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Wideband two‐dimensional multiple input multiple output (MIMO) radar can be used for target three‐dimensional (3‐D) imaging. As the echo of wideband MIMO radar is derived from both time and spatial sampling, the time‐frequency coupling is contained, resulting in imaging defocussing. To solve this problem, a time‐frequency decoupling algorithm based on matrix enhancement and matrix pencil (MEMP) is proposed to achieve high‐resolution 3‐D imaging. By combining the enhanced matrix constructed via MEMP with the modern spectral estimation theory, the time‐frequency coupling term is regarded as a space rotation factor and is eliminated in the process of target position estimation. Simulation results show that the proposed algorithm can reduce the negative influence of time‐frequency coupling and improve the imaging accuracy of wideband two‐dimensional MIMO radar.

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.

Unimodular waveforms with good correlation properties are desired for multiple‐input‐multiple‐output radar to achieve an increased transmitting/receiving virtual aperture. In this study, a new optimisation framework is introduced to design waveforms with good correlations. It is shown that many existing waveform design problems can be incorporated into this framework. Since the considered problem is in general highly non‐linear and non‐convex, an iterative optimisation method based on majorisation‐minimisation is developed. By carefully devising the surrogate function, the only requirement is to deal with a series of simple problems, which have closed‐form solutions. The proposed algorithm can be implemented via fast Fourier transform and hence is computationally efficient. In addition, the proposed algorithm is extended to deal with spectral constraints. Numerical results demonstrate the efficiency of the proposed algorithm compared with the state‐of‐the‐art techniques.

In recent years, machine learning theory has set off a wave of research in the field of radar signal processing. In this study, a novel algorithm for sea‐surface target detection based on a stacked autoencoder (SAE) is proposed, which has already been applied in the authors’ coastal defense radar system. In the proposed algorithm, the sea surface echo data are cut into a large number of two‐dimensional (2‐D) images through a sliding window, mapping the cell under test (CUT) and the 2‐D images one by one and performing classification or detection from the perspective of 2‐D signal processing. Experimental results of simulated data and real radar data show that the proposed algorithm based on the SAE has better target detection performance compared with the traditional cell averaging constant false alarm rate (CA‐CFAR) algorithm. Besides, the proposed algorithm shows certain interference suppression ability in real data processing. As far as it is known, there is no public report on the detection of 2‐D sea surface targets based on the SAE algorithm in coastal defense radar.

Vortex electromagnetic waves (VEMWs) carrying orbital angular momentum (OAM) are promising to benefit the higher degree of freedom for inverse synthetic aperture radar (ISAR) imaging on account of their helical wavefront. For high‐speed targets, the ‘stop and go’ hypothesis is no longer valid. Thus, the intrapulse movement of the target will cause the broadening and shift of the one‐dimensional range profile and the blurring of the OAM eigenvalues coupling azimuth. This contribution proposes a high‐speed target imaging method for the VEMW ISAR. First, an OAM‐based ISAR imaging model for high‐speed targets is derived and the echo characteristics are analysed. Subsequently, in order to effectively estimate the ratio of the echo to correct the imaging result, an estimation method that takes into account the calculation speed and accuracy is proposed. On this basis, the target velocity distribution obtained under multiple echoes is analysed to improve the accuracy of the results. Finally, the range profile and azimuthal profile are reconstructed. Compared with the existing vortex electromagnetic imaging algorithm, the proposed method can effectively eliminate the influence of high‐speed motion on the range and azimuth profile. This work provides suggestions for the development of EM vortex ISAR imaging technology.

Due to the high‐speed rotors and overlapped propellers, it is still a challenge to extract the micro‐motion parameters of the quadcopter. In this work, the authors establish a radar backscattering model of the quadcopter based on the flight control theory and radar backscattering characteristics. Then, the improved template matching method and peak grouping method are proposed to capture the periodic information for overlapped propellers on a range‐time profile and extract the rotation speeds of rotors. By simulating the nine types of quadcopter movements, the relative error of the rotation speeds is estimated to be lower than 1.4% for each rotor with a signal‐to‐noise ratio of 15 dB.

Feature selection plays a key role in target recognition especially for multiple combined sensors’ applications. This study presents an improved relief feature selection (Relief‐F) model to rank features for multi‐target recognition. Besides, the evaluation of the effectiveness of the method is conducted based on the following three measuring indices: feature redundancy, class separability and overall accuracy. The simulated data acquired by radar infrared combined sensors are used to verify the metrics of the proposed method. The experimental results demonstrate that the Relief‐F method reveals the best results compared to other feature selection methods, including principal component analysis and Fisher linear discriminant analysis. It indicates that Relief‐F can be an effective option for feature selection with high‐class separability and a low feature redundancy rate. Furthermore, high overall accuracy can be obtained using a relatively small amount of features selected by the Relief‐F method.

