Showing papers in "Iet Radar Sonar and Navigation in 2019"

Cognitive radar antenna selection via deep learning

Abstract: Direction-of-arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna. Since larger arrays have high associated cost, area and computational load, there is a recent interest in thinning the antenna arrays without loss of far-field DoA accuracy. In this context, a cognitive radar may deploy a full array and then select an optimal subarray to transmit and receive the signals in response to changes in the target environment. Prior works have used optimisation and greedy search methods to pick the best subarrays cognitively. In this study, deep learning is leveraged to address the antenna selection problem. Specifically, they construct a convolutional neural network (CNN) as a multi-class classification framework, where each class designates a different subarray. The proposed network determines a new array every time data is received by the radar, thereby making antenna selection a cognitive operation. Their numerical experiments show that the proposed CNN structure provides 22% better classification performance than a support vector machine and the resulting subarrays yield 72% more accurate DoA estimates than random array selections.

Band pass filter design against interrupted-sampling repeater jamming based on time-frequency analysis

Abstract: The interrupted-sampling repeater jamming (ISRJ) is coherent with an emitted signal, and significantly limits radar's ability to detect, track and recognise targets. This study focuses on the research of ISRJ suppression for linear frequency modulation radars. A new band pass filter design method based on time frequency (TF) analysis is proposed. A function named ‘max-TF’ is constructed from the TF energy distribution of the de-chirped signal, reflecting the changes of the maximum signal component amplitude with respect to time. Based on the ‘max-TF’ function, jamming-free signal segments are automatically and accurately extracted to generate the filter, which is smoothed subsequently. After filtering, jamming signal peaks in pulse compression results are suppressed while real targets are retained simultaneously. Comparing with the state-of-the-art filtering method, the proposed method has improved jamming suppression ability and extended the feasible scope of signal-to-noise ratio and jamming-to-signal ratio conditions. Simulations have validated the improvements and demonstrated how the parameters affect performance. The average signal to jamming improvement and average radar detection rate of the proposed method is about 7.4 dB and 23% higher than those of the state-of-the-art filtering method, respectively. The direction of further works is inferred.

CRBs for direction-of-departure and direction-of-arrival estimation in collocated MIMO radar in the presence of unknown spatially coloured noise

Abstract: 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.

Augmented subspace MUSIC method for DOA estimation using acoustic vector sensor array

Abstract: In the scenario of ambient noise, the noise powers received by the pressure and velocity components in an underwater acoustic vector sensor (AVS) array are unequal. This paper proves when using the MUSIC method, this inequality causes virtual sources and thus increases the rank of the signal subspace. This fact dramatically degrades the DOA estimation performance of the MUSIC method. Then, an augmented subspace (AS) MUSIC method is proposed to take account of the virtual sources, by augmenting the number of the virtual sources into the signal subspace. Simulation results demonstrate in the case of a high signal-to-noise ratio (SNR), the performance of the AS MUSIC method and the MUSIC method is similar. However, in the case of a low SNR, the AS MUSIC method is superior to the MUSIC method in terms of spatial spectrum, estimation accuracy, and resolution. Experimental results further verify the superiority of the AS MUSIC method over the MUSIC method.

Altitude measurement based on characteristics reversal by deep neural network for VHF radar

Abstract: A novel direction of arrival (DOA) estimation method is proposed for very high-frequency (VHF) radar by the deep neural network (DNN) under strong multipath effect and complex terrain environment The classical methods are all based on the classical multipath signal model, hence, it often causes the problem of model mismatch and results in poor performance in estimation It is generally considered that the serious multipath effect reduces the precision of elevation estimation However, the characteristics of the multipath signal are exploited and used to improve the precision in this study This is the highlight of the proposed method The approach of the deep neural network is applied to learn the received data's characteristics from a different elevation A new characteristic space is constructed in the training procedure In the test procedure, the characteristic of data is extracted by the well-trained network and projected into the constructed characteristic space Reversing the DOA is finished at last The results of simulation data verify the validity of the proposed method The results of the practical data show the practicability of the proposed method for the low-elevation target under the serious multipath effect Combining the DNN approach with altitude measurement of a low-elevation target for VHF radar is meaningful to explore

