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Most avian radars use the resonance effect within the S-band to estimate the maximum bird detection distance. However, the specified range (2 km) for a standard avian target (SAT) with a 0.5 kg mass and 0.025 m2 radar cross section (RCS) as reported by the US Federal Aviation Administration is too small to cover the airfield clear zone at airports. The research shows that the measured RCS value of a pigeon is 0.25 m2, and furthermore, the authors argue that Ku-band avian radar can detect a pigeon as a SAT at a longer range, 12 km. They attribute this magnification effect on bird RCS due to the corner reflector effect related to wingbeats. During certain flapping motions, the wings and body of birds in flight act as corner reflectors; thereby, when radar echoes fall upon either face will impinge onto the other face, and subsequently reflected towards the illuminator, thus contributing considerably to bird RCS. The measured RCS of a pigeon is consistent with the theoretical RCS of a dihedral corner reflector. An expanded coverage of the avian radar larger than the main area of an airfield clear zone can improve the detection probability and reduce bird strike hazards.

Ground moving target indicator (GMTI) RADAR system frequently incorporates space-time adaptive processing for successful moving targets discrimination in a highly cluttered environment. The minimum detectable velocity (MDV) of the target gets affected due to the added clutter impairments such as foliage penetration and angle independent channel mismatch. A novel clutter model is postulated in this work that includes assessment enacted in view of both angle and Doppler clutter impairments at a time instead of their individual analysis as in the reported literature. The performance of the GMTI RADAR in terms of the signal-to-interference-plus-noise ratio loss is evaluated using the extrusive beamforming methods namely sample matrix inversion and principal component canceller. In high decorrelated clutter environment, the later outperforms in terms of the MDV, making use of a lesser number of training snapshots. The approach is validated with mathematical modelling and numerical simulations.

The deployment of deep neural networks in real-world radar-based human activity classification is largely hindered by both the high computational cost and the large amount of training samples. In this study, the authors propose a method to simultaneously reduce the computational burden and the number of labelled training samples. Different from previous transfer learning methods that simply prune fully-connected layers and modify the weights of the convolutional layers, they enforce filter-level sparsity in the transfer learning from ImageNet to the micro-Doppler measurements. Through the sparsity-driven transfer learning, unimportant convolutional filters can be identified and then be pruned. Therefore, a light but effective transfer learned net can be obtained. The experiments demonstrate the sparsity-driven transfer learned VGG-19 Net not only outperforms convolutional neural networks trained from scratch by nearly 10% accuracy but also gives an 11 × reduction in the number of parameters and a 10 × reduction in computing operations compared with the original VGG-19 Net.

In this study, the authors propose a robust measurement validation algorithm for radar target tracking in a heavy-clutter environment. This algorithm defines the prior information with the radar measurement information from the estimated target trajectory. An additional validation gate is set up within the conventional validation gate using the statistics of the prior information then only a part of the validation measurement is selected to update the target state. To verify the effectiveness of the proposed algorithm, the authors designed a simulation for target tracking. The simulation results show that the proposed algorithm has a lower root mean squared position error compared to conventional algorithms in a heavy-clutter environment.

The authors investigate the problem of a compressive sensing radar (CSR) trying to improve the detection performance with strong noise and clutter. Structured measurement matrix is employed to achieve pulse accumulation and then improve the detection performance of CSR under low signal-to-noise ratio (low-SNR). Targets' Doppler frequency can be firstly detected when the received signal is measured by the proposed Doppler filter measurement matrix. With the estimated Doppler frequency, the pulse accumulation measurement matrix (PAMM) is devised to achieve the integration of the received pulses. Different from the existed methods, with the proposed PAMM, target's delay information of all velocities can be estimated simultaneously with just one optimised reconstruction. A modified K-targets-PAMM (KPAMM) scheme is then introduced to achieve further detection performance improvement. The target's range migration problem is also considered in this study and a long-term KPAMM (LT-KPAMM) detection scheme is proposed by devising a migration compensation measurement matrix. Grouped pulse accumulation can be achieved by the LT-KPAMM to avoid the influence of the range migration. Numerical results demonstrate that the proposed measurement matrix based method can obtain good detection performance improvement under low-SNR.

In this study, a low probability of intercept (LPI)-based orthogonal frequency division multiplexing radar waveform design strategy is developed for target time delay estimation in a cooperative radar-communications system, where the communications base station (CBS) is able to receive and process the radar emissions. The basis of the LPI-based radar waveform design strategy is to utilise the optimisation technique to design the transmitted waveform of a radar system for specified target time delay estimation accuracy in order to achieve enhanced LPI performance. The expression for Cramer–Rao lower bound is analytically derived and utilised as a performance metric for target time delay estimation performance. The resulting optimisation problem is solved by the approach of linear programming. Several numerical results are provided to verify the superiority of the proposed radar waveform design algorithm in terms of the LPI performance of the radar system. It is also shown that the radar transmitted power can be significantly reduced with the help from the CBS which plays a role of the bistatic radar receiver.