Synthetic Impulse and Aperture Radar (SIAR) is a kind of Multiple Input Multiple Output (MIMO) radar. It is a four‐dimensional radar that has advantages against stealth targets, multi targets, anti‐radiated missile, reconnaissance and interference, as well as valuable features in high range resolution and high detection probability. SIAR uses special frequency coding separately in each antenna. This code has an essential function in SIAR, such as separating frequency bandwidth, satisfying orthogonality in transmitted signals, suppressing range‐angle and range‐Doppler frequency coupling. Pulse‐to‐pulse frequency code agility is a kind of radar signal coding that is used to suppress the sidelobes in range‐Doppler frequency. In this study, a design method is suggested to transmit waveform based on non‐sequential positive, negative and random carrier frequency coding with the dot product approach in pulse‐to‐pulse frequency code agility, to reduce sidelobes in range‐Doppler frequency coupling. The simulation results showed that this method could effectively remove the strong coupling between range‐Doppler frequency. Removing coupling in the range‐Doppler frequency decreases the sidelobes.

The phased‐array multiple‐input multiple‐output (PA‐MIMO) airborne radar faces more severe sample shortage problem than the conventional PA radar. Hence, it suffers from severe performance degradation when it adopts the traditional space‐time adaptive processing (STAP) methods. Fortunately, the introduction of sparse recovery (SR) brings a novel perspective to reducing the sample requirement. However, most existing SR‐STAP methods only consider the clutter sparsity as prior information. In fact, more useful information, such as radar parameters, ground environment, airplane state, can all be obtained in advance and further utilised to improve the performance of SR‐STAP. In this study, the clutter structure, obtained based on the abovementioned prior knowledge, is utilised in the block SR‐STAP in the PA‐MIMO airborne radar. The clutter suppression performance is poor when the block SR algorithm is directly applied into the STAP because the clutter structure is destroyed by common vectorisation operation. To solve this problem, the vectorisation operation is first redesigned based on the clutter distribution characteristics and a novel dictionary is constructed. A basic block sparse Bayesian learning STAP scheme is then proposed in the PA‐MIMO radar’s application. The numerical experiments illustrate that the proposed method can achieve satisfactory performance with a few training samples and outperforms the state‐of‐the‐art SR‐STAP methods.

The linear frequency‐modulated frequency agile radar (FAR) can be used for synthesizing high‐range‐resolution profiles (HRRP), and also has excellent electronic counter‐countermeasures performance. Similar to the Newtonalized orthogonal matching pursuit (NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed as NOMP‐FAR is designed to process each coarse range bin to extract the HRRP and velocities of multiple targets, including the guide for determining the oversampling factor and the stopping criterion. In addition, it is shown that the target will cause false alarm in the nearby coarse range bins, and a post‐processing algorithm is proposed to suppress the ghost targets. Numerical simulations are conducted to demonstrate the effectiveness of NOMP‐FAR.

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.

Recent years have seen a steady convergence of Radar and Communications band Radio Frequency (RF) transceiver systems. Not only have Communications systems colonised large swathes of previously allocated Radar bands but there is also a convergence of technologies driven by the relatively low cost of software‐defined transceivers and solid‐sate RF sources. Thus, where conventional radar transmissions are characterised by short narrowband pulses with high peak power, new classes of ‘pulse‐compression’ radar are being developed to exploit this new technology. The resulting Low Probability of Intercept waveforms are designed to spread energy in both time and frequency, yielding a very low instantaneous power spectral density. Methods to detect, analyse and distinguish such sources require longer acquisition periods to collect more energy from the sources. Here, a novel solution is provided for detection and separation based on direction‐finding utilising polynomial matrix methods in conjunction with sparse array geometries. This approach provides enhanced detection, separation and direction finding while using relatively few antenna elements.

In the complex electromagnetic environment of cities, detection and positioning of slow and small unmanned aerial vehicles (UAVs) have always been a very challenging research topic. Usually, multiple radar observation stations are required to detect and track targets. Moreover, each radar observation station requires strict synchronisation, and the system is complex and costly. In view of this, under the condition of known prior information of urban geography, this study proposes a method to locate UAVs in complex electromagnetic environments, which use multiple virtual mirror synchronous radars constructed by multipaths and created by the complex buildings in urban environments. First, the simulation model is constructed, and the corresponding simulation parameters are given. Then, the algorithm for solving the single‐station radar location is proposed, and the algorithm is verified by the simulation of a ray propagation model. Finally, the proposed algorithm is further verified based on the measured data captured by the radar system in Section 2. The results show that the proposed algorithm based on the single‐station radar is able to locate the UAV in both line‐of‐sight and non‐line‐of‐sight site, and its positioning accuracy is better than 1 m.

