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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 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.

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.

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.

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.

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.

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.

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.

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.

With the multi-function need for airborne space-time adaptive processing (STAP) radar, range synthesis is usually necessary for the follow-up function of target recognition. Utilising the excellent performance on orthogonality and bandwidth synthesis of the frequency division multiple access (FDMA) waveform and the longer accumulation time of multiple-input multiple-output (MIMO) radar, a robust low-sidelobe target synthesis method for airborne FDMA–MIMO STAP radar is developed. The spectra characteristics of target and clutter are first analysed. Then, the keystone transform is employed to mitigate velocity migration. To avoid gain fluctuation induced by multiple independent adaptive weight vectors, the robust STAP beamformer is developed. Finally, to decouple the range–angle coupling relationship resulted from the FDMA waveform, angle compensation is added in the robust STAP beamformer. Numerical examples are given to demonstrate the effectiveness of the presented method.

A distance selective pseudo-random noise (PRN)-coded Doppler radar is proposed. The radar is capable of measuring the vital signs of a human in a noisy environment with high precision. The distance selectivity feature is achieved by the use of PRN modulation to focus on the desired target at a certain distance and suppress the Doppler frequencies of other targets at different distances. In addition, an offset signal using Delta–Sigma modulation is generated to cope with the suppression of low-Doppler frequencies, very close to DC, in AC-coupled systems. The authors compare the performance of the proposed radar system to a frequency modulated continuous wave radar in measuring the Doppler frequencies of two loudspeakers located at different ranges. In addition, they experimentally evaluated the proposed radar in measuring the vital signs of a human in a noisy environment. The radar is capable of isolating the Doppler frequencies of the vital signs from the surrounding noise. In addition, vital signs information is preserved at higher frequencies away from the high-pass filter in AC-coupled systems.

Bats and birds are endangered by wind turbines, leading to a significant amount of fatalities in recent years, mainly due to a direct collision of the animals with the rotor blades or through barotrauma effects. The search for fatalities and the quantification of their number is an important tool to understand the adverse effects of wind turbines on bats and birds. This method has several problems: Cadavers do not always remain on site due to predators, only a part of the relevant area on the ground can be searched manually and not all fatalities can be found. This leads to the fact that searches, although extremely valuable in the analysis of animal mortality, are hardly used in practice due to the high effort and enormous costs. The present work closes this research gap by proposing a radar barrier approach that automatically detects animal fatality in real-time. Therefore, three FMCW radar systems are attached to the tower of a wind turbine at different heights. Subsequent processing of time-of-flight information enables a binary classification of whether a fatality was observed or not. This study presents the results from numerical simulations as well as preliminary experimental results for a proof of concept.

This work deals with the entire process of target detection and ranging by a sky-wave over-the-horizon radar (OTHR) computational model simulation. The different processing stages of the transmitted signal along its space–time trajectory from transmission to digital signal processing are modelled. With this simulation tool a moving target present over the sea can be detected according to a set of given initial conditions together with the ionosphere model inputs and the target electromagnetic model. Initial conditions as well as the modulation and filtering options among other parameters of the model can be set easily. The present work is intended to be a further contribution to OTHR studies, providing a user-friendly tool of easy application in order to improve a radar design, facilitate its implementation, as well as for debugging algorithms and signal processing techniques.

High-frequency surface wave radar (HFSWR) is an effective method for detecting and monitoring the targets on the ocean surface and above. To improve the detection performance of HFSWR, a new detector is proposed by applying weighted distance iteration (WDI) processing to the joint domain localised (JDL) matrix constant false alarm rate (CFAR) detector. Based on information geometry theory and Riemannian manifold, the proposed detector uses multi-dimensional information (azimuth, Doppler velocity and range information) of the signal to effectively find the target that has drowned in sea clutter and uses WDI-processing technique to suppress the clutter. The experimental results are given to show the superiority of the proposed detector. Compared with cell average CFAR detector and JDL matrix CFAR detector, the proposed detector improves the detection performance effectively.