Electromagnetic warfare is the most extensive and most hidden theatre of battle in modern warfare. To enhance the jamming effectiveness of a cooperative jammer platform against the threat of a radar net, a combinatorial-optimisation-based threat evaluation and jamming allocation (COTEJA) system is proposed. This COTEJA system fully considers the confrontation analysis in the jammer-radar process, including the interactions between radars, jammers, and jammer-radar pairs, and emphasises the realisation of cooperative jamming strategies. The cooperative jamming strategies include the combination of jamming techniques and optimisation algorithms for the objective function. The performance of the COTEJA system is evaluated through a combat mission that considers a platform with four jammers attacking five threats. In addition, the extended permutation-based differential evolution algorithm is used for the first time to optimise the jamming coding matrix, which effectively reduces the danger value of netted radar under multiple constraints. The numerical results reveal that the COTEJA system can make the optimal jamming decision within 1 s, which improves the survival ability of the platform in a complicated electromagnetic environment.

In this work, an extensive correlation between two commonly used radar cross-section (RCS) computation techniques, i.e. shooting and bouncing rays (SBR) and multilevel fast multipole method (MLFMM), is performed. Numerical results are compared with the measured data, obtained from an anechoic chamber, for a 1:8 scaled down model of a fighter aircraft. It is ascertained that the SBR method, notwithstanding its numerical simplicity, can provide qualitatively similar and reasonably accurate RCS results for an object of varying complexity.

Autonomous navigation of platforms in complex environments has a key role in many applications. However, environmental conditions could negatively affect the performance of electro-optical sensors. Hence, the idea of using radar odometry (RO) has been recently developed. However, it suffers from the presence of outliers in the scene as its electro-optical counterparts. This work presents a method to classify radar echoes as inliers or outliers for two-dimensional RO, based on their range rate and bearing angle. The range rate and bearing angle are, in fact, combined to give a classification value, different for each target. At each acquisition time, the median of all classification values is computed. Since classification values of stationary targets, i.e. the inliers, cluster around the median, while moving targets, i.e. the outliers, exhibit larger distance from the median, stationary targets and moving targets can be separated. This is also useful for sense-and-avoid purposes. The method has been tested in the simulated scenario to show effectiveness in detecting outliers and in a real-case scenario to demonstrate significant improvement in the reconstruction of the trajectory of the platform, keeping the final error around 10% of the travelled distance. Further improvement is envisaged by integrating the method in the target tracking strategy.

This work presents a random sequence coding scheme for joint radar-communication waveforms in scenarios that require a high degree of data covertness. The authors’ goal is to embed the resultant signals in a parameter space of the probability density function of statistical distribution, leading to the samples of the underlying random process being used for waveform construction. The simulated performance shows that an unfriendly interceptor will be penalised significantly compared to an authorised receiver when the SNR is greater than −20 dB. The proposed method is well-suited for asynchronous, ad-hoc communications with a high low probability of intercept/low probability of detection potential. The authors also propose an algorithm to improve the performance of thus created communication signals when they are used simultaneously for radar. Experimental results for both communications and radar are also presented.

A two-component compound Gaussian model is proposed for coherent radar sea clutter, with each component having its own spatial correlation. The slow component, centred near zero Doppler, is modulated in both frequency and intensity by gravity waves, while the fast component, offset in Doppler frequency, models scattering from breaking waves. The intensity of each component is represented by an independent gamma variate with its own scale and shape parameter, with the overall texture distributed as the sum of these two gamma variates, denoted G2. The model is compared to data collected at medium grazing angles with the DST Ingara airborne X-band radar. It can produce realistic simulations of coherent radar sea clutter.

Standard Doppler processing techniques are not capable of controlling strong clutter sidelobes when non-uniform pulse repetition frequency (PRF) radar waveforms are utilised. In this study, the authors propose a weighted bandlimited interpolation (WBI)-based Doppler processing technique, which generates a spectrum similar to that of uniform PRF waveform processing, successfully suppressing undesired spectral sidelobes. Three versions of WBI are introduced: WBI windowed, WBI adaptive, and WBI-principal components inverse (WBI-PCI), and their detection performance is quantified by generating receiver operating characteristics and estimating associated signal-to-noise ratio (SNR) losses. The performance of the algorithms is also analysed in terms of computational complexity. The superiority of the proposed WBI techniques compared to existing techniques is also demonstrated. WBI-PCI is an attractive choice for non-uniform PRF waveform processing, given its low SNR loss, low complexity, and low training requirement.

Experimental measurement results of automotive radar signal attenuation during various intensities of snowfall at the current automotive radar frequency (77 GHz) and low-terahertz (THz) (100-300 GHz) frequencies are presented and compared in this study. The attenuation is characterised by measuring the ratio of the received power from a reference target through snow precipitation of various intensities and through the same path with no precipitation. Statistical analysis of the attenuation is presented. Higher attenuation is measured at a higher frequency, and also attenuation increases as snowfall rate and liquid water content in snowflakes increases. This study is fundamentally important to investigate the effect of adverse weather conditions on low-THz radar performance in comparison with the current automotive radars operating in the traditional mm-wave band. In addition, the effects of low-THz wave attenuation and scattering due to typical contaminants formed on the radome of automotive radars and reflection from common objects on the road are presented.

Realistic simulation of radar sea-clutter is extremely important to evaluate radar detection algorithms and to stimulate radar processors during development and testing. Here, the authors compare a number of coherent sea clutter simulation methods designed to model not just the amplitude distribution, but also the time and range varying Doppler spectrum. This is important as the Doppler spectrum plays a key role in determining the statistics that are important for coherent processing. The models studied include the evolving Doppler spectrum model, the spatial and temporal limited clutter model and the compound G2 model which captures many of the features from the other two. To analyse the models, simulated data are generated with parameters estimated from the Ingara medium grazing angle sea clutter data set. Comparison with the original data then allows for a comprehensive study of the strengths and weaknesses of each model over a range of polarisations and swell angles.