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.

The dechirp‐receiving radar measures range by the frequency difference between the target echo and the reference signal. It can operate at a low sampling frequency while transmitting a wideband signal, which simplifies the radar hardware. However, when detecting high‐speed and highly maneuvering targets, residual video phase (RVP), frequency mismatch, frequency modulation (FM) rate mismatch, and across range‐Doppler unit (ARDU) problems occur after dechirp receiving, which cause difficulties in integrating the target echo and result in severe performance loss. To solve these problems, the dechirp generalized Radon‐Fourier transform (DGRFT) and an algorithm for its fast implementation are proposed. The DGRFT compensates for the frequency and FM rate mismatch in the fast‐time domain and the RVP in the fast‐time frequency domain, and then implements joint envelope and phase compensations to overcome the ARDU phenomenon. After these compensations, the target echo can be effectively integrated. Experiments with simulated and measured data show that the performance of the DGRFT is better than that of the conventional moving target detector.

The radar cross section (RCS) is an important parameter that reflects the scattering characteristics of radar targets. Based on the monostatic radar RCS time series' statistical features by sliding window segmentation, a novel sliding window‐statistical‐gated recurrent unit (SW‐S‐GRU) method for radar target recognition (RTR) is proposed using a GRU. The sliding window method divides the RCS time series into many segments for obtaining the statistical features as the input of the GRU. The deeper features are extracted from the segmented time series and then classified by the GRU. Under the model of precession motion, simulation results show that the proposed method achieves a great recognition accuracy. Even under the condition of a low SNR, the proposed method can obtain better recognition accuracy. For the precession dataset, the effect of sliding window parameters on RTR is analysed. The simulation results show that the recognition result of the model is better using the longer series overlap length between adjacent times. And when the number of hidden layers is 8 or above 8, the recognition accuracy does not change. Using the publicly available measured drone RCS data, the proposed method can also identify the drone well.

This Editorial introduces a Special Issue of papers from the SEE International Conference RADAR 2019, which took place in Toulon, France, 23–27 September 2019. The papers are expanded versions of ones that were judged in the reviewing process to be of the highest quality and relevance. Benefitting from the unique location of the conference centre on the French Riviera, the conference was given the theme ‘Sensing from sea to space’, attracting nearly 400 attendees from 26 countries from all over the world (Figure 1). Engineers and researchers coming from private or public sector (universities and laboratories) shared their knowledge and experience on radar topics of common interests from physics to signal processing, from sensor validation to system design, and also on emerging technologies based on artificial intelligence; all topics being supported by 200 papers presented either in oral (150 papers in 36 sessions), or poster sessions (50 papers). Figure 2 shows the range of topics of the papers. 1 FIGURE Conference attendees by country 2 FIGURE Conference paper topics

Traditional space‐time adaptive processing (STAP) suffers from severe performance degradation in dense false target jamming scenarios, especially mainlobe deceptive jamming. To solve this problem, the superimposed stepped frequency (SSF) radar is explored and its ability in suppressing the mainlobe deceptive jamming is studied in this work. SSF radar can provide a controllable degree of freedom in range domain besides the traditional freedom in angle and Doppler domains, which can be used to distinguish true and false targets. Thus, the range cascaded STAP approach is proposed to suppress the deceptive jamming and the clutter. First, the true target and clutter are blocked in carrier frequency domain. Then, the jamming is attenuated by the adaptive filter in carrier frequency domain with the jamming covariance matrix being constructed by the blocked data. At last, the clutter can be suppressed by the cascaded STAP in the space‐time domain. Simulation results demonstrate the effectiveness of the proposed radar framework and the processing approach in suppressing the clutter and jamming.

With the development of the radar technology, it is required that a radar has wider bandwidth and can transmit multiple signals simultaneously. The polyphase digital channelised transmitter structure can transmit multiple signals simultaneously and increase the bandwidth of the output signal. However, in order to reduce the frequency aliasing and improve the spectrum utilisation, the prototype filter needs to have sharp transition bandwidth. The design of the prototype filter directly affects the performance of the radar transmitter. Using the complex‐exponential modulation frequency‐response masking (CEM‐FRM) approach to design the prototype filter can obtain both sharp transition bandwidth and low complexity. The application of a polyphase structure can further reduce the complexity of the filter bank and make the sampling rate of the output signal several times the field programmable gate array operating frequency. The proposed CEM‐FRM‐based low complexity polyphase channelised radar transmitter is verified by MATLAB simulation and the theoretical complexity of the proposed CEM‐FRM‐based channelised radar transmitter structure is compared with other methods.