DOA estimation for coprime EMVS arrays via minimum distance criterion based on PARAFAC analysis

Abstract: This study presents a minimum distance-based direction-of-arrival (DOA) estimation algorithm for coprime electromagnetic vector sensor (EMVS) arrays The idea is to split-up the coprime array into two uniform linear arrays (ULAs) of vector sensors and arrange the received ULA data in the form of a three-way array suitable for parallel factor (PARAFAC) analysis, which fits least-square models to the received source signal mixtures of ULAs and thus enables to retrieve the model matrices corresponding to each ULA Nevertheless, because of the array splitting the estimated DOAs from these matrices are not unique To uniquely determine the DOA, the authors state and prove a theorem which is fundamental to the proposed algorithm and provides a means to find an estimate based on the minimum distance criterion Efficacy of the proposed algorithm is demonstrated through performance comparison with other existing algorithms such as Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), long vector MUltiple SIgnal Classification (MUSIC), conventional PARAFAC and the propagator method being simulated for an equivalent element ULA of EMVS and spaced half a wavelength apart Numerical simulations reveal that the proposed algorithm outperforms the others

In-flight RCS measurements of drones and birds at K-band and W-band

Abstract: This study presents the in-flight radar cross-section (RCS) data of drones and birds at K-band and W-band obtained from extensive experimental trials. The focus of this study is to demonstrate the RCS characteristics of these targets in practical scenarios, hence experimental results are used exclusively. Owing to variations in orientation, aspect angle and target motion, the measured RCS values of these targets fluctuate significantly during their flight. Three very well-calibrated frequency modulated continuous wave radar systems, one operating at the K-band (24 GHz) and two at W-band (94 GHz), have been used to collect data for RCS analysis. Three drones of different sizes (DJI Phantom 3 Standard, DJI Inspire 1 and DJI S900 Hexacopter) and four birds of prey of different sizes (Northern Hawk Owl, Harris Hawk, Indian Eagle Owl and Tawny Eagle) have been used for data collection. The results demonstrate that the RCS scales broadly with the size of the target, consistent with the optical scattering regime and that the RCS values for each target are comparable at the K-band and the W-band. The statistical distribution of RCS for each target falls within a certain range which is useful for predicting the performance of a drone detection radar.

Automatic modulation classification based on joint feature map and convolutional neural network

Abstract: The authors propose an automatic modulation classification algorithm to discriminate the radar emitter signals exploiting deep learning of convolutional neural network (CNN) based on a joint feature map. The joint feature map including time-frequency map and instantaneous autocorrelation (IA) map is generated to achieve superior classification performance on the identification of both phase modulated signals and frequency modulated signals. Afterwards, a CNN is designed to extract the features of the joint feature map. Training and test samples are designed to evaluate the recognition rate of the CNN. Simulation results show that the joint feature map makes up for the weakness of a single feature map for the classification of frequency modulated signals and phase modulated signals simultaneously, and superior classification accuracy is obtained.

Modified cell averaging CFAR detector based on Grubbs criterion in non-homogeneous background

Abstract: Constant false alarm rate (CFAR) is the desired property for automatic target detection in an unknown and non-stationary background Here, a modified cell averaging CFAR (CA-CFAR) detector based on the Grubbs criterion (CAG-CFAR) is proposed for target detection in non-homogeneous background The CFAR property of the CAG-CFAR with respect to the distribution parameter in exponential-distributed background is verified via Monte Carlo simulations The detection performances of the proposed method in scenarios of multiple targets and clutter edges are investigated with different significance levels of the Grubbs criterion and sizes of reference window Results show that the CAG-CFAR detector exhibits a similar detection performance as the CA-CFAR in homogenous environment with an appropriate significance level At clutter edges, the CAG-CFAR detector attains a similar and acceptable false alarm rate control compared to several relevant competitors In the multiple-target scenario, the proposed method achieves a robust detection performance with a low computational burden, whereas the competitors suffer performance degradations in varying degree Simulations and experimental results verify the effectiveness and superiority of the proposed method in multiple-target situation with an unknown number of the interfering targets