A method of applying repeat-pass synthetic aperture radar coherent change detection to forest-covered terrain is proposed and analysed. Backscatter from the canopy can be suppressed whilst the complex reflectivity of the ground is preserved by vertically beamforming multiple across-track image channels acquired on each pass. A multichannel coherence metric is formulated assuming a dual-layer (surface and volume) model of the scene. The optimal volume suppression is derived and then quantified using the interferometric random-volume-over-ground model for all pairs of channels. It is shown that good volume suppression is achievable with just three channels, and the resulting coherence estimation is reasonably accurate if the effective ground and volume scattering responses are of similar strength. Significant degradation occurs if knowledge of the ground height is inaccurate. Two aspects of radar design are discussed in detail: the selection of a wavelength that permits both adequate foliage penetration and high sensitivity to subtle changes in the ground clutter, and the sensitivity above noise required to measure the ground backscatter, which is typically weak at foliage-penetrating wavelengths.

The digital radio frequency memory repeat jammer, which replicates transmit signals and transmits them back to a radar receiver to mimic real targets, is a notably efficient electronic countermeasure device. It has been pointed out that the colocated multiple-input multiple-output (MIMO) radar has an additional advantage in combatting with repeat jammers whose amplifiers operate in the saturation mode and thus can only transmit constant-modulus waveforms, because the amplitude modulation of angular waveforms is difficult to simulate for them. Two waveform design criteria are presented, and both suppress a mutual correlation between angular waveforms and corresponding constant-modulus jamming signals. The two approaches differ on whether the receive beamforming operation is considered. Numerical results validate that the two approaches can both efficiently suppress the mutual correlation and that the second approach outperforms the first approach in terms of the peak sidelobe level.

Modern electronically scanned-array radars can switch between beams in a matter of microseconds. This allows great latitude in constructing the beam schedule. From the point of view of an electronic support (ES) receiver, what are the implications? Repeated search tends to make the radar's beam schedule more regular and predictable. Can this be exploited by the receiver to intercept the radar more quickly? What can be done about strongly aperiodic beam schedules? In this study, the author proposes two opposing models for a beam-agile radar. One is a periodic model, accounting for radars with a predictable, repetitive search operation. The other is a stochastic model, in which the beam schedule is modelled by a continuous-time Markov chain, accounting for strongly aperiodic radars. Taking the published beam schedule of a hypothetical, agile radar, numerical experiments are conducted under two different assumptions about the ordering of beam dwells in each radar scan. Under the assumption that the dwells are sequentially ordered, it is found that the beam schedule appears periodic from the receiver's point of view, to a good approximation. On the other hand, when the radar dwells are randomly shuffled on each scan, the Markov-chain model proves useful in guiding receiver settings.

Due to the presence of noise and range resolution variation, each attribute of the scattering centre (SC) extracted from high-resolution range profile (HRRP) data is a random variable fluctuating around the real attribute. For the scatterer-based target recognition method, the extraction error will disturb the similarity measurement between SC sets and adversely affect the recognition performance. In this study, a novel approach for HRRP target recognition is proposed via the statistics-based SC set registration. The attribute set of SCs is first characterised by a Gaussian mixture model (GMM) in which each Gaussian component represents the corresponding SC attributes containing the amplitude and position. Besides, the noise power and range resolution are taken into account into the GMM parameters, which enhances the recognition robustness against noise and resolution variation. Afterwards, based on the different stability of SC attributes against the variation of target-sensor orientation, the weighted Gaussian quadratic form distance is developed to register the test Gaussian mixture with the templates. The resulting recognition method considers the statistical property of the estimated attribute of the SC and exhibits robust recognition performance. Experiments conducted on the measured data demonstrate the validity and robustness of the proposed method.

Multi-mode spread-Doppler clutter (SDC), caused by a stratified ionosphere, affects the detection performance of over-the-horizon radar on slow ships. To improve a multiple-input multiple-output over-the-horizon radar (MIMO-OTHR) system's performance and to mitigate the lingering effect of SDC, a novel multi-mode SDC suppression method based on full-mode spatial separation is proposed here. Furthermore, authors' method can estimate the elevation angle of direction of departure (DOD) and direction of arrival (DOA), contributing to mode localisation. By applying the bi-orthogonality of matrices, the cost function of single-mode component analysis is constructed under a least square criterion. By iteratively solving for the cost function, and by applying a systematic multistage decomposition, the elevation DOD and DOA and the slow-time Doppler information of each mode are obtained sequentially. Compared with classical SOBI and SS-SOBI methods, the proposed method improves the output signal-to-clutter-plus-noise ratio for spatial filtering. The estimated DOD and DOA do not need to be paired, reducing the angle error inherent in the SR method while performing the transmit-receive cascade refinement searching step. Simulation results demonstrate the excellent performance of the proposed method, which is beneficial to improve the ability of MIMO-OTHR to detect slow ships and determine the propagation mode.

The use of random noise waveforms is investigated for radar surveillance and their performance is compared with traditional chirp waveforms based on the correlation and relative ratio of received to transmitted powers after reflections from simple targets and terrain. For simple shapes, the correlation of the two signals was similar in value and pattern with respect to the incidence angle. The reflection from terrain clutter gave smaller correlations for the noise waveform indicating that it may be less susceptible to false alarms. Additional simulations were performed for a hummer vehicle target using terrain clutter models to create receiver operating characteristic curves. It was found that the noise waveform performs as well as the chirp for cases of strong clutter response, and much better for cases of weak clutter response.