The performance of the distributed coherent aperture radar (DCAR) is heavily influenced by the antenna positions. Therefore, an antenna position optimization method is proposed based on the adaptive genetic algorithm with a self‐supervised differential operator. In the proposed method, the antenna positions are firstly coded as the chromosomes of the population with multiple constraints, and the reciprocal of the peak side lobe level (PSLL) of the beam pattern is calculated as the fitness function for optimization. Then, the adaptive probabilities are calculated for the crossover and mutation of chromosomes and a self‐supervised differential operator is utilized in the mutation. Finally, the optimal antenna positions for DCAR can be obtained with the lowest PSLL compared with the existing methods. The effectiveness of the proposed method is verified by linear and planar DCARs, respectively.

International audience; Radio sources in the Very High Frequency (VHF) band can be seized as opportunity donors in a passive radar configuration such as FM radio stations and VHF omnidirectional range (VOR). A full-wave simulation of three size classes of aeroplanes shows that their bistatic radar cross-section (RCS) are statistically comparable, albeit perform differently in time while the plane is flying. This difference can be exploited to recognize the size of the aeroplanes with respect to these classes. Measurements confirm this possible differentiation between the aeroplanes within the same class. Encouraging initial results were obtained using convolutional or recurrent neural networks to classify aircraft classes, combining simulated bistatic RCS results and real trajectories (collected from automatic dependent surveillance-broadcastdata).

The vortex electromagnetic (EM) wave radar has the potential to obtain more accurate micro‐motion parameters for target recognition. However, with the existing algorithms of micro‐motion parameter extraction it is difficult to obtain the real rotation radius and tilt angle of a rotational target in the presence of multiple scattering points in the radar beam. A micro‐motion parameter extraction algorithm for rotating targets based on the vortex EM wave radar is proposed in this article. The angular Doppler is obtained from the dual‐mode vortex EM echoes. The time interval between the maximum and minimum angular Doppler frequency is derived. The relationship between the time interval and micro‐motion parameters is shown. By combining the linear Doppler and the angular Doppler, the micro‐motion parameters are roughly estimated. Then, fine micro‐motion parameters are obtained by using an iterative soft threshold algorithm. The proposed algorithm can extract the real rotation radius and tilt angle in the case of multiple scattering points. The performance and robustness of the algorithm are proved by simulations.

Frequency agile radar (FAR) systems have been demonstrated to have outstanding performance in electronic counter‐countermeasures (ECCM). With increased bandwidth in which FAR can switch carrier frequency, the phase errorswill appear at the corresponding frequency point, resulting in the decrease of coherence of the system. To accomplish phase error correction and range and velocity information extraction of the target simultaneously, a joint phase error estimation and sparse scene restoration algorithm based on sparse recovery (JPSA‐SR) is proposed here. To further reduce the computational complexity of the algorithm, a joint phase error estimation and sparse scene restoration algorithm based on alternating direction method of multipliers (JPSA‐ADMM) is also proposed. Numerical examples show the effectiveness of the proposed algorithms.

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.

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.

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.

In order to improve the accuracy and resolution for transmit beamspace (TB) multiple‐input multiple‐output (MIMO) radar, a search‐free direction‐of‐arrival (DOA) estimation method based on tensor modelling and polynomial rooting is proposed. In the proposed method, a 3‐order tensor is designed to model the received signal of TB MIMO radar on the basis of the multi‐linear property. The factor matrix with target DOA information is obtained by tensor decomposition via alternating least squares algorithm, and subsequently the DOA estimation is converted to independent minimisation of the virtual transmit beampattern for TB MIMO radar. By exploiting the Vandermonde structure of the transmit steering matrix, a polynomial function is constructed to solve the minimisation problem via polynomial rooting. The proposed method can fully take advantage of the TB technique and tensor modelling, and it requires no spectrum searching, which is different from the existing DOA estimation techniques for TB MIMO radar. The effectiveness of the proposed method is verified by simulations.

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target‐dependent and target‐independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler‐time spectrograms and high‐range resolution profiles generated for diverse radar frequencies and wall characteristics in around‐the‐corner radar (ACR) scenarios. Additional experiments are performed on range‐enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal‐to‐noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single‐layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of −10 dB and label mismatch error of 50%.

Bistatic airborne radar has excellent performance due to the separate transmitter and receiver, but its clutter distribution is usually non‐stationary, which causes the performance of the traditional Space time adaptive processing (STAP) method to fail. Herein, firstly a bistatic airborne radar clutter signal model established, followed by an analysis of its clutter distribution characteristics in detail, and finally a bistatic airborne radar space–time compensation STAP method based on adaptive segmentation is proposed. The method first uses the distribution characteristics of the peak spectral centre in the first pulse data of the airborne radar to adaptively segment the clutter data, including extremely non‐stationary, severely non‐stationary, and non‐stationary clutter regions. Then the clutter data are compensated based on the estimation value of the space–time peak spectrum centre. Finally, each range section is processed by using STAP, and the detected target Doppler frequency is adaptively moved. Simulation results verify the effectiveness of the proposed method.