Cognitive radars have the capability of using the prior environmental information to improve the radar performance. In this study, a cost function for fully adaptive waveform parameters design for cognitive tracking radars is proposed. These parameters include – but not limited to – pulse length, pulse repetition frequency, number of transmitted pulses, and target revisit rate. Thus, the proposed cost function is capable of reducing the tracking error, using adaptive waveform parameters, along the time resource management, with adaptive coherent processing interval and target revisit time. The purpose of the proposed cost function is balancing the different radar measurement errors (which depend on radar transmitted waveform in contrary directions), with respect to the target manoeuvre scenario, to reach the radar tolerable tracking error. Simulation results show that using the proposed waveform parameter design cost function, tracking error is reduced, and an optimised radar time resource usage is obtained.

A clutter suppression method based on a combined method of clutter map and K-SVD algorithm has been proposed, which aims at reducing false alarm rate of air surveillance radar caused by wind farm clutter. First, a clutter map, based on the fixed position and Doppler spectral width characteristics of wind turbines, is established using constant false alarm rate detection. The obtained clutter map is then used to process the traces detected by radar. To retain the target while the clutter is suppressed, the traces in the clutter area are processed using the K-SVD algorithm, which is first proposed to mitigate wind turbine clutter in this study. The experimental results of measured data show that the false alarm caused by the wind turbines can be effectively reduced.

The authors consider radar classifications of activities of daily living, which can prove beneficial in fall detection, analysis of daily routines, and discerning physical and cognitive human conditions. They focus on contiguous motion classifications, which follow and commensurate with the human ethogram of possible motion sequences. Contiguous motions can be closely connected with no clear time gap separations. In the proposed approach, they utilise the Radon transform applied to the radar range-map to detect the translation motion, whereas an energy detector is used to provide the onset and offset times of in-place motions, such as sitting down and standing up. It is shown that motion classifications give different results when performed forward and backward in time. The number of classes, thereby classification rates, considered by a classifier, is made varying depending on the current motion state and the possible transitioning activities in and out of the state. Two different examples are given to delineate the performance of the proposed approach under typical sequences of human motions.

This study introduces a cognitive radar system for autonomous robotic navigation and presents results of simulations and experiments. The study focuses on the role of memory within autonomous cognitive radar systems, examining the impact that working memory has on navigation performance. A suitable radar architecture is derived, inspired by basic natural cognitive processes. It is then implemented and tested in laboratory conditions with an appropriate technology demonstrator on-board a ground mobile robot. To the best of the authors’ knowledge, this work presents the first results of an experimentally realised cognitive radar system that explicitly incorporates a short-term memory function for robotic navigation.

This study considers the Bayesian programming methodology for recognition and classification of radio emission sources. A mathematical model of Bayesian programming proposes forming a family of probability distributions based on known parameters contained in a training sample (database). The correlations between the object classes have been estimated according discussed methodology. The received assessment has been used to separate procedures of recognition and classification of radar emission sources. The simulation of methodology has carried out for four parameters of radar signals (frequency range, pulse width, pulse repetition interval and radar rotation frequency) by used database with 346 classes and 16 types of radar. Based on the existing database of radar emission sources, it is possible to predict the probability of class recognition for the general population of objects, if its distribution is known. This study demonstrates the consistency of the Bayesian programming methodology for object identification in ELINT systems.

An active sensing system, such as radar, extracts the useful features of the targets of interest, by transmitting a certain waveform to a particular area and analysing the returned signal. There are two factors that are critical to the system performance, namely the receive filter and the transmitted waveform. Transmitted signal is among the most interesting factor affecting the radar system performance in complex environments. The waveform design problem is an important stage in the performance of cognitive radars. Here, the matched illumination approach is studied which specially addresses the issue of adaptive waveform design to maximise the signal-to-disturbance ratio that finally leads to an increased probability of detection. Different constraints on the designed waveform have been introduced in the literature. However, the focus is on the similarity constraint that controls the ambiguity function and modulus variations of the devised code. In more details, it is shown that this kind of constraint should be written based on the estimated background covariance matrix leading to a proposed whitened similarity constraint. It is shown by simulation that the proposed whitened constraint outperforms the well-known similarity one in resultant ambiguity function properties and probability of detection, assuming the same environmental conditions.