RAD-DAR is a frequency-modulated-continuous-wave radar demonstrator, working at X-band, completely designed and constructed by the authors' research group following the ubiquitous-radar concept. After introducing its main hardware and software blocks, with special emphasis to its off-line signal processing and data processing, this study presents the results of a drone detection test, where RAD-DAR achieved a DJI-Phantom 4 detection and tracking at a range up to 3 km. The system performance is discussed with different flights including an attack manoeuvre, and a free flight, which helps to highlight their advantages in surveillance tasks due to the RAD-DAR staring nature. Furthermore, range, speed and azimuth accuracies are discussed, considering the drone global positioning system data. Finally, this study shows a statistical radar cross-section study based on the processed data of the drone and attempts to classify this target by means of Swerling models.

Space-time adaptive processing (STAP) is supposed to be a key technique for detection of slow-moving targets in airborne radar applications. To overcome the difficulties of obtaining lots of independent and identical distributed secondary data samples, many sub-optimal STAP algorithms are proposed such as reduced-dimension STAP, reduced-rank STAP, or the sparse-recovery-based STAP. In this study, based on the observation that the STAP filter weight is sparse, a novel clutter-dependent reduced-dimension STAP is proposed. In the proposed approach, the sparse feature of the weight vector in the angular-Doppler domain is utilised to design the reduced-dimension transform matrix. Simulation results show that the output signal-to-interference-plus-noise ratio loss is just 3 dB with only one secondary data sample in the extremely heterogeneous clutter environments.

The micro-motion features induced by space targets provide effective information for target classification and recognition. This study introduces the equivalent rotational velocity (ERV) estimation for precession parameter estimation of the cone-shaped target with inverse synthetic aperture radar image sequence. On the basis of the rotation matching relationship of precession cone-shaped target, the estimated ERV is used to build an eigenfunction, and the parameter searching set is obtained. Through theoretical analysis, the coupled parameters of the eigenfunction will concentrate on a curve on the searching plane. Thus, the parameter searching set is reduced to the neighbouring area of this curve, which reduces the computational burden greatly. Finally, the parameters are decoupled by the reconstructed radar observation angle, and the precession parameters such as precession period, precession angle, average observation angle, radial length and bottom radius of the target are precisely estimated. The proposed algorithm requires no prior information and it has high computational efficiency. Simulation results with electromagnetic data illustrate the validity of the proposed method.

Golay binary complementary sequences are used to code quadriphase waveforms. The Gaussian minimum shift keying coding scheme is used to produce waveforms with constant amplitude, and continuous and differentiable phase. To satisfy spectral requirements, the waveforms are further spectrally optimised by a spectrum-optimisation process, making the final waveforms implementable. The extra-low sidelobe level is achieved by the use of an extended mismatched filter on receive of pulse compression. To improve Doppler tolerance, Doppler-resilient complementary waveforms are derived that provide first-order suppression to the raised range sidelobes introduced by the Doppler frequency shift in returned signals. This study is an expanded version of a conference paper presented at 2018 International Radar Conference held in Brisbane, Australia.

The authors explain the development of a cost-effective frequency-modulated continuous wave (FMCW) radar test system in the E-band with a simple-structure, and demonstrate it for an automotive application using an FMCW radar chipset evaluation board and an FMCW radar. The system consists of a down-converter, based on a fundamental mixer with a phase-locked loop-Gunn oscillator, and a mixed domain oscilloscope or a conventional digital oscilloscope. It is able to evaluate an FMCW signal in the time domain, such as the chirp rate, chirp length, chirp width, linearity, and the relative power level.

In the presence of the mainlobe jamming, the performance of the modern radar system would degrade significantly, i.e. increasing probabilities of false alarm and loss detection. In this study, the authors consider the jamming suppression problem in noise environment for the distributed radar with single transmitter and multiple receivers, where the multiple jammings enter into all the receivers through the main beam of the antennas. A framework based on joint blind source separation (JBSS) is proposed. First, the signal model accounting for both the target and jammings is developed. Second, the target and the jamming signals are separated by exploiting the generalised non-orthogonal joint diagonalisation JBSS method. Then, the separated target signals are employed to find the target location with the elliptic location method. Finally, they assess the performance in noise environment by numerical simulations, and the results show that the proposed algorithm can suppress successfully quite a lot different kinds of jamming signals, e.g. the noise-modulated and the digital radio-frequency memory jammings.

Radar systems emit a time-varying signal and measure the response of a radar-reflecting surface. In the case of narrowband, monostatic radar signal domain, all spatial information is projected into a radar cross-section (RCS) scalar. The authors address the challenging problem of determining shape class using monostatic RCS estimates collected as a time series from a rotating object tumbling with unknown motion parameters under detectability limitations and signal noise. Previous shape classification methods have relied on image-like synthetic aperture radar or multistatic (multiview) radar configurations with known geometry. Convolutional neural networks (CNNs) have revolutionised learning tasks in the computer vision domain by leveraging images and video rich with high-resolution two-dimensional (2D) or 3D spatial information. They show that a feed-forward CNN can be trained to successfully classify object shape using only noisy monostatic RCS signals with unknown motion. They construct datasets containing over 100,000 simulated RCS signals belonging to different shape classes. They introduce deep neural network architectures that produce 2% classification error on testing data. They also introduce a refinement network that transforms simulated signals to appear more realistic and improve training utility. The results are a pioneering step toward the recognition of more complex targets using narrowband, monostatic radar.