In skywave radar, the slow‐time samples received in a certain range‐azimuth cell are usually processed for signal analysis and target detection. Particularly, to extract the principal components, such as sea clutter and target signal, in slow‐time samples, the Hankel matrix structure and subspace‐based methods are often applied, which can also reduce the additive Gaussian noise. In the real environment, the slow‐time samples sometimes are seriously contaminated by the transient interference, affecting the performance of useful signal reconstruction. The robust low‐rank matrix recovery methods have been applied to solve this problem, where the transient interference is assumed to be sparse, following the Laplace distribution. However, the Hankel structures of useful signal and transient interference have not been fully utilized in the conventional methods, which can enhance the reconstruction performance. Here, the robust low‐rank Hankel matrix recovery problems is reformulated for skywave radar slow‐time samples and solve them with the inexact augmented Lagrange multiplier method. Additionally, the low‐rank Hankel matrix completion problem is discussed, where the location of the transient interference is determined. The experimental results have demonstrated the good performance of our proposed methods and also some interesting conclusions are obtained.

Integration of radar and communication (IRC) is conducive to improving hardware resource utilization and spectrum utilization. This paper proposes a joint range and velocity estimation method for IRC based on multi‐symbol orthogonal frequency division multiplexing (OFDM) radar pulses. A two‐step parameters estimation method is applied, where estimating the signal parameters via rotational invariance technique (ESPRIT) and weighted subspace fitting (WSF) perform rough and fine estimations. We first build a receiving model for integrated signal based on element‐wise division. The ESPRIT method is then adopted based on the double Vandermonde structure array manifold for a rough estimation. It has relatively high estimation error but low computational complexity. The WSF method based on alternating projection (AP) algorithm is introduced to make the estimation more precise and reduce its error. It uses the estimation results in the first step and reduces the overall computational cost. Simulation results show that the proposed method reduces the computational complexity while improving the estimation accuracy compared with the WSF method.

The interaction of radar signals with a planetary surface is critical for the utility of radar data. Herein, a power‐law roughness spectrum is used to model the roughness and an inhomogeneous vertical permittivity to describe the dielectrics from the surface of Mars. Numerical simulations of depolarised radar backscattering are then conducted. To gain a more complete picture of depolarised characteristics, a wide range of surface roughness is considered. Results reveal that the depolarised backscattering is sensitive to the dielectric profiles with –10 dB is observed. The polarization conversion ratio, the ratio of depolarisation to total polarization backscattering, is a sensitive indicator of dielectric profiles and roughness, and thus is useful in retrieving dielectric profile.

A deep‐learning‐based approach to estimating the number of coherent sources in radar is presented. A proper estimate of the number of sources in a signal enables improved angle‐of‐arrival (AoA) estimation common in applications such as radar, sonar, and communication systems. Many AoA estimators utilised in these areas require accurate estimates of the number of sources for enhanced performance. Herein, a robust method that performs well under the existence of coherent sources is developed. The proposed method is based on deep learning and it is shown to perform better than state‐of‐the‐art versions of the Akaike Information Criteria (AIC), Minimum Description Length (MDL), and Exponentially Embedded Families (EEF) estimators, which employ spatial smoothing of the covariance matrix to handle coherent signals.

A signal embedding scheme into the emission of multiple‐input multiple‐output (MIMO) frequency hopping (FH) radar is analysed. In this scheme, the phase shift keying (PSK) communication symbols are multiplied with the frequency hops in fast‐time, resulting in dual‐function radar‐communication (DFRC) waveforms. The authors examine the impact of PSK symbol embedding on the ambiguity function (AF) and power spectral density (PSD) and show that modulation of the radar pulse can benefit the radar functionality. The effect of PSK symbol embedding on the radar target detection performance of the DFRC system is delineated. The authors’ analyses show that the use of the DFRC waveforms enhances target detection performance. The authors also provide the symbol error rates (SERs) corresponding to different phase constellations at the communication receiver and address the practical data transfer rates that can be achieved with the underlying embedding scheme.

To improve the clutter suppression performance of airborne radar in heterogeneous environment, a robust refined training sample reweighting space–time adaptive processing (STAP) method called RRSRW is proposed here. First, some target‐free training samples around the cell under test (CUT) are selected and the corresponding clutter dictionary matrices are constructed using the radar system parameters. Then, the clutter patch amplitudes and array error for selected training samples are simultaneously estimated through the formulated constrained least squares problem. Subsequently, based on the covariance matching estimation criterion, the local weighting coefficients for selected training samples are estimated by the redesigned convex optimisation problem. Finally, the STAP weight vector is calculated to process the CUT data. The proposed method is robust to array error and can effectively protect the moving targets in CUT data, which is free of hyper‐parameters and has global convergence properties. Simulation results demonstrate that the proposed RRSRW method can effectively suppress the strong ground clutter and greatly improve the detection performance of moving targets in heterogeneous environment.