Maritime target imaging via simultaneous DVB-T and DVB-S passive ISAR

Abstract: This work presents an analysis of passive inverse synthetic aperture radar images obtained exploiting simultaneously digital video broadcasting-terrestrial (DVB-T) and digital video broadcasting-satellite (DVB-S) as illuminators of opportunity (IOs) over a cooperative maritime target with known motion. The combined exploitation of these two IOs is extremely appealing for passive imaging purposes, given their complementary characteristics. The analysis is first conducted in a simulated environment, to show the different expected outcomes from the two considered bistatic geometries and operating bandwidths. Subsequently, the same analysis is repeated over real data acquired during a field trial, by exploiting experimental setups developed at Fraunhofer FHR. In particular, DVB-T and DVB-S data are focused by means of back projection, which enables an easier comparison of the different ISAR products. Real data results show good match with simulated ones. Target size can be estimated with good accuracy in both DVB-T and DVB-S cases, and dominant scatterers can also be identified. DVB-S also enables target-shape recognition, given its higher signal bandwidth.

BP algorithm for the multireceiver SAS

Abstract: The imaging performance and efficiency are two important issues for the multireceiver synthetic aperture sonar (SAS). Back projection (BP) algorithm is characterised by the high performance and low efficiency. In this study, the authors first recall the standard BP algorithm based on the interpolation for the multireceiver SAS. Then, two improved BP algorithms based on the range Fourier transformation (FT) are presented. Considering the fact that the time delay in the time domain can be carried out by the phase shifting in the frequency domain, an FT shifting based BP algorithm avoiding the interpolation error is presented. Although this method produces the focusing result with high performance, it is very time consuming. In order to improve the imaging efficiency without loss of imaging performance, the authors propose an oversampling-based BP algorithm, which is based on the fact that the zero-padding in the frequency domain is equivalent to the interpolation in the time domain. After that, the computation complexity of three BP algorithms is analysed in detail. Finally, simulations and real data are exploited to validate the presented methods.

Hierarchical coastline detection in SAR images based on spectral-textural features and global–local information

Abstract: This study presents a novel approach to detect the coastline from single-polarisation synthetic aperture radar (SAR) images. The proposed method encompasses land/sea segmentation, coastline detection, and refinement. A novel spectral–textural segmentation framework (STSF) is proposed by using the spectral–textural features extracted from the input image patches. The STSF distinguishes various coastal/sea types and is robust to noise. Also, a hierarchical region-based level set method (LSM) is proposed to detect the coastline, accurately. The first LSM step applies global information for evolution. The LSM initialisation is performed using the obtained rough segmentation, which is very practical as the final LSM evolution depends on the initial value, particularly on complex SAR images. The global region-based LSM (GRB-LSM) step modifies the previous segmentation and approaches to the coastline. To improve accuracy, a local region-based LSM (LRB-LSM) is proposed. Therefore, in the second LSM step, the LRB-LSM applies to the results of GRB-LSM step. The LRB-LSM improves the accuracy of the detected coastline while ensuring its smoothness. To verify the performance of the proposed method, several high-resolution SAR images from different microwave bands and various coastal environments are used. The performance of the proposed method is confirmed by the given experiments.