The presence of fault antennas in bistatic multiple-input–multiple-output (MIMO) radar causes a significant degradation of angle estimation performance. In this study, the authors present a signal-processing scheme for bistatic MIMO radar with impaired receive array including array diagnosis as well as angle estimation. The damaged antenna in the receiver, which is incapable of receiving the target signals and leaves only the thermal noise generated within the receiver itself, leads to a decrease in the entropy of its received data. The dissimilarity of the entropy values between the damaged and normal receive antennas is used for array diagnosis. Thereafter, the useless noise data of the detected fault antennas is removed in the receive signal matrix, so that the successive data missing occurs along the row directions of the signal subspace matrix. A low-rank block-Hankel structured matrix is constructed from the incomplete signal subspace matrix, which can facilitate the exploitation of the matrix completion technique to recover that successive missing data. On the basis of the restored rotational invariance structure of the complete signal subspace, the estimation of signal parameters via rotational invariance technique can then be applied for angle estimation. Simulation results confirm the validity of the proposed methods.

This study investigates the problem of designing unimodular waveform with spectral compatibility for multiple-input multiple-output radar in the presence of multiple targets and the signal-dependent interferences. A new approach is proposed to optimise the spectrally compatible radar waveform subject to not only the constant modulus constraint and the similarity constraint but also the signal-to-interference-plus-noise ratio (SINR) requirements for targets, aiming at suppressing the radar energy in some space-frequency areas occupied by other cooperative communication systems. However, the formulated optimisation problem is NP-hard because of the existence of the non-convex constraints. Based on the dual ascent framework, the authors develop an iterative algorithm to solve this challenging problem. The receive filter is obtained at the beginning of each iteration with the off-the-shelf technique and an innovative method termed phase-only dual ascent method is proposed to get the desired waveform. Additionally, they analyse the performance of the waveforms designed under the global SINR constraint and the local SINR constraint, respectively. Moreover, the convergence of the proposed algorithm is discussed concretely. Finally, the numerical simulations are provided to show the validity of the proposed approach.

As of the large receiving gain from radar antenna and the little difference with targets in the time-frequency-spatial domain, the main-lobe interference has become an important challenge to radar detection. In recent years, suppressing main-lobe interference by using polarisation filtering has gained significant theoretical and practical progress. However, when there are multiple interference sources occurring in the radar main lobe, the performance of all existing methods is badly limited. With the idea of feature identification, a novel target detection method based on polarisation-space joint spectrum characteristic is developed, which effectively utilises the polarisation scattering feature difference between the target returns and the interference signal, instead of trying to suppress interference energy as usual. This proposed method can effectively enhance the capability of radar target detection under the background of multi-point source interference in main lobe. The simulation results show that the proposed method can detect the target in the background of the main-lobe multi-point source interference.

The microwave power source of non-coherent short pulse (NCSP) radar is a relativistic backward wave oscillator, which is a kind of vacuum electronics device. The moving target coherent integration method for the NCSP radar is a challenging task. Firstly, due to the mechanism of generating microwave pulses, the centre frequency and initial phase of the transmitting signal in the NCSP radar are random, and the envelope of the microwave pulse also has jitter. Therefore, the well-known moving target detection method cannot be carried out straightforwardly by the NCSP radar. Secondly, range cell migration (RCM) effects induced by target motion would result in serious integration performance loss. In this study, a method based on Radon–Fourier transform is proposed to the scenario of moving target coherent integration for the NCSP radar. Not only the non-coherent factors of transmitting signal but also RCM effects have been compensated carefully. Also, the staggered pulse repetition frequency technique is applied to overcome blind speed sidelobes effect. Numerical experiments are performed to demonstrate the effectiveness of the proposed method.

The authors consider the problem of multipath exploitation on adaptive radar detection of point-like targets in a multipath environment where a priori information is available. A new approach to exploit multipath returns with knowledge-aided adaptive target-detection regime is proposed. The authors model the received signal as the sum of direct-path and reflected-path return under the assumption of a zero-mean complex circular Gaussian noise with an unknown covariance matrix. The advantage of the proposed method is exploiting multipath returns with a priori knowledge of the reflecting environment, so that it has the knowledge of the reflected steering vector for a known actual direct-path steering vector. A Generalised Likelihood Ratio Test (GLRT) for the corresponding hypothesis testing problem is derived. It is shown that the devised detector also secures the Constant False Alarm Rate (CFAR) property regarding the unknown parameters of the noise. Performance comparison of the proposed detector with the existing well-known adaptive detectors is provided. It is presented that better-detection performance can be achieved by exploiting multipath with knowledge-aided adaptive radar. It is also observed that the devised detector has a small performance degradation in case of weak multipath return. © The Institution of Engineering and Technology 2019

As the importance of radar sensors for automotive vehicles has rapidly increased, the mutual interference between vehicle radars has become important. If two or more radars share the same transmitting frequency band, their interference effects are retained after low-pass filtering. These interference signals can degrade the performance of target estimation, as they increase the noise floor level and create ghost targets, consequently causing false alarms. Several studies on mitigating interference effects have been conducted. However, they only focused on suppression techniques. For a complete interference mitigation algorithm, the identification of the existence of interference signals and classification of their modulation techniques should precede the mitigation steps. Therefore, the authors propose a method for the identification and the classification of interference signals. They modelled five different radar modulation signals as interference signals, and extracted five features from the time-domain mixer output signals. The training of the classification model and validation of its classification performance is done using the support vector machine. The simulation results show that the existence of interference signals is identified with the accuracy of over 99%. Furthermore, the interference signals are effectively classified into their modulation types with an accuracy of over 96%.