The beam steering of phased array (PA) radar is fixed in one direction for all the ranges, thus the resulting beampattern is angle‐dependent but range‐independent. Compared with the PA radar, the frequency diverse array (FDA) radar can achieve range‐angle dependent beampattern by employing a small frequency increment across the array elements. Numerous studies have been conducted using linear FDAs, while other array configurations are seldom studied. On the other hand, the range‐angle dependent beampattern generated by the FDA is also time‐variant. However, previous studies on time‐invariant spatial‐focusing FDA beampatterns neglected the propagation process of the transmitted signals, leading the focused beampattern difficult to achieve when the transmit delay caused by wave propagation is considered. Taking the time‐variant property of the FDA beampatterns into consideration, in this study, we propose a novel uniform circular FDA (UC‐FDA) radar for short‐range beampattern synthesis. The UC‐FDA radar has the ability to scan azimuthally through 360°, which is a potential solution for applications requiring three‐dimensional beam steering. Furthermore, the proposed UC‐FDA radar adopts a pulse‐dependent non‐linear frequency offsets based on logistic map, together with multi‐carrier technique to achieve a dot‐shaped time‐variant focusing beampattern with minimum sidelobe levels and high‐resolution. The effectiveness of the proposed UC‐FDA radar is validated with simulated experiments.

Space‐time adaptive processing (STAP) for sparse arrays such as coprime and nested arrays is shown to have improved performance for clutter suppression in airborne radar as compared with uniform linear arrays with the same size. However, most of the existing STAP algorithms are derived based on the assumption that the array manifold is exactly known. In this study, a robust STAP algorithm for the clutter suppression with coprime arrays in the presence of gain and phase (GP) errors is proposed. In particular, the virtual space‐time signal with GP errors is first formulated using the covariance matrix of the perturbed array received data under the Gaussian distributed interference assumption. Then, the problem of the clutter spectrum and GP errors' estimates is formulated as a joint minimization optimization problem by exploiting the sparsity of the clutter in the angle–Doppler plane. Finally, a two‐step approach based on the sparse recovery and least‐squared method is developed to solve the resultant optimization problem and the STAP filter can be designed using the clutter spectrum and GP errors' estimates. Compared with the existing sparsity‐based STAP algorithms for coprime arrays, the proposed algorithm shows more robustness to GP errors as illustrated by simulation results.

Fil: Collado Rosell, Arturo. Instituto Balseiro - Universidad Nacional de Cuyo, 2LIAT, Dto. de Ingeniería en Telecomunicaciones Fil: Pascual, Juan Pablo. Instituto Balseiro - Universidad Nacional de Cuyo, 2LIAT, Dto. de Ingeniería en Telecomunicaciones Fil: Areta, Javier Alberto. Universidad Nacional de Río Negro. Centro Interdisciplinario de Telecomunicaciones, Electrónica, Computación y Ciencia Aplicada. Río Negro, Argentina. A spectral algorithm for processing staggered-pulse repetition time (SPRT) signals in weather radar is introduced. It includes new approaches for ground clutter filter and hydrometeor spectral moments estimation. The algorithm uses ideas similar to GMAP but applied to non-uniform sampled signals. This work is focused on staggered sequences 2/3, but can be extended to other staggered sequences. Monte Carlo experiments were used to evaluate the performance of the spectral moments estimators for simulated weather signal, in scenarios with and without the presence of ground clutter. When clutter is present, a study using different clutter-to-signal ratios was carried out, showing that the method can deal with a wide range of situations and is appropriate for implementation in real scenarios. A comparison against GMAP-TD was performed, showing similar estimation results for both algorithms and a fivefold processing speed improvement for the proposed method. The performance was also validated using real weather data RMA-12 from a radar located in San Carlos de Bariloche, Argentina.The proposed algorithm has an easy implementation and is a good candidate for real-time implementations. Presentamos un algoritmo espectral para procesar señales de tiempo de repetición de pulsos escalonados (SPRT) en radares meteorológicos. Este incluye un nuevo enfoque para el filtrado de reflexiones aleatorias en el suelo (clutter terrestre) y para la estimación de momentos espectrales de hidrometeoros. El algoritmo utiliza ideas similares a GMAP pero aplicadas a señales muestreadas en forma no uniforme (staggered). Este trabajo se centra en secuencias escalonadas 2/3, pero puede extenderse a otras secuencias escalonadas. Se realizaron experimentos de Monte Carlo para evaluar el rendimiento de los estimadores de momentos espectrales para la señal meteorológica simulada, en escenarios con y sin presencia de clutter terrestre. Se llevó a cabo un estudio utilizando diferentes relaciones señal a ruido, que mostró que el método puede hacer frente a una amplia gama de situaciones y es apropiado para su implementación en escenarios reales. Se realizó una comparación con GMAP-TD, que mostró resultados de estimación similares para ambos algoritmos y una mejora de la velocidad de procesamiento de cinco veces para el método propuesto. El desempeño también fue validado utilizando datos meteorológicos reales RMA-12 de un radar ubicado en San Carlos de Bariloche, Argentina. El algoritmo propuesto es de fácil implementación y es un buen candidato para implementaciones en tiempo real.

