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Skeleton‐based action recognition algorithms have been widely applied to human action recognition. Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non‐Euclidean graphs and achieve significant performance in skeleton‐based action recognition. However, existing GCN‐based models have several issues, such as the topology of the graph is defined based on the natural skeleton of the human body, which is fixed during training, and it may not be applied to different layers of the GCN model and diverse datasets. Besides, the higher‐order information of the joint data, for example, skeleton and dynamic information is not fully utilised. This work proposes a novel multi‐stream adaptive spatial‐temporal attention GCN model that overcomes the aforementioned issues. The method designs a learnable topology graph to adaptively adjust the connection relationship and strength, which is updated with training along with other network parameters. Simultaneously, the adaptive connection parameters are utilised to optimise the connection of the natural skeleton graph and the adaptive topology graph. The spatial‐temporal attention module is embedded in each graph convolution layer to ensure that the network focuses on the more critical joints and frames. A multi‐stream framework is built to integrate multiple inputs, which further improves the performance of the network. The final network achieves state‐of‐the‐art performance on both the NTU‐RGBD and Kinetics‐Skeleton action recognition datasets. The simulation results prove that the proposed method reveals better results than existing methods in all perspectives and that shows the superiority of the proposed method.

The dual-polarized array is considered as one of the key anti-jamming techniques due to twice counts of the jamming signals suppressed relative to a monopolar array. This paper proposes an adaptive anti-jamming dual-polarized ellipsoid minimum variance distortionless response (EMVDR) method to evaluate the carrier-to-noise ratio (Ca/N0) for global positioning system (GPS) coverage. Firstly in GPS L1 band, the dual-polarized gains obtained by controlled reception pattern antenna (CRPA) array are investigated as the signal model of GPS coverage evaluation. Secondly, a novel EMVDR method based the Hermitian tridiagonal loading technique is proposed to obtain the smooth anti-jamming weight matrix, which is against the system error by using the highpass filtering performance of Hermitian tridiagonal matrix. Finally, Ca/N0 based the proposed EMVDR is used to evaluate the coverage percentage of the GPS signal, namely Ca/N0 needs to be beyond a threshold of 28 dB∙Hz by recovering GPS signal. Through many Monte Carlo simulations, the results indicate that the anti-jamming performance of proposed EMVDR method outperforms the classic minimum variance distortionless response (MVDR) based the space adaptive processing (SAP) and the space time adaptive processing (STAP), and the GPS coverage ratio can keep from 60% to 70% until degrees of freedom (DOFs) are exhausted to zero.

Space‐time adaptive processing (STAP) methods can provide good clutter suppression potential in airborne radar systems. However, the performance of these methods is limited by the training samples' support in practical applications. To address this issue, a deep learning framework for STAP is developed. First, the clutter space‐time data and their exact clutter covariance matrices (CCMs) are simultaneously modelled via simulation, in which various non‐ideal factors such as aircraft crabbing, array errors, and internal clutter motion with all possible levels in practice are all considered. Then, a multi‐layer two‐dimensional convolutional neural network (CNN) is developed. In this CNN, low‐resolution angle‐Doppler profiles estimated by a few training samples are used for the input and the high‐resolution counterpart obtained by the exact CCMs are utilized for the labels. Once trained, the CNN can be used to predict the high‐resolution angle‐Doppler profile using a few measured data in near real time. The high‐resolution clutter spectrum can be further calculated using the space‐time steering dictionary and the above obtained profile. Finally, the CCM of the measured data can be constructed and the space‐time weight vector can also be achieved. Compared with recently developed sparsity‐based STAP methods, the performance of the proposed method is better and the computational load of it is far fewer, and therefore more suitable for real‐world implementation. The simulation results have demonstrated the superiority of the proposed method in both clutter suppression performance and computation efficiency.