This study proposes an artificial neural network-based method to classify road structures for automotive radar systems. Generally, road structures generate unwanted echoes called clutter, which degrades the performance of target detection, and may cause critical errors in autonomous driving modes. However, recognition and classification of each individual structure type is very difficult, because there are various types of structures made by different materials which cause ambiguity in the recognition and classification process. To deal with these classification ambiguities, the authors propose an artificial neural network-based recognition and classification method. Road structures are classified by applying artificial neural network to the frequency domain-received signals of automotive radar systems. Once the neural network has been trained with the received signals, it is used to determine the type of road structure with instantaneous received signals. The classification performance was evaluated by experimenting with the number of antenna elements and radar snapshots. In addition, to find the most suitable artificial neural network structure, the authors experimented with the number of nodes and layers. After completing a period of deep learning using actual experimental data, the total classification accuracy was about 95%.

This research proposes a novel linear frequency modulation (Linear-FM) random radar waveform and introduces a dechirping compression method. The proposed linear-FM random waveforms possess a thumbtack ambiguity function and low cross-correlation properties which are good for fighting against digital radio frequency memory repeat jammer similar to random noise waveforms. The dechirping compression method for the proposed linear-FM random radar waveform works by mixing the random radar echo signal with a local reference linear frequency modulated (LFM) signal to reduce the echo bandwidth and thus allows to use low-speed analogue-to-digital converter similar to the dechirping method for LFM waveform compression. Both numerical simulation and high-resolution circular synthetic aperture radar imaging experiment are conducted to demonstrate the good performance of the proposed waveform and the proposed dechirping compression method.

When the azimuths of clutter and targets are in the same beam bin, the clutter is called main-lobe clutter of the corresponding targets. In small-aperture high-frequency surface wave radar (HFSWR) systems, the angle spectrum of main-lobe clutter suffers from severe broadening under the influence of the smaller array aperture, which can affect the detection performance of moving vessels. This situation occurs because the target vessels are more easily submerged in this broadened angle spectrum and can hardly be detected. In this study, a novel two-dimensional cascaded algorithm for main-lobe clutter that is based on combining an adaptive selection strategy for the optimal training samples and the rotating spatial beam method is proposed to suppress the clutter in both the angle domain and range domain. First, the correlation between training samples is analysed with the angle-Doppler joint eigenvector method. Then, the samples that are similar to the cell under test serve as training samples. Finally, the secondary beams that have Euclidean distances closest to the main beam are chosen. The experimental results of simulation and measured data confirm that the proposed approach provides far superior suppression performance and has strong robustness against array amplitude-phase errors and beam deviation.

Recently, radar target detection based on fractal theory has received great attention. Especially in the sea clutter background, fractal modelling, fractal dimension, and multifractal spectrum are widely used in radar signal processing. Here, a novel target detection method based on cross-correlation singularity power spectrum (CSPS) is proposed. Firstly, the CSPS theory and algorithm is derived based on SPS; secondly, based on the SPS and CSPS analysis of radar sea clutter, radar target detection parameters are designed, and the detection flow and algorithm based on the CSPS method is presented in detail. The proposed methodology is tested on sea clutter, both with and without targets, from the Ice Multi-parameter Imaging X -Band radar data set. The simulation results indicate that the proposed method performs better than the conventional multifractal-based methods and traditional CFAR methods, and the detection probability of detecting low-observable targets within sea clutters is close to 100%.

In this study, to reduce the performance degradation due to iron-clutter, the authors propose a suppression method for stationary clutter under iron-structures for an automotive radar using a frequency-modulation continuous-wave (FMCW) radar. The presence of a large number of iron-structures such as iron-bridges, soundproof walls, and iron-tunnels creates a harsh environment that deteriorates the associated radar detection performance owing to clutter. To mitigate the adverse effects of clutter, they propose a clutter suppression scheme using up- and down-chirp signals based on an analysis of signal characteristics. In particular, it is shown that the stationary clutters can be suppressed by making that the phases of the up-chirp and down-chirp are similar. To experimentally verify the proposed method, 77 GHz FMCW radar data for adaptive cruise control was acquired along a road on which several iron-structures were located. The experimental results show that using the proposed clutter suppression scheme, the frequency of the moving target was detected well using the proposed clutter suppression scheme in an iron-clutter-rich environment. Through the application of the proposed clutter suppression scheme, stable target detection performance of the radar can be obtained.

In this study, the authors consider a low-complexity robust adaptive beamforming problem in a collocated multiple-input multiple-output (MIMO) radar. This study is motivated by the fact that in practical applications, the conventional adaptive beamforming algorithm for MIMO radar requires a large computational complexity and suffers from a great performance degradation because of the finite number of training snapshots, the desired signal steering vector mismatch and the corruption of training data by the desired signal. Since the dimension of the virtual steering vector of the MIMO radar is relatively large, the proposed method can estimate the covariance matrix by using a low-complexity method to effectively improve the computational efficiency of the adaptive beamforming algorithm and the robustness of the beamformer against the covariance matrix uncertainty. Besides, based on the estimated covariance matrix, the proposed method can also correct the desired signal steering vector mismatch to efficiently prevent the desired signal cancellation phenomenon. Simulation results demonstrate that the performance of the proposed method is always close to that of the optimal processing in a wide range of signal-to-noise ratio or of the number of training snapshots.