Some new problems in distributed inverse synthetic aperture radar (ISAR) fusion imaging may be caused by constant frequency errors (CFE) due to the imperfect match of two independent oscillators. In this study, the effects of the CFE on distributed ISAR coherent fusion imaging are first analysed, which show that the CFE will produce false images (incorrect scatterer number and position) in distributed ISAR fusion imaging. To eliminate the effects of the CFE, a novel CFE calibration scheme is devised by exploiting the echo signal phase relationship between different sensors and making some reasonable approximations. The echo signal of the active radar (transmit and receive) is used as a reference signal. After some derivation, the phase relationship between the reference signal and other signals with CFE can be derived. Based on the derived phase relationship, the CFE of each radar will be estimated by multiple signal classification algorithm. Then, the estimated CFE value is further used to calibrate the CFE of each radar before signal coherent fusion. As a result, the false image is eliminated. Simulation results prove the authors theoretical analysis and the effectiveness of their method.

Compared with conventional phased array radar, the collocated multiple-input multiple-out (C-MIMO) radar is envisioned to offer greater freedom in waveform design and can simultaneously manage different beams to track multiple targets in the simultaneous multi-beam (SM) working mode. In this study, aiming at the multiple targets tracking (MTT) problem, a joint beam selection integrated with power and bandwidth allocation (JSPBA) scheme for the C-MIMO radar is proposed in the SM mode. By incorporating the modified particle filter, the predicted conditional Cramer–Rao lower bound (PC-CRLB) is calculated, which gives a measure of the best achievable performance for targets tracking. Then, the optimisation model is established with the aim of improving the worst case of PC-CRLB to achieve better performance of the worst-case tracking. Next, a three-step solution method is proposed to solve the JSPBA problem by converting the non-convex problem into a series of convex problems. At last, after the solutions are fed back to the control centre to guide the beam generation in the next tracking epoch, a cognitive tracking system is established. Simulation results confirm the superiority of the proposed method in improving the MTT performance in C-MIMO radar system.

© The Institution of Engineering and Technology 2020. The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data.

Ionospheric phase contamination deteriorates the coherence of high-frequency echoes, reducing the detection performance of multiple-input multiple-output over-the-horizon radar (MIMO-OTHR) on slow ships. A high-precision phase decontamination method approximately extracts a single-frequency reference signal from the echo in advance, called the calibration signal, which is completed in the Doppler domain. When severe ionospheric phase contamination causes the echo Bragg peaks to overlap, this operation is difficult to achieve. To solve this problem, the authors transform calibration signal extraction into a sparse decomposition of a short-time sequence vector set and solve it by an iterative method. This process is based on the sparsity of the short-time sequence in the Doppler domain, as well as the reversibility between the original data vector, accumulated after a long coherence time, and its corresponding short-time sequence vector set, obtained by sliding window segmentation of the original data vector. Then the ionospheric phase contamination is extracted from the calibration signal to compensate for the original echo. Compared with existing methods, the proposed method can adaptively extract a calibration signal to achieve high-precision phase compensation of a MIMO-OTHR echo while adding a more robust performance against noise when considering overlapping Bragg peaks formed by serious ionospheric phase contamination.

The electronic support measure has a pivotal role in electronic warfare. Radar signal deinterleaving achieves complex radar pulse streams to deinterleave by analysing the intercepted radar emitter signals and estimating the pulse repetition interval (PRI) values. At present, most of the deinterleaving algorithms use the classical model of pre-sorting combined with the PRI deinterleaving algorithm, which is challenging to adapt to the current radar environment. The agility of pulse parameters increases the error rate; the existence of PRI jitter and pulse missing leads to the sub-harmonic problem and low estimation accuracy; the overall algorithm real time is mediocre. To overcome these disadvantages, the authors propose a synthetic algorithm incorporating the statistical histogram into pre-sorting, combining the improved sequence difference histogram algorithm with the multi-width PRI transform in PRI deinterleaving. The simulation results show that the algorithm can accurately estimate the PRI values in a short time and detect various radar signals. C language experiments validate the algorithm in real time.