As training samples are not always target-free in space–time processing for airborne radar, the traditional methods usually use the sample covariance matrix (SCM) as the test covariance matrix (TCM) to censor contaminated training samples. However, the SCM cannot represent the property of the cell under test (CUT) accurately, resulting in low selection efficiency. To deal with this problem, this letter proposes a novel training sample selection method based on covariance matrix whitening. Specifically, we utilize the reconstructed subaperture’s clutter covariance matrix (RSCCM) of the CUT as the TCM. The RSCCM is only determined by the CUT and can characterize the CUT directly. Then, we use the RSCCM to whiten the subaperture’s covariance matrix of the training sample. A criterion for selecting the training samples is derived based on the maximum eigenvalue of the whitened subaperture’s covariance matrix, which is related to the energy of the outliers and more stable than the statistic of the generalized inner product method. Simulations are conducted to evaluate the performance of the proposed method.

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

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.

As training samples are not identically distributed with cell under test (CUT) in heterogeneous environments, the performance of space–time adaptive processing (STAP) to suppress clutter degrades. To improve the performance of STAP, this study proposes a novel training sample selection method for STAP based on multi-frames. The key feature of the new method is to overcome the deficit of independent and identically distributed (IID) training samples in heterogeneous environments. First, multi-frames are selected according to similarity from continuous frames; second, training samples which are IID to the CUT from current frame are selected; finally, combine the training samples of the current frame and same range samples in reference frame into final training sample set. The proposed method is applied to real-radar data, and experimental results demonstrate the effectiveness of the proposed method.

Conventional space-time adaptive processing (STAP) achieves clutter suppression by combining spatial signals and the temporal pulses. However, extreme slow-moving target cannot be effectively detected by space-time joint filtering in ambiguous and non-broadside clutter environment for airborne-phased array radar. By increasing the polarisation information in clutter suppression, improved moving target detection performance can be obtained by the polarisation-space-time adaptive processing (P-STAP). In this study, the performance of P-STAP for airborne-phased array radar is analysed. The polarised signal model of airborne-phased array radar is firstly established and then clutter suppression principle of P-STAP is derived in detailed. Afterwards, the performance of P-STAP is investigated and compared with conventional STAP in ambiguous clutter environment. It is verified that the P-STAP can outperform the conventional STAP for slow moving target detection by three-domain joint processing, especially for ambiguous and non-broadside clutter environment for airborne-phased array radar.

For an airborne-phased array radar system, space-time adaptive processing (STAP) is supposed to be a crucial technique for improving its target detection performance in a strong clutter background. Here, the authors consider the extreme heterogeneous case, i.e. the number of available training samples is limited to one. With only one training sample, the sparse recovery (SR) technique is utilized to estimate the clutter patches in the angular-Doppler plane, i.e. the pair of Doppler frequency and spatial frequency corresponding to the grid in angular-Doppler plane is determined. Based on the observation that some clutter components may be missed for one secondary data sample, the clutter covariance matrix (CCM) is estimated through reconstructing some missed clutter components by utilising the symmetric property of clutter return. From the simulation results, the proposed approach can achieve great performance of clutter suppression with only one training sample compared with conventional SR-based STAP algorithms.

In the heterogeneous interference and target-rich environment, the clutter-plus-noise covariance matrix (CCM) estimate with a good match for the cell under test is significant for space–time adaptive processing. In this paper, an intelligent sample selection method is proposed to estimate the CCM and, thus, to suppress the clutter in the heterogeneous background. First, the proposed method applies the sample amplitude and the spatial relationship between the sample and the antenna beam center to describe the characteristics of samples, which preliminarily selects the crucial homogeneous and heterogeneous samples based on the joint histogram property. Then, the generalized inner product value and the spatial included angle are used to work as two representative features for the training samples as well as the testing samples. Based on the feature vector, the multi-dimensional feature projection based on the kernel function is carried out to achieve the offline training classifier, and the testing samples are classified with intelligence in the multi-dimensional feature space by utilizing the trained support vector machine. Due to the utilization of space–time property in representative feature extraction and intelligent strategy for the decision-making process, the proposed method acquires more reliable and efficient sample selection results compared with most of conventional sample selection methods. The experimental results based on the simulated and measured data show the effectiveness of the proposed method.