Both polarimetry and Doppler characteristics can be employed in remote sensing applications. Doppler information provides insight into the dynamic properties of radar targets, and polarimetry is associated with the intrinsic attributes of targets. Determining how to obtain accurate polarisation measurements from Doppler information has been a concern. For high-speed targets, the relativistic effect on radar wideband echoes may be notable. With the conventional matched filter, this scale effect may produce significant amplitude and phase errors between two columns of the polarisation scattering matrix (PSM), except for signal-to-noise ratio (SNR) loss. On the basis of the Taylor series expansion, the amplitude and phase expressions are derived in this study. Theoretical analysis shows that the amplitude and phase errors are related to the time–frequency allocation of the orthogonal waveforms and that the accuracy of the PSM is more susceptible to scale factors using frequency modulation waveforms than phase-coded waveforms. To suppress these amplitude and phase errors, a compensation method is proposed for orthogonal linear frequency modulation signals. Furthermore, the performances of the SNR loss and the velocity error effect are given. Finally, some numerical experimental results are provided to illustrate the effectiveness of the compensation.

This study addresses a radar architecture to design a waveform that dynamically fits into the gaps in the radio-frequency spectrum and that matches the target scattering properties. The optimisation metric considered is the mutual information between the received signal and the target impulse response, the maximisation of which makes the radar waveform illuminate a wider number of target spectral components. The proposed scheme is conceived to be part of a cognitive – or fully adaptive – system, which adapts its waveform in a perception-action cycle, by using a trade-off between the observation time and the desired performance. In this work, particular attention is paid to analyse how the chosen metric is influenced by external parameters such as the target, interference and clutter power and radar parameters such as the observation time and the transmit power.

Coherently combining dual identical radars is a promising technology that can obtain a 23 times signal-to-noise ratio promotion over a single radar. The basic principle of dual-radar coherently combining is to adjust transmitting/receiving time and phase of each radar via the coherent parameters (CPs), with the purpose of achieving coherent-on-transmit and -receive. However, this technology has not been researched systematically, especially in the target moving scenario. In this paper, a signal processing paradigm for dual-radar coherent combination is proposed. Two stages exist in the proposed paradigm: coherence preparation stage and coherence maintenance stage. The former stage is considered as a means of bootstrapping to the latter stage, by offering the necessary coherent combining information (predicted transmit CPs and estimated synchronisation errors). And a great coherent gain is obtained in the latter stage, which allows dual-radar works in a fully coherent transmit and receive mode. In both stages, CPs estimation and prediction are the indispensable key steps, whose corresponding algorithms and constraints are developed, respectively. Given to this proposed paradigm, a dual-radar coherently combining example is implemented under a ballistic target tracking scenario, whose results verify the effectiveness of the proposed paradigm.

We demonstrated a vehicle detection and classification method based on Long Term Evolution (LTE) communication infrastructure based environment sensing instrument, termed as LTE-CommSense by the authors. This technology is a novel passive sensing system which focuses on the reference signals embedded in the sub-frames of LTE resource grid. It compares the received signal with the expected reference signal, extracts the evaluated channel state information (CSI) and analyzes it to estimate the change in the environment. For vehicle detection and subsequent classification, our setup is similar to a passive radar in forward scattering radar (FSR) mode. Instead of performing the radio frequency (RF) signals directly, we take advantage of the processing that happens in a LTE receiver user equipment (UE). We tap into the channel estimation and equalization block and extract the CSI value. CSI value reflects the property of the communication channel between communication base station (eNodeB) and UE. We use CSI values for with and vehicle and without vehicle case in outdoor open road environment. Being a receiver only system, there is no need for any transmission and related regulations. Therefore, this system is low cost, power efficient and difficult to detect. Also, most of its processing will be done by the existing LTE communication receiver (UE). In this paper, we establish our claim by analyzing field-collected data. Live LTE downlink (DL) signal is captured using modeled LTE UE using software defined radio (SDR). The detection analysis and classification performance shows promising results and ascertains that, LTE-CommSense is capable of detection and classification of different types of vehicles in outdoor road environment., This paper is a postprint of a paper submitted to and accepted for publication in IET Radar, Sonar & Navigation and is subject to IET Radar, Sonar & Navigation Copyright

With the rapid development of the integrated circuit manufacturing technology and the radar technology, the demand for miniaturisation, low power consumption, and real-time performance of synthetic aperture radar (SAR) imaging system is becoming higher and higher. By designing reconfigurable IP cores and configuring parameters through CPU, a reconfigurable missile-borne SAR imaging SoC chip based on IP reuse is designed to meet this urgent demand. Using 0.13μm CMOS process, the SoC chip has been taped out and verified successfully. Compared with the traditional ‘DSP + FPGA’ platform, this chip can ensure the real-time performance of the missile-borne SAR imaging system and has more advantages in scale and power consumption.