Time delay estimation (TDE) between receivers plays a significant role in various fields such as radar, sonar, global positioning system, and underwater communications. TDE is the first step in the localisation and tracking of the source. Estimation of the time delay of a passive sonar system in a reverberant environment is one of the most challenging tasks. For some decades, the well-known generalised cross-correlation (GCC) method has been implemented to estimate the time delay. By using various pre-filters in the GCC method, the quality of TDE has improved in various cases. In this work, the use of wavelet denoising for TDE in a passive sonar system has been proposed. The proposed method uses the denoised signal in a maximum likelihood estimator in the reverberant environment to estimate the time delay. The proposed method is efficient for the underwater reverberant environment.

Recognition of multi-function radar (MFR) work mode in an input pulse sequence is a fundamental task to interpret the functions and behaviour of an MFR. There are three major challenges that must be addressed: (i) The received radar pulses stream may contain an unknown number of multiple work mode class segments. (ii) The intra-mode and inter-mode knowledge of a modern MFR may be too flexible and complicated to be represented and learned through traditional hand-crafted features and learning models. (iii) The variable duration of each enclosed work mode makes the identification of the transition boundaries of adjacent modes difficult. To address these challenges and implement automatic recognition of MFR work mode sequences at a pulse-level, this study develops a novel processing framework based on a time series representation of MFR work mode sequence and sequence-to-sequence (seq2seq) long short-term memory network. The proposed method can not only automatically recognise multiple complexes modulated work mode classes in a pulse sequence. Still, it can also accurately identify the transition boundaries between each class by labelling the class information for each pulse. The experimental results showed the extended capabilities and improved performance of the proposed method over the state-of-the-art work mode classification methods.

Pulse repetition frequency (PRF) staggering is a well-established technique in the pulse-Doppler radar, used in order to increase the unambiguous range and Doppler and to minimise blind spots. The study offers a new staggering dimension that can increase the number of different staggering choices. The new approach is to add an inter-pulse coherent phase-coded overlay. The code is shorter than the number of pulses in each PRF batch and is repeated an integer number of times within the batch. A required property of the phase code is perfect periodic autocorrelation, namely zero autocorrelation sidelobes. In order to minimise the added complexity, the overlay codes use a two-phase or three-phase alphabet. As in a conventional PRF staggering, there are P coherent batches, each using a different PRF, with its matched phase-coded overlay. The performance analysis uses a conventional binary integration rule that declares detection if there were H , or more, individual detections in the P batches.

Most existing work on radar classification of micro-drones assumes that the received signal is wholly reflected from a single micro-drone. However, when there are multiple micro-drones in the observed scene, the superimposition of their micro-Doppler signatures increases the classification difficulty. In particular, it is even more challenging to determine if a specific type of micro-drones exists. In this study, a method for recognition of multiple micro-drones based on their micro-Doppler signatures via dictionary learning is introduced. First, the dictionary is learnt for each type of micro-drone by using the K-SVD algorithm on cadence-velocity diagrams (CVD) of training samples. The CVD is obtained by computing the Fourier transform of the time series of a complex time-frequency spectrogram. Subsequently, the sparse representation of the CVD of multiple micro-drones is obtained by the orthogonal matching pursuit algorithm with the learnt dictionary. Finally, a threshold detector is applied to the sparse solution in order to extract the components of multiple micro-drones. Experimental results using measured data, which are collected from hovering drones by a continuous-wave radar in an indoor environment, show that this dictionary-learning-based method achieves a recognition performance of 93% when half of the measured data are used for training.