In this paper, a deep learning framework for space-time adaptive processing is developed. Firstly, a set of clutter covariance matrixes (CCMs) are modeled based on the prior parameters of radar and navigation system with respect to all possible levels of non-ideal factors, and the columns of each CCM is formulated as undersampled noisy linear measurements of the sparse coefficients corresponding to angle-Doppler spectrum. Then the original spectrum coefficients, obtained by least-square estimation from the modeled CCMs and known space-time steering dictionary, are used as input to train the convolutional neural network (CNN). Meanwhile, the corresponding labels can be obtained by the exact spectrum of modeled CCMs via minimum variance distortionless response algorithm. Once trained, the CNN can be used to predict angle-Doppler spectrum coefficients that corresponds to a new measurement vector in near real time. Simulations results have demonstrated the superiority of the proposed method in both clutter suppression performance and computation efficiency.

Modern RF environments are becoming increasingly congested. This limits the opportunities and capabilities of modern RF systems, obstructing the development and proliferation of new technologies. Novel vehicular RF technologies promise a new era of transportation capabilities, but legacy design techniques cannot adapt to the current spectral congestion. We summarize recent RF Convergence results and discuss how they mitigate spectral congestion in modern vehicular applications. We propose a joint radar, communications, positioning, navigation, and timing (JRCPNT) system architecture as a suitable candidate for future automotive applications. We present relevant performance bounds and initial experimental results to demonstrate the potential performance enhancements of such multiple-function RF systems. We define multiple-channel, multiple-user receiver (MCMUR) techniques that enable this architecture and discuss how these components cooperate to enable these performance enhancements. We summarize initial experimental results to demonstrate the viability of such multiple-function architectures in the context of urban air mobility (UAM) and other automotive applications.

ISAR imaging for a marine moving target faces a series of challenges in the airborne radar system, especially the strong clutter interference with Doppler frequency spreading. To alleviate such problems, this paper proposes an efficient ISAR imaging method based on sliding window STAP. In the method, the whole CPI is divided into a series of coherent processing sub-intervals (CPSIs). Those CPSIs are generated with sliding window technique and they have identical length. In each CPSI, sub-CPI STAP is adopted to suppress the clutter. After that, the target signal is enhanced in terms of signal-to-clutter-plus-noise ratio (SCNR). Meanwhile, the Doppler frequency linear differences with respect to the azimuth dimension is still maintained, which contributes to the further ISAR imaging by Range-Doppler (RD) algorithm after range migration correction. Comparing with existing full-CPI STAP based method, the proposed method improves ISAR imaging performance while requiring less computational complexity. The simulation experiments are carried out to verify the effectiveness of the proposed method.

Ground moving target indicator (GMTI) radar processing attempts to distinguish between radar returns emanating from moving targets and stationary ground clutter. The task is confounded by the relative motion between the radar platform and the scene, as well as by the strength of clutter returns. Techniques such as space-time adaptive processing require an unknown interference covariance describing clutter, jammers, and thermal noise. The covariance is estimated from training data not under test, but, heterogeneous, contaminated, or limited training data degrade the covariance estimate and reduce the detection performance. State-of-the-art techniques for interference covariance estimation reduce the required amount of training data by imposing assumed structure on the covariance matrix. Here, a Bayesian signal model is adopted for jointly estimating targets and clutter in a single cell under test, allowing GMTI processing without training data. The approach incorporates the knowledge of an approximate digital elevation map, platform kinematics (velocity, crab angle, and antenna spacings), and the belief that moving targets are sparse in the scene. Low-complexity computation with the Bayesian model is enabled by recent algorithm developments for fast inference on linear mixing models. Results from the KASSPER I dataset show improved detection performance compared to existing techniques using scores or even hundreds of training bins.