Air-to-ground ranging (AGR) is one of the important operation modes of an airborne monopulse radar system. Here, an approach for the detailed modelling and simulation of the AGR performance of such a radar system over complex terrain is proposed. Time-domain monopulse signals reflected by the terrain's surface were modelled using the dynamics of the airborne platform, clutter properties, and RF specifications of the radar, which included the operating frequency, antenna beam patterns, and pulse repetition time. The AGR performance was then numerically analysed by employing the modelled monopulse return signals and a digital elevation model of the terrain's surface. Several crucial factors affecting the accuracy of line-of-sight estimation, including shadowing effects and roll stabilisation, were addressed using numerical simulations of various scenarios.

The authors propose an algorithm to estimate the path followed by refracted signals from a source to a target, through a medium formed by uniform parallel layers with known different refractive indices, a common model used for ice radio-echo sounding. The analytical solution is a polynomial with a degree that exponentially depends on the number of layers, being computationally inefficient. For low incidence angles, the small-angle approximation can be used to avoid the polynomial. In their technique, they normalise the governing equations to obtain a framework where to find a narrow angular interval containing the solution, finally estimated interpolating the boundaries. The new approach improves the results regarding the small-angle approximation for a wider angular range at a slightly higher computational time. This method has been applied to focus airborne synthetic aperture radar images for deep ice sounding, reducing the calculation time and improving the detected response in wide beam and squinted geometries, used for high along-track resolution or the detection of sloping internal layers.

Cognitive radars have the capability of adapting the transmitted waveform with the dynamic state of target and environment, to enhance the radar performance. In this study, a cost function is proposed for designing the waveform parameters in cognitive radars, along managing the radar time resources. The proposed cost function has the capability of adapting to any radar measurement vector. The purpose of the proposed cost function is to design the waveform parameters adaptively, so that a compromise between the different radar measurement errors is made. This compromise between the different radar measurement errors depends on the prior and posterior target state estimations. Both simulated target data and real target data are used for evaluating the proposed cost function. Also the performance of the proposed cost function is compared to the previously presented cost function. Simulation results indicate that by exploiting the proposed cost function, a great saving on time resources is achieved, while tracking error is kept stable. This time resource-saving lies within the range of 10% up to 50% depending on the target manoeuvre scenario. Also, in a non-time resource management case, an improvement in tracking error is achieved.

Human identification plays a vital role in daily lives. A majority of biometric technologies require the active cooperation of humans, while gait recognition does not. Compared with other identification technologies, radar-based technology can monitor the human body around the clock without being affected by light/weather, and is not easy to be forged while protecting privacy. Previous researches have revealed that gait signatures acquired using radar can be used for human identification, but there is almost no literature on the long-term stability of gait signatures. Due to the long-term interval observation, the human micro-Doppler will change according to the subject (such as slight differences in walking posture). In this study, a novel network is proposed to realise stable identification of humans by extracting long-term stable features. The micro-Doppler data is processed by a short-time Fourier transform and finally classified by the proposed neural network. Data acquisition was carried out within more than a month. The experimental results demonstrate that the recognition accuracy of the validation set can reach about 99%, and the recognition accuracy of the test set can reach 90% (improved 3% compared with the network without a recurrent neural network), showing the potential of the proposed method in long-term stable identification.

For high-resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice. However, for future vehicle autonomy and driver assistance in adverse weather conditions, improvements in automotive radar technology and the development of algorithms and machine learning for robust mapping and recognition are essential. In this study, the authors describe a methodology based on deep neural networks to recognise objects in 300 GHz radar images using the returned power data only, investigating robustness to changes in range, orientation and different receivers in a laboratory environment. As the training data is limited, they have also investigated the effects of transfer learning. As a necessary first step before road trials, they have also considered detection and classification in multiple object scenes.

In this study, the authors take further insight into the sparse geometry which offers a larger array aperture than uniform linear array with the same number of physical sensors. A novel direction of arrivals (DOAs) estimation model with flexible sparse geometry, which possesses the potential to significantly improve the estimation performance especially when the placed space and the weight of carrier such as airborne radar are restricted, is proposed to offer a larger aperture compared with co-prime array. The proposed algorithm can estimate DOAs by solving phase ambiguity. To improve the capability of spectrum analysis in frequency domain, all phase discrete Fourier transform (DFT), which can effectively alleviate spectrum leakage compared with traditional DFT, is proposed to apply into DOAs estimation. Additionally, the performance on degrees of freedom can be considerably improved compared with the state of the art where all the targets can be distinguished by Doppler information of received echo signal. More importantly, the proposed algorithm can effectively deal with DOA-closely-spaced targets because the proposed algorithm does not require to estimate signal subspace with ill-conditioned steering matrices. Both the theoretical analysis and simulation results demonstrate that the proposed algorithm significantly improves DOAs estimation precision with less computation cost.