GNSS multipath estimation and mitigation based on particle filter

Abstract: Multipath interference is the main error source for high-precision positioning applications, and multipath suppression and mitigation is particularly important. Particle filters are widely used to solve non-linear filtering problems without limitation of Gaussian distribution, and Global navigation satellite system (GNSS) multipath estimation and mitigation based on particle filter are proposed in this study. This approach has four innovations: Firstly, the Kalman-based multipath signal model is improved to obtain a particle filter multipath signal model, and I and Q two channel signals in the algorithm model solve the coherence correlation phase estimation problem. Secondly, the particle filter algorithm constructs the correlation function using the current particle amplitude and delay. Thirdly, taking full advantage of every particle information, the weighted average is used to calculate state quantity of multipath. Fourthly, three resampling algorithms, including simple random, pseudo-parallel genetic algorithm and niche genetic algorithm, are taken to resample. Particle filter algorithm for multipath estimation and mitigation based on simulated data and actual navigation satellite signal data is verified. The simulation results show the proposed algorithm has high accuracy in multipath estimation than MEDLL and TK-MEDLL, especially in multichannel multipath estimation. The actual experimental results show particle filter algorithm is effective for improving the positioning accuracy in complex environments.

Drone detection and radar-cross-section measurements by RAD-DAR

Abstract: 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.

Train distance and speed estimation using multi sensor data fusion

Abstract: 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.

Mainlobe jamming suppression for distributed radar via joint blind source separation

Abstract: 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.

Adaptive JSPA in distributed colocated MIMO radar network for multiple targets tracking

Abstract: In this study, the authors present a distributed resource-awareness colocated multiple-input-multiple-output radar network for multiple targets tracking. Specifically, taking posterior Cramer-Rao lower bound as the performance metric, they propose a joint scheduling and power allocation (JSPA) scheme as a sparse optimisation problem. The sparsity of the solution enables the JSPA scheme to jointly optimise the schedule and corresponding transmit power of radars by the non-zero entries with associated amplitudes. The JSPA scheme has the robustness of feasibility even if there is not enough resource to achieve the prescribed performance requirements of targets. In this case, the JSPA scheme will automatically relax the performance bounds for individual targets at the lowest price and decide the number of targets to be tracked according to the priorities. They also propose a global optimal fusion rule that builds the connection between resource allocation and distributed tracking. Simulation results demonstrate the effectiveness of the proposed methods. The JSPA scheme can dynamically adjust the resource utilisation of the tracking of individual targets according to the accuracy demands. The performance of important targets is favoured to be satisfied, whereas a target with low priority may be dropped by the JSPA scheme if necessary.

Robust target motion analysis using the possibility particle filter

Abstract: Bearings-only target motion analysis (TMA) is the process of estimating the state of a moving emitting target from noisy measurements collected by a single passive observer. The focus of this study is on recursive TMA, traditionally solved using the Bayesian filters (e.g. extended or unscented Kalman filters, particle filters). The TMA is a difficult problem and may result in track divergence, especially when the assumed probabilistic models are imperfect or mismatched. As a robust alternative to Bayesian filters for TMA, the authors present a recently proposed stochastic filter referred to as the possibility filter. The filter is implemented in the sequential Monte Carlo framework, and named the possibility particle filter. This study demonstrates its superior performance against the standard (Bayesian) particle filter in the presence of a model mismatch, while in the case of the exact model match, its performance equals that of the standard particle filter.

Joint polarisation and frequency diversity for deceptive jamming suppression in MIMO radar

Abstract: The joint exploitation of polarisation and frequency diversity is considered here as a way to improve the deceptive jamming suppression capability in multiple-input multiple-output (MIMO) radar. The authors explore a special polarisation and frequency diverse MIMO (PFD-MIMO) radar system here. In the PFD-MIMO radar system, polarisation and frequency diversity are employed in the transmit array, and two-dimensional vector sensors are adopted in the receive array to measure the horizontal and vertical components of the received signals. To fully explore the potential of the PFD-MIMO radar system, the transmitting polarisation and frequency increment are both optimised by maximising the output signal-to-interference-plus-noise ratio. The simulation results demonstrate that the PFD-MIMO radar is more robust and exhibits a better performance for deceptive jamming suppression than the frequency diverse array MIMO radar and polarimetric MIMO radar. Moreover, improved deceptive jamming suppression performance is achieved when the transmitting polarisation and frequency increment are both optimised.

Research on a chip scale atomic clock aided vector tracking loop

Abstract: With the development of Micro-Electro-Mechanical-System (MEMS) technology, size and accuracy of Chip Scale Atomic Clock (CSAC) gradually improve, and a commercial CSAC products available, which makes it possible to integrate a CSAC to a handheld GNSS receiver. Published results have demonstrated that CSAC could better the Scalar Tracking Loops (STL) navigation results. However, the Vector Tracking Loop (VTL) has a different architecture compared with STL, which allows it meaningful to investigate CSAC driven VTL. In this study, two problems are discussed in a classic VTL employing CSAC as frequency reference: (i) how much improvement in navigation solutions initialed by substituting TXCO with CASC; (ii) whether the CSAC driven VTL can work excluding clock bias or clock drift as state variables, in other words, whether the VTL can provide navigation solutions at a moderate accuracy with only three satellites in view. Two comparisons have been done in this investigation and demonstrated that CSAC could improve the VTL navigation solutions, and the clock drift variable could be removed from the VTL model with no influence on the VTL performance.

Accelerated translational motion compensation with contrast maximisation optimisation algorithm for inverse synthetic aperture radar imaging

Abstract: Range alignment of traditional translational motion compensation for inverse synthetic aperture radar imaging generally cannot be implemented accurately under low signal-to-noise ratio, resulting in the following phase adjustment invalid. In this study, a novel accelerated translational motion compensation with contrast maximisation optimisation algorithm is proposed. Translational motion is first modelled as a parametric finite order polynomial. The translational motion property can be compactly expressed by a polynomial coefficient vector. Meanwhile, the image contrast is utilised to estimate the polynomial coefficient vector based on the maximum contrast optimisation, implemented by Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. BFGS is an effective quasi-Newton algorithm, yielding fast convergence and small computational complexity. Moreover, a method called pseudo Akaike information criterion is also proposed to determine the polynomial order adaptively. Both simulated and real data experiments are provided for a clear demonstration of the proposed algorithm.

Waveform design for a dual-function radar-communication system based on CE-OFDM-PM signal

Abstract: 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.

Deep CNNs as a method to classify rotating objects based on monostatic RCS

Abstract: 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.

Experimental study on low-THz automotive radar signal attenuation during snowfall

Abstract: 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.

Research on a chip scale atomic clock driven GNSS/SINS deeply coupled navigation system for augmented performance

Abstract: Chip scale atomic clock (CSAC) is a recently developed low-profile atomic frequency reference device. Researchers have demonstrated CSAC was able to improve the accuracy of Global navigation satellite system (GNSS) receiver positioning results. However, the work was conducted using scalar tracking loops (STL). It is meaningful to investigate CSAC augmented vector tracking loop (VTL), since the VTL has different architecture with STL. Thus, in this paper, the authors investigated a CSAC driven VTL_DI system for exploring CASC impact on VTL. Firstly, the authors explored 1) whether VTL_DI could operate without regarding clock offset and drifts as VTL_DI integration filter state variables, and 2) whether VTL_DI could provide reliable position with only three satellites in view. Secondly, side-by-side comparative field tests were conducted to evaluate performance improvement: by solution A: VTL_DI supported by CSAC and solution B: a temperature compensated crystal oscillator (TXCO) with more than four satellites and three satellites in view scenarios. It was discovered that the CSAC could enhance the position performance, even if amount of satellites were fewer than four in view. However, VTL_DI system could not guarantee its positioning accuracy without clock offset as state variable, which was different from STL_DI system.

HRR profile estimation using SLIM

Abstract: In this study, authors address high-range-resolution (HRR) profile reconstruction, when stepped-frequency waveforms are eventually used to maintain a narrow instantaneous bandwidth, resorting to the sparse learning via iterative minimisation (SLIM) paradigm, a regularised minimisation approach with an -norm constraint (for ), providing a variant to the original method. Particularly, the proposed method resorts to the regularised maximum-likelihood estimation paradigm including a term promoting the sparsity of the profile and related to the -norm of the vector containing the scatterers’ reflectivities. A priori information on the interference power level is also accounted for, at the design stage, and, assuming that each range cell under test contains at most one scatterer, the actual active scatterers composing the target are determined by exploiting the Bayesian information criterion (BIC). BIC is also used to automatically select the optimised q , so as to make the procedure adaptive with respect to q . Once the location of the active scatterers has been determined, a least-squares approach is also used to obtain even more precise HRR reconstruction. Furthermore, an efficient algorithm to define optimised frequency hopping patterns, in the presence of a continuous and coordinated feedback between the transmitter and receiver, is presented and assessed. The carried out analysis shows that the SLIM-based procedure presents higher accuracy in the HRR profile recovery than other widely used techniques, i.e. the iterative adaptive approach (IAA). Moreover, results demonstrate that the target range profile estimation capabilities are enhanced, both for SLIM and IAA, when the cognitive paradigm is employed.

Passive SAR imaging using DVB-T illumination for airborne applications

Abstract: This study presents recent research results obtained by the Warsaw University of Technology, Poland in airborne passive synthetic aperture radar (SAR) imaging using digital video broadcasting-terrestrial illumination. The main goal of the research was to prove the possibility of passive SAR imaging and its usage for airborne applications. In the study, different signal processing techniques used for passive SAR image creation are discussed in detail, together with an analysis of their computational requirements and implementation possibilities on the computing platforms available on the market. Also, a real measurement campaign is presented showing passive SAR images obtained. Finally, challenges in airborne passive SAR image creation are discussed, showing potential directions of the future development in this field.

Reduced complexity and near optimum detector for linear-frequency-modulated and phase-modulated LPI radar signals

Abstract: Dealing with low probability of intercept (LPI) radar signals in very low signal-to-noise ratio (SNR) stages requires reconnaissance systems to employ an effectively practical method for detecting and fully characterising the received signals. Following the aim of LPI signal detection, in this study, a novel method based on concentrating the energy of the signal in one particular row of the time–frequency domain matrix is proposed. It is shown that the proposed method enables to detect and estimate the parameters of multiple low-power linear-frequency-modulated signals. A comparison has been made between the performance of the matched filter and the proposed method, and it is shown the proposed method has approximately the same performance and low computational complexity as matched filters. This method is also able to detect the poly-phase signals such as Frank-coded signals and also non-linear-frequency-modulated signals, as well. To show the effectiveness of the method, extensive simulations are carried out with different LPI radar waveforms corrupted with additive white Gaussian noise of SNR down to −25 dB.

Sidelobe level reduction in ACF of NLFM waveform

Abstract: In this study, an iterative method is proposed for non-linear frequency modulation (NLFM) waveform design based on a constrained optimisation problem using Lagrangian method. To date, NLFM waveform design methods have been performed based on the stationary phase concept which has been already used by the authors in a previous work. The proposed method has been implemented for six windows of Raised-Cosine, Taylor, Chebyshev, Gaussian, Poisson, and Kaiser. The results reveal that the peak sidelobe level of autocorrelation function (ACF) reduces about an average of 5 dB in the proposed method compared with the stationary phase method, and an optimum peak sidelobe level is achieved. The minimum error of the proposed method decreases in each iteration which is demonstrated using mathematical relations and simulation. The trend decrement of minimum error guarantees convergence of the proposed method.

Joint range–velocity deception jamming suppression for SIMO radar

Abstract: This study deals with the problem of range-velocity joint deception jamming suppression for a single-input multiple-output (SIMO) radar system. The Doppler diversity and spatial geometric correlation are exploited to distinguish the jamming and multiple moving targets. First, the echoes in each receiver are mapped into range-Doppler plane by pulse compression and slow-time Fourier transform operations. Then, in each receiver, the range and Doppler frequency of the targets are extracted. Third, based on differences between the physical target echoes and the jamming signals in both range and Doppler dimensions, two identification algorithms are developed to discriminate the false targets in fusion centre. Finally, through numerical simulations, the performance of the proposed suppression scheme is evaluated, showing its capability to suppress the range-velocity joint deception jamming and locate the multiple physical targets with high precision.