Long-time coherent integration is an effective method to implement low-observable target detection in a complex scenario. Radon-fractional Fourier transform (RFRFT) can compensate the range and Doppler migration simultaneously and obviously improve the signal-to-clutter-noise ratio (SCNR) via long-time coherent integration. However, RFRFT uses a combination of moving parameters, i.e. initial range, velocity and acceleration, to search targets, increasing the computational burden. In this study, a novel two-stage RFRFT is proposed to detect low-observable targets. In the first stage, by calculating the spectrum similarity between adjacent bins, regions of interest (ROIs) can be detected in the range–Doppler domain. The processing in the first stage reduces the global search to a limited number of ROIs. In the second stage, based on the extraction of moving parameters, a refined search is performed to realise target detection and false alarm discrimination. To verify the effectiveness of the proposed two-stage method, both a simulation scenario and real radar dataset are applied. The theoretical analysis and experiment results show that the novel method is effective for the detection of dim targets with far less computation than standard RFRFT.

Any sensing task is constrained by the resources that the sensor has at its disposal. For example, a radar system can only allocate a certain amount of transmit energy per unit time to track all necessary target parameters. When multiple dynamic parameters must be tracked over time, a common approach is to schedule updates at regular intervals, subject to all of the tasks assigned to the radar timeline. However, with the advent of much more agile sensors, such as all-digital apertures, it is possible to conceive of increasingly adaptive and dynamic resource allocation strategies that will yield better performance in target-dense, resource-constrained scenarios. In this study, the authors investigate adaptive resource allocation in a canonical scenario by assuming a parallel, linear channel model, and derive various allocation strategies and compare their short- and long-term performance. The authors also investigate effectiveness as a function of varying circumstances, such as varying signal-to-noise ratio and strength of the target dynamics, and relate the results to adaptive allocation strategies for phased arrays and all-digital apertures.

Owing to the exponentially increasing spectrum demand for commercial communication, radar and communication are increasingly encouraged to share spectrum. One approach is to incorporate information bearing communication sequences into radar waveforms. However, the varying radar/communication waveforms in a coherent processing interval lead to a range sidelobe modulation (RSM) that cause a loss of coherence when the Doppler processing operation is applied, thus leading to reduced target visibility. In this study, a new approach of embedding communication into radar waveform is proposed. Based on the scheme of constant envelope orthogonal frequency division multiplexing phase modulation (CE-OFDM-PM) and the orthogonality among trigonometric function, information bearing sequences are implemented using CE-OFDM-PM signal and then phase attached to a radar modulation term with a weighted coefficient. Also, the radar modulation term is synthesised by multiple sinusoidal functions where the coefficients of each sinusoidal modulation function are generated using random variables to achieve a thumbtack-like ambiguity function. The weighted coefficient of a communication modulation term provides a direct control of the degree of RSM. Theoretic analysis and simulation results show that a satisfactory communication bit error rate performance can be achieved without sacrificing the radar performance.

This study gives explicit closed-form expressions of the stochastic Cramer–Rao bounds (STO-CRBs) on direction-of-departure and direction-of-arrival estimation accuracies for collocated multiple-input–multiple-output (MIMO) radar with unknown spatially coloured noise. In some special cases, i.e. the CRB of direction estimation accuracy for monostatic MIMO radar, the white noise scenario is discussed. Theoretical comparisons between the STO-CRBs and the deterministic ones are presented. Finally, these bounds are numerically compared.

The accurate distance and speed estimates of train and individual coaches are necessary for the safe operation of the high-speed train system. Often, the train system does not rely on a single sensor for its distance and speed measurements as the sensors are susceptible to diverse operating conditions such as snow, rain, fog, tunnel, hilly region, slip, slide, etc. Hence, the information from a combination of sensors which can complement each other under certain operating conditions is required for the correct estimation. For distance measurements, Global Navigation Satellite System (GNSS) and balise are generally used. For speed sensing, the combination of wheel sensor, radar and GNSS are chosen. The diversity of sensors in terms of sampling rate and noise characteristics, etc. greatly affect the overall estimation accuracy and reliability if the measurements are used directly. Hence, this work presents a probabilistic weighted fusion algorithm which is based on the nonlinear longitudinal train dynamic model. The fusion algorithm combines the state estimates from distributed and sensor-specific extended Kalman filters. The effectiveness of the proposed fusion algorithm is demonstrated on the simulated sensor measurements along with a wide range of noises, spurious measurements, train operating conditions and track environmental disturbances.

Here, nadir echo suppression (NES) in synthetic aperture radar (SAR) satellites is studied in detail. Moreover, advantages of suppressing the nadir echo in space domain, by beamforming, rather than in time domain, which imposes stringent restrictions on radar timing, are addressed. Performing NES in time domain restricts the swath width, as well as the selection of the pulse repetition frequency (PRF). It is shown that the spatial NES could increase the swath width by ~25%. It also increases the freedom in PRF selection, which is useful in multiple-swath imaging modes and high-resolution wide-swath satellites. Furthermore, it is useful in confining the operating PRF range of SAR satellites, which offers several advantages, such as better thermal stability, more predictability of power consumption, and efficient use of downlink bandwidth. Simulation results show the effectiveness of beamforming-based NES under different situations, namely different off-nadir angles, and land types. Additionally, the conventional formula used for calculation of range ambiguity to signal ratio (RASR) neglects the nadir echo, so it could not be used in the performance assessment. Therefore, the authors derive a generalised formulation for RASR and prove that it converges to the conventional formula at medium-to-high off-nadir angles.