Multiple-input multiple-output (MIMO) radar enjoys the advantage of increased degrees-of-freedom and spatial diversity gain, but it cannot effectively resolves the targets closely spaced in the same angle cell (but different range cells). Frequency diverse array (FDA)-MIMO radar can handle this problem by exploiting its range-dependent beampattern. FDA-MIMO radar was, thus, suggested for range–angle estimation of targets. Nevertheless, it is necessary to provide theoretical performance analysis for such a relatively new radar technique. Since multiple signal classification (MUSIC) algorithm is widely adopted in most of the FDA-MIMO literature, this paper derives the Cramer–Rao lower bound and mean square error expressions in MUSIC-based range–angle estimation algorithms for a general FDA-MIMO radar. Furthermore, the corresponding range and angle resolution thresholds in target detection and localization are also derived. Numerical results verify that the FDA-MIMO indeed outperforms conventional MIMO radar in both range–angle estimation and resolution threshold performance.

In the heterogeneous clutter and target rich environments, the conventional statistical space-time adaptive processing (STAP) methods are confronted with the problem of the lack of valid training samples, which may result in the performance degradation in clutter rejection. Thus, in this paper, a generalized sample weighting method in angle-Doppler domain (GSWADD) is presented and hence an implementation of STAP based on GSWADD. Firstly, the angle-Doppler spectrum of common clutter is estimated by means of the local weighting coefficients, measured according to the distinctions between the clutter and moving targets in the angle-Doppler plane along the range gate, where moving targets are sparse while clutter are dense. Then, the reconstructions of clutter-plus-noise covariance matrix (CCM) without aperture loss and subsequently clutter suppression are carried out. Finally, the results of the experiments based on simulated and real measured data demonstrate that the proposed method can effectively alleviate the STAP performance degradation resulting from the loss of training samples contaminated by the interference targets, compared with the conventional statistical STAP algorithms.

In space-time adaptive processing (STAP), the selected training samples should have the same covariance matrix as the clutter of the cell under test (CUT). The traditional methods usually select samples whose waveforms are similar to that of the CUT. We notice that completely dissimilar waveforms may have the same covariance matrix. As a result, many valid samples are lost in traditional methods. So we propose a training samples selection method based on system identification. The proposed methods select samples with similar covariance matrices instead of similar waveforms. First, a samples selection model based on system identification is proposed. Then, the neural network is used to identify the clutter model of the CUT. Finally, samples are selected according to the output variance. Compared with the methods in [1, 2, 3, 4], the proposed method has the following advantages: (1) More than twice the valid training samples can be obtained; (2) The clutter suppression performance can be improved more than 2 dB for the measured data.

We consider a new hybrid radar paradigm consisting of an active array and a passive array. In this hybrid system, the radar transmits a probing signal from its active array and receives two types of echoes: One is the return from the cooperative active transmission and the other is the target return due to transmission from noncooperative illuminators of opportunity. The motivation for this approach is to exploit not only the active signal but also any passive signals that are present in the surveillance area, thereby maximizing the signal-to-interference-plus-noise ratio (SINR). Numerical results demonstrate that the proposed concept can achieve a significant improvement of the output SINR, compared with conventional methods for active-only or passive-only radar systems.

For a target illuminated by a high-resolution and wide-swath synthetic aperture radar system, the velocity is estimated based on sparse direction-of-arrival estimation. Then, the space-time adaptive processing algorithm is utilized to suppress the clutter and reconstruct the spectra of the target, followed by the traditional target imaging. The performance of the proposed algorithm is analyzed in detail. The proposed algorithm can retain the power of the moving target, and is free of searching.