Radar cross-section (RCS) of an object is a complex function of various geometric variables, frequency and angles of incidence. In this work, an artificial intelligence solution is provided to predict the non-deterministic characteristics of RCS using the supervised machine learning algorithm that involves Gaussian process (GP) regression. A parametrised aircraft model is used to generate training data where five variables are selected as predictors while the response is chosen to be monostatic RCS in the azimuth plane. To provide a comparison of GP modelling-based predictions, shooting and bouncing rays-based multi-frequency RCS simulations are used and the results show good agreement. To further validate the GP-based modelling approach, the data of a design point is compared with the measured RCS of 1:8 scaled-down aircraft model, which confirms the accuracy of the proposed methodology. Good prediction capabilities of GP regression for RCS evaluation of complex geometries and requirement of small data set make it an excellent tool for exploring the large design space as well as integration into multi-disciplinary design optimisation environments.

This study proposes a ground-based threat probability model based on a Markov chain to evaluate multiple ground threats faced by the platform during flight. On this basis, a new threat evaluation and jamming allocation system is constructed by combining the model with jamming resource allocation methods. In the threat evaluation phase, the interaction relationship between the platform and radar is represented by the transfer intensity among radar states according to the domain expert knowledge. Then, the potential state probability of radars in each time period is deduced and converted into the danger value as the objective to be optimised in the next stage. In the jamming allocation phase, the system considers some effects such as jamming technologies, multi-channel, the uncertainty of the threatening environment and other factors, and simulates the process of ground threats. There is traversing all jamming combinations at each period to select the best jamming strategy. This study also designs a multi-threat environment to verify the effectiveness of the system and test its relative performance improvement. Compared with the original threat evaluation and jamming allocation system, the new system has proved its feasibility and better application value.

Most research regarding passive radar exploiting `illuminators of opportunity', such as FM radio, has focused on detecting and tracking targets. This study explores adding automatic target recognition (ATR) capabilities to such systems. The ATR algorithms described here use the radar cross-section (RCS) of potential targets, collected over a short period of time. The received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. One proposed algorithm uses a coordinated flight model to estimate aircraft orientations, while a more sophisticated algorithm uses an extended Kalman filter to estimate the target orientations along with measures of uncertainty in those estimates. In both cases, the orientations are estimated using velocity measurements obtained from a tracking algorithm. The radar return of each aircraft in the target library is simulated as though each is executing the same manoeuvre as the target detected by the system. To improve the robustness of the result, the more sophisticated algorithm jointly optimises over feasible orientation profiles and target types via dynamic programming.

The ionosphere causes serious phase error in P-band inverse synthetic aperture radar (ISAR) systems, which makes it difficult to obtain a high-quality image. Recently, the convolutional neural network (CNN) has gained much attention in signal processing, and it can automatically extract features to realise an image super-resolution reconstruction. As a popular CNN-based network, U-net can work with less training samples. Hence, the authors are interested in exploiting and modifying the U-net to enhance the P-band ISAR imaging. In this study, in light of the analysis of the effect of the ionospheric total electron content on the ground-based P-band radar echo signal, a novel ISAR imaging method is proposed for the ionospheric effect correction based on the modified U-net and polar formatting algorithm (PFA). The PFA is performed for the phase error coarse compensation. Then, the phase error fine compensation is exploited by the trained U-net. The proposed method can adapt the ionosphere disturbances and show high performance in imaging quality and computational efficiency. The simulation results show the effectiveness of the proposed method.

Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal-to-noise ratio (SNR) levels from -14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at -6 dB and close to 100% when SNR > -2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.

In this study, the problem of the low probability of intercept (LPI) performance optimisation for a joint radar-communications system (JRCS) is investigated, which can simultaneously estimate surveillance channel parameters from the target returns and decode the received communications signals. The primary purpose of the proposed LPI performance optimisation strategy is to improve the LPI performance of a JRCS by optimising the energy spectral density of radar waveform design and the communications power allocation while maintaining a predetermined mutual information threshold for parameter estimation and a certain communications data rate for information delivery. The traditional isolated sub-band (TISB) situation, radar isolated sub-band (RISB) situation, and communications isolated sub-band (CISB) situation are analysed. Subsequently, the technique of Lagrange multipliers and the Karush-Kuhn-Tuckers optimality conditions are employed to solve the resulting optimisation problems. Moreover, the successive interference cancellation method is adopted to process the received radar-communications compound signal. Finally, several numerical simulations are conducted to demonstrate the theoretical calculations and to validate the effectiveness of the proposed LPI performance optimisation strategy. It is also shown that the achievable LPI performance in RISB and CISB situations is much better than that in TISB situation.

Collocated multiple-input multiple-output radar can track multiple targets simultaneously by transmitting multiple orthogonal beams and adopting the digital beamforming technology. In this scenario, the authors propose a joint power and time width allocation approach, which combines a cognitive tracking model based on the posterior Cramer-Rao lower bound (PCRLB) and the square-root cubature Kalman filter. The aim of the optimisation model is to improve the velocity estimation accuracy by minimising the sum of the PCRLBs of the velocity of multiple targets, which are predicted based on the feedback information from the cognitive tracking model. However, there are two finite working resources in the optimisation model: the total transmit power of multiple beams and the total effective time width of each corresponding signal. The resource allocation problem can be transformed into a non-convex optimisation problem, which can be converted into a standard convex optimisation problem by the linear relationship between the optimal power and the optimal time width. In this way, the joint power and time width allocation scheme is established as an adaptive closed-loop system. Numerical results demonstrate that the velocity tracking accuracy can be improved efficiently by the proposed algorithm.

The authors present a non-traditional, parametric method for velocity, angle, and range estimation in a frequency diverse array radar. Unlike the traditional beamforming techniques, the proposed scheme transmits an omni-directional sinusoid regardless of the target locations. They propose a simple sampling strategy, which eliminates the need for employing a bank of bandpass filters at the receiver. Under the proposed sampling scheme the received data follows a convenient low rank model. They exploit this model to design a fast and accurate parametric estimation algorithm. Their velocity and range estimation steps employ known spectral analysis techniques. For angle estimation, they propose a new grid-less sparse recovery algorithm. The resulting methods are applicable to any arbitrary array geometry. Furthermore, they propose an efficient method to mitigate jamming. They also provide necessary guidelines to avoid ambiguity and achieve the desired resolution performance. The Cramer-Rao lower bound for the estimation problem is derived. The utility of the proposed method is demonstrated via numerical simulation results.

To deal with problems of uncertain modulations and multiple pulse widths in pulse waveforms (PWs) during the identifying procedure, a novel specific emitter identification (SEI) method based on PW images (PWIs) and convolutional neural network is proposed. In the method, a more accurate signal model is built with considering the rising, steady and falling part of the whole PW based on actual radar pulse signals. PWI achieves transforming time-domain waveforms to 2D binary images as an SEI analysis feature. To match the PWI feature, a convolutional neural network with the small convolutional kernel is designed to extract the subtle features and finish the supervised training. By tuning the parameters of the convolutional neural network, it completes a balance of consuming time and identifying accuracy. Simulations and experiments indicate that the proposed method outperforms the existed methods on identifying radar individuals with uncertain modulations and multiple pulse widths in the intercepted pulse signals.

Funding: UK Science and Technology Facilities Council ST/N006569/1 (DR). This study presents a convolutional neural network (CNN) based drone classification method. The primary criterion for a high-fidelity neural network based classification is a real dataset of large size and diversity for training. The first goal of the study was to create a large database of micro-Doppler spectrogram images of in-flight drones and birds. Two separate datasets with the same images have been created, one with RGB images and other with grayscale images. The RGB dataset was used for GoogLeNet architecture-based training. The grayscale dataset was used for training with a series architecture developed during this study. Each dataset was further divided into two categories, one with four classes (drone, bird, clutter and noise) and the other with two classes (drone and non-drone). During training, 20% of the dataset has been used as a validation set. After the completion of training, the models were tested with previously unseen and unlabelled sets of data. The validation and testing accuracy for the developed series network have been found to be 99.6% and 94.4% respectively for four classes and 99.3% and 98.3% respectively for two classes. The GoogLenet based model showed both validation and testing accuracies to be around 99% for all the cases. Postprint