (WPT), the best sub-band tree structure of the signal can be obtained, and the depth representation of the signal can be well achieved, which can meet requirements of time–frequency analysis automatically. Here, Symlets wavelet packet decomposition (WPD) is used as an example to implement multi-layer signal feature representation. The combination of characteristic parameters at different levels can reflect the different information contained in different radar signals. The simulation results show that the method can effectively identify the modulation mode of radar signals and enhance the information mining ability from radar signals. [ABSTRACT FROM AUTHOR]

Synthesising radar and communication waveforms in different directions using a common array is a hardware-saving solution for the coexistence of communication and radar in competitive spectrum environments. Here, the authors establish the problem of designing array probing signal to maximise power of the synthesised communication and radar waveforms, given array transmitting power, under no-waveform-error constraint and unit-modular-array-probing-signal constraint. Alternating direction method of multipliers (ADMM) is used to solve this problem. The results of numerical experiments show that, by solving this problem with ADMM, error of the synthesised waveforms can be completely eliminated, and the power of synthesised waveforms is almost optimal. [ABSTRACT FROM AUTHOR]

As a typical signal processing problem, direction-of-arrival (DOA) estimation has been adapted to a wide range of applications in radar-based systems. A high DOA resolution requires a large number of antenna elements which increases the overall cost. To minimise the cost, it is desirable to choose an optimum sub-array from a full array. To enable cognition, the subarrays are selected based on the present target scenario. By using deep learning (DL) based techniques, the authors show a cognitive sparse array selection technique. By using hardware simulations, they demonstrate the applicability of the deep learning (DL)-based sparse antenna selection network in direction-of-arrival (DOA) estimation problems. They show that the DL-based sub-arrays lead to a higher direction-of-arrival (DOA) estimation accuracy by 6 dB over random array selection. [ABSTRACT FROM AUTHOR]

Radar sensors have a new growing application area of dynamic hand gesture recognition. Traditionally radar systems are considered to be very large, complex and focused on detecting targets at long ranges. With modern electronics and signal processing it is now possible to create small compact RF sensors that can sense subtle movements over short ranges. For such applications, access to comprehensive databases of signatures is critical to enable the effective training of classification algorithms and to provide a common baseline for benchmarking purposes. This Letter introduces the Dop-NET radar micro-Doppler database and data challenge to the radar and machine learning communities. Dop-NET is a database of radar micro-Doppler signatures that are shareable and distributed with the purpose of improving micro-Doppler classification techniques. A continuous wave 24 GHz radar module is used to capture the first contributions to the Dop-NET database and classification results based on discriminating these hand gestures as shown. [ABSTRACT FROM AUTHOR]

Nyquist folding receiver (NYFR) can realize the wideband receiving with a small amount of equipment. As a tradeoff between the amount of equipment and the complexity of the signal processing, the NYFR output is usually a hybrid modulated signal. Therefore, it is necessary to investigate the parameter estimation method of the signal intercepted by the NYFR. Binary phase shift keying (BPSK) signal is a typical low probability of intercept radar signal and the authors focus on the parameter estimation of the BPSK radar signal intercepted by the NYFR. Firstly, matching component function is proposed to obtain Nyquist zone (NZ) index. Then, the Hankel-singular value decomposition (SVD) method is introduced to estimate the code rate of the BPSK signal. The efficacy of the Hankel-SVD method is proved in a mathematical way. Several numerical simulations demonstrate the merits of the proposed approach and finally, a brief investigation for case of the multiple BPSK signals intercepted by the NYFR is given. For NZ index estimation, the computational complexity of the matching component function is low and it has no need to use squaring process. In addition, the code rate estimation using the Hankel-SVD method is more accurate compared with the wavelet transform. [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]

Performance bounds on range resolution and range sidelobe level are built for waveforms with spectral notches. In this study, the authors use the integrated mainlobe energy (IME) and integrated sidelobe energy (ISE) of the autocorrelation function to assess the performance of range resolution and range sidelobe level, respectively. By considering the minimisation of the weighted sum of IME and ISE under given spectral constraints, they derive the optimal power spectrum density condition and subsequently calculate the best achievable IME and ISE. With this study, the radar system can (i) analyse the trade-off relationship between range resolution and range sidelobe level and thus better balance them in designing the transmit waveform; (ii) assess the fitness of potential working bands and thus select the best one to use. [ABSTRACT FROM AUTHOR]

The multiple-input multiple-output (MIMO) radar combines ultra-wide angular illumination on transmit and narrow transmission angular resolution after processing on receive. Orthogonal waveforms are needed in theory for all independent transmitters, but this property is usually not achievable in practice, therefore leading to a degraded ambiguity function. In this study, the authors focus on phase codes waveform, which gives quasi-optimal angle and range resolution. On the other hand, they induce a harmful ‘floor’ of high-level sidelobes in the range–angle domain. The authors propose to apply the orthogonal matching pursuit (OMP) to the MIMO radar signals. Therefore, it becomes theoretically possible to detect targets initially hidden by high-level sidelobes. Nevertheless, the authors identify phenomenon (grid granularity, neighbour influence) that lead to possible localisation error resulting in ghost targets. Considering this, the authors propose an extension of the OMP rejection step that includes, in addition to the expected target position, the neighbour positions within the uncertainty area. The authors show that this extension allows them to suppress the sensibility of the OMP to localisation error, even in case of very close targets. Finally, the authors demonstrate the performance of the proposed method on simulations as well as on experimental MIMO radar signals. [ABSTRACT FROM AUTHOR]

A design of dual-band cavity-backed slot antenna loaded with a spurline is presented for GPS and S-band radar applications. In the design, a very thin cavity (0.017 in thickness at 1.575 GHz) is used to achieve unidirectional radiation. The dual-band responses of the antenna are excited by the slot resonance at 1.575 GHz and waveguide transition effect at 2.4 GHz. Reasonable impedance bandwidths are obtained as 1.6% (26 MHz) at 1.575 GHz and 8.4% (203 MHz) at 2.4 GHz. The proposed slot can also be loaded with a spurline to adjust the antenna centre frequency and the phase of the radiated field. A switch using a PIN diode is deployed with the spurline. It is found that the spurline and the corresponding bias circuit for the PIN diode have little effect on the antenna radiation. Two proposed slots are combined toform an antenna which is right-hand circularly-polarised at both 1.575 and 2.4 GHz. A phase delaying system consisting of delay line and the spurline is designed to achieve 90° phase difference at both frequencies. The measured axial-ratio bandwidths are 1.5% (24 MHz) at 1.575 GHz and 4.5% (110 MHz) at 2.4 GHz, which are sufficient for practical applications. [ABSTRACT FROM AUTHOR]

Radar measurement accuracy is mainly dependent on the signal-tonoise ratio (SNR). SNR variation of the same target is related to fluctuating target radar cross-section (RCS) that is known as Swerling RCS model. Radar measurement accuracy is an important parameter to design a radar system or to analyse its performance. More useful measurement accuracy formula than the previous research that is sensitive to the detection threshold is proposed. To investigate the measurement accuracy affected by Swerling model, the relative standard deviation is defined to compare the value of the previous research with the proposed value. [ABSTRACT FROM AUTHOR]

The article presents an interview with Dr. Ludger Prünte who is from Fraunhofer Institute for High Frequency Physics and Radar Techniques (FHR), Germany. Topics discussed include information on his fi eld of research which involves synthetic aperture radar (SAR), and regularization-based signal processing methods to airborne and pulsed radar data; his aim towards reducing pulse repetition frequency (PRF); and his views regarding development of a cognitive radar.