As driver assistance systems and autonomous driving are on the rise, radar sensors become a common device for automobiles. The high sensor density leads to the occurrence of interference, which decreases the detection capabilities. Here, digital beamforming (DBF) is applied to mitigate such interference. A DBF system requires a calibration of the different receiving channels. It is shown how this calibration completely changes the DBF beam pattern required to cancel interferences, if the system has no IQ receiver. Afterward, the application of DBF on a multiple-input multiple-output (MIMO) radar is investigated. It is shown that only the real aperture and not the virtual one can be used for interference suppression, leading to wide notches in the pattern. However, for any target the large virtual aperture can be exploited, even if interferers are blinded out. Moreover, the wide notches for interference suppression of the real aperture appear narrow in the virtual aperture for target localization. The results are verified by measurements with time-multiplexing MIMO radar.

Adaptive filtering algorithm is one of the most active research topics in signal processing at present. Speech signal processing is the hot issue in the field of digital signal processing. Based on theoretical research of adaptive filter, the work simulated Least mean square error (LMS) adaptive filtering algorithm under different signal-to-noise ratios, spreading to speech signal sequences of different sampling rates. By changing sampling rate factors and filter orders, the work discussed adaptive filtering problems of LMS algorithm under narrowband and broadband interference. Simulation result is consistent with theoretical analysis. With good noise reduction effect, this algorithm is simple and easy to implement.

Many modern radar systems are overcoming the need for high-power transmitters by utilizing low peak-power, high duty-cycle waveforms, making noncooperative detection methods by traditional electronic surveillance a difficult task. This technological difficulty is driving a need for computationally tractable detection and characterization algorithms. Here, a practical method for detecting and fully characterizing an arbitrary number of low-power linear frequency modulated continuous wave (LFMCW) radar signals is achieved by dividing the time-domain signal into contiguous segments and treating each signal segment as a sum of harmonic components corrupted by noise with an unknown, time-varying power spectral density. This method is developed analytically and evaluated experimentally, revealing that the practicality of the method comes at the ex-pense of a loss in estimation accuracy when compared to the Cramer–Rao lower bound. Experimental results indicate that the parameters of two simultaneous LFMCW signals can be estimated to within $10\%$ of their true values with probability greater than $90\%$ when input signal-to-noise ratios are $-$ 10 dB and above with a 25 MHz bandwidth receiver.

In this paper, we introduce a simple; but an effective algorithm to detect and image the precise location of moving targets behind obstacles based on one-transmitter and two-receiver configuration. The problem geometry together with the formulation details of the algorithm are given. The performance of the algorithm is first evaluated by synthesizing a numerical scenario that is simulated by moving ideal scatterers. Then, the success and the validity of the algorithm are being assessed by an error analysis study that calculates the error associated by the approximations. The algorithm is tested with measured data obtained by an experimental set-up for detecting a human movement behind the wall. Resultant two-dimensional consecutive images indicate that this algorithm can be effectively used for pinpointing the moving targets in TWR or similar through-the-obstacle radar applications with good fidelity. (C) 2017 Wiley Periodicals, Inc.

Automotive radars, along with other sensors such as lidar, (which stands for "light detection and ranging"), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter-wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird's-eye view to the existing research community.

Space–time adaptive processing (STAP) for airborne radar employs training snapshots to estimate a clutter covariance matrix (CCM). However, the resulting estimated covariance matrix can be corrupted by a target-like signal (outlier), which leads to a further target self-nulling phenomenon. When these outliers are dense, STAP performance degradation is most severe. To cope with this problem, a novel robust STAP algorithm based on knowledge-aided sparse recovery (SR) is proposed, which can eliminate the influence of dense outliers on target detection. This algorithm exploits the property that the clutter components of side-looking airborne radar are distributed along the clutter ridge, which is used to distinguish clutter components and outliers. Each snapshot is decomposed by the SR method, and then the similar degree function is defined to recognise and select clutter components via a threshold. Subsequently, the CCM is estimated by the select clutter components. Therefore, this algorithm can select appropriate coefficients and space–time steering vectors to assess clutter accurately. Monte Carlo experiments affirm that the proposed algorithm has advantages in robustness and target detection over other conventional STAP algorithms [e.g. samples matrix inverse-STAP, prolate spheroidal wave function-generalised inner product, and SR-STAP algorithms] in heterogeneous clutter environments.

A new form of space-time adaptive processing (STAP) is presented that leverages additional training data obtained from waveform-diverse pulse compression filters possessing low cross-correlation with the primary waveform that is used for traditional airborne and space-based ground moving target indication. In contrast to traditional training data in which clutter and targets are “focused” in range via pulse compression of the primary waveform, this new set of training data possesses a “smeared” range response that better approximates the identically distributed assumption made during sample covariance estimation. The Multi-Waveform STAP (MuW-STAP or simply $\mu $ -STAP) formulation is shown for both multiple-input multiple-output and single-input multiple-output configurations, with the former retaining the spatially-focused primary emission supplemented by low-power secondary emissions that illuminate sidelobe clutter and the latter a special case of the former. In simulation, signal to interference plus noise ratio analysis reveals enhanced robustness to nonstationary interference compared to standard STAP training data.

Adapting the space-time adaptive processing (STAP) filter with finite number of secondary data is of particular interest for airborne phased-array radar clutter suppression. Sparse representation (SR) technique has been introduced into the STAP framework for the benefit of drastically reduced training requirement. However, most SR algorithms need the fine tuning of one or more user parameters, which affect the final results significantly. Sparse Bayesian learning (SBL) and multiple sparse Bayesian learning (M-SBL) are robust and user parameter free approaches, but they converge quite slowly. To remedy this limitation, a fast converging SBL (FCSBL) approach is proposed based on Bayesian inference along with a simple approximation term, then, it is extended to the multiple measurement vector case, and the resulting approach is termed as M-FCSBL. To improve the performance of STAP in finite secondary data situation, the M-FCSBL is utilized to estimate the clutter plus noise covariance matrix (CCM) from a limited number of secondary data, and then the resulting CCM is adopted to devise the STAP filter and suppress the clutter. Numerical experiments with both simulated and Mountain-Top data are carried out. It is shown that the proposed algorithm has superior clutter suppression performance in finite secondary data situation. We study the problem of clutter suppression in STAP with finite training samples.Fast converging sparse Bayesian learning approaches are derived.A novel STAP algorithm named as M-FCSBL-STAP is proposed.The M-FCSBL-STAP has superior performance in low training support situation.

The accuracy of the prior knowledge of the clutter environments is critical to the clutter suppression performance of knowledge-aided space–time adaptive processing (KA-STAP) algorithms in airborne radar applications. In this paper, we propose an enhanced KA-STAP algorithm to estimate the clutter covariance matrix considering inaccurate prior knowledge of the array manifold for airborne radar systems. The core idea of this algorithm is to incorporate prior knowledge about the range of the measured platform velocity and the crab angle, and other radar parameters into the assumed clutter model to obtain increased robustness against inaccuracies of the data. It first over-samples the space–time subspace using prior knowledge about the range values of parameters and the inaccurate array manifold. By selecting the important clutter space–time steering vectors from the over-sampled candidates and computing the corresponding eigenvectors and eigenvalues of the assumed clutter model, we can obtain a more accurate clutter covariance matrix estimate than directly using the prior knowledge of the array manifold. Some extensions of the proposed algorithm with existing techniques are presented and a complexity analysis is conducted. Simulation results illustrate that the proposed algorithms can obtain good clutter suppression performance, even using just one snapshot, and outperform existing KA-STAP algorithms in presence of the errors in the prior knowledge of the array manifold.

Ground Moving Target Indication (GMTI) for Near-space Hypersonic vehicle borne Synthetic Aperture Radar (NH-SAR) is an important part of remote sensing applications. However, the ultra-long range and the ultra-high flight speed of NH-SAR causes the lower echo signal-to-noise ratio (SNR) and the expanded clutter spectrum, which reduces the performance of traditional GMTI methods such as Displaced Phase Centre Antennas (DPCA), along track interferometry (ATI) and Space Time Adaptive Processing (STAP). In this paper, a method based on Track Before Detect (TBD) in image domain is proposed to perform GMTI for NH-SAR. This method obtains the multi-frame images of NH-SAR firstly, and tracks the trajectories of the moving targets in the images. The simulation results validate the effectiveness of the proposed method, and show that this method has lower false alarm probabilities under the low-SNR condition.

Serious security risks and economic losses have increased rapidly under the threat of foreign object debris. Most detection methods employing optical devices are limited in severe weather for low visibility. This study proposes an applicable approach for moving foreign object debris on the basis of space-time adaptive processing. The geometrical model of an airport is introduced whereby the runway and lawn are divided into clutter cells. After calculating the complex amplitude of clutter in each cell, the authors employ the minimum variance power spectrum and degrees of freedom to describe the ground clutter characteristics. Then the space-time filter is designed under the linearly constrained minimum variance criterion to suppress the clutter and detects the moving target. Simulations show that the errors in azimuth and Doppler are both less than −15 dB when SCNR = −20 dB.

Conventional space–time adaptive processing (STAP) methods would suffer severely performance loss in the complex clutter environment of an airborne-phased array radar, especially when the estimated clutter covariance matrix (CCM) is corrupted by the interference targets (outliers). In order to improve the clutter suppression in a practical complex clutter, a robust knowledge-aided sparse recovery STAP method for non-homogeneity clutter suppression is proposed. In the proposed method, the spectral profiles of the clutter and outliers are firstly estimated by sparse recovery processing. Then based on the system prior parameters, the clutter mask is constructed to select the space–time steering vectors corresponding to the clutter components. Afterwards the clutter suppression is achieved based on the clutter subspace obtained from the selected space–time steering vectors. Since the clutter and outlier profiles are effectively estimated and distinguished by the knowledge-aided sparse recovery processing, robust clutter subspace estimation can be achieved for clutter suppression. Through the simulated and actual airborne-phased array radar data, it is verified that the proposed method can effectively improve the STAP performance in a non-homogeneous environment.

Detecting ground moving target in bistatic forward-looking synthetic aperture radar (BFSAR) has become increasingly important in military and civil field. Compared with pulse Doppler radar, the long observation time and high resolution of BFSAR will lead to three problems in the received signal: Doppler ambiguity, large range cell migration and Doppler cell migration, which tremendously aggravate the difficulty of ground moving target detection (GMTD). In this study, the authors propose a three-step ground moving target detection method for BFSAR to solve these problems and achieve good performance on GMTD. First, the bulk-deramp filtering and Keystone transform are exploited to suppress Doppler ambiguity and correct range cell migration, respectively. Then, time-division space–time adaptive processing is utilised for clutter suppression, which eliminates the effect of the Doppler cell migration. Finally, modified Wigner–Ville distribution method is applied to further improve the signal to clutter and noise ratio and the moving target can be detected in the integration domain. Simulations are carried out to demonstrate the effectiveness of the proposed method.

The operational benefits of fully adaptive and Knowledge Aided (KA) processing approaches are discussed for airborne as well as for ground based Radar applications. The advantages of combining different a priori, prior internal and external information from Digital Elevation Models and sources such as Open Street Map are pointed out and demonstrated with real data acquired from Hensoldt’s Air Traffic Control Radar ASR. Real-time Space Time Adaptive Processing (STAP) effects on clutter inhomogeneities are presented on data acquired from Hensoldt’s Air-to-Ground SmartRadar AESA Pod system and how to mitigate them via KA-STAP approaches. The necessities of software defined Radar as provided by Hensoldt’s new ISR Radar family PrecISR™ along with KA resource management are outlined as prerequisites towards cognitive Radar.

The radar system uses space time adaptive processing as a type of signal processing technique. STAP uses antenna array with multiple channels to obtain two dimensional filtering. In ground moving target indicator system(GMTI),the echo is received by the radar from the target, these signals received not only contains the reflected echo from the target but also contains the ground returned signals, the ground returns are called as clutter. The total returned clutter is stronger in amplitude than the returned echo from the target, this poses a challenge for target detection in the presence of clutter and jammer interference, as this clutter signal's energy fills all range bins, clutter filtering is a critical part of ground moving target indicator (GMTI) system. Jamming signal is also a significant interference source to radar. STAP, filter the signal received in angular and Doppler domains to suppress both the clutter and jammer returns. The main goal of STAP is to reduce clutter and null the jammer interference.

There has been significant research into the use of reflected GPS signals for remote sensing. The reflected GPS signals may contain information on soil moisture, ocean waves, and altitude [1]–[4]. Space radar systems have also been a topic of intense interest for several decades, as a means of providing global coverage for both military and geoscience applications [5]–[7]. These systems implement Synthetic Aperture Radar (SAR), Ground Moving Target Indicator (GMTI), and Space Time Adaptive Processing (STAP) algorithms to detect targets and form images. Orbit selection is important for these systems to provide optimum coverage and performance. By using reflected GPS signals to implement space-based bistatic radar, a more robust and cost-effective system could be fielded. However, the performance analysis of such a system would be complex, since the orbits of the receiving spacecraft and the GPS constellation will be significantly different, and their interactions dynamic. This relationship between the receiving satellite's orbit and the GPS constellation was the subject of the research. A prototype antenna array was used to estimate the resolution and signal levels of the system for various transmitter/receiver relationships. Based on an understanding of the desired signal characteristics for bistatic GPS, a family of orbits was simulated to determine the dependency on inclinations and altitudes.

Current state-of-the-art global positioning system (GPS) anti-jam technology has relied on a linear-phased antenna array that consists of N antenna elements and a processing unit that performs a phase-destructive sum of any intentional and unintentional jamming up to $N-1$ signals. If this limitation is exceeded, then the global positioning system (GPS) signal will degrade rapidly and become buried in the noise. Hence, alternative anti-jamming technologies have been sought. This paper describes an anti-jam method that can suppress more than $N-1$ jamming signals with a significant reduction in size, weight, and power (SWaP) and no degradation under adaptive white Gaussian noise (AWGN) and a block Rayleigh fading channels.

Conventional moving target detection methods for airborne radar always need many training range units. To solve this problem, this paper transforms the target detection problem into a multi-classification problem. Firstly, the training dataset is constructed based on a small amount of training range units. Then, a multi-class classifier based on SqueezeNet is constructed. Finally, the trained classifier is used to extract the characteristics of the received space-time data for target detection and parameter estimation. Simulation results show that the SqueezeNet-based airborne radar moving target detection method proposed in this paper can effectively detect the target and estimate its distance, doppler frequency, and other parameters. Compared with the conventional space time adaptive processing method, the proposed method can significantly reduce the number of needed training range units. Compared with the existing target detection method based on classification, the proposed method can effectively improve the accuracy of target detection and parameter estimation.

A method for selecting auxiliary channels in reduced-dimension space-time adaptive processing (STAP) is proposed for airborne multiple-input multiple-output radar. The auxiliary channel selection of the proposed approach is data dependent. Based on maximum cross-correlation energy metric, the significance of each spatial-Doppler channel is evaluated, and the auxiliary channels are selected step-by-step through utilising iteration. For the sake of achieving better performance as much as possible, the proposed approach will select two auxiliary channels at the first step, and select one channel at the next each step. Due to that the explicit physical meaning is very important for a STAP algorithm, the physical meaning of the maximum cross-correlation energy metric is discussed, and the fact that the local optimal output signal-to-interference-noise (SINR) performance can be assured by the maximum cross-correlation energy metric is proved theoretically. The simulations demonstrated that the output SINR loss of the proposed approach is about −1.9 dB when only two auxiliary channels are selected. Consequently, the proposed approach can reduce the requirement of the sample support dramatically. This will be more obvious advantage for the practical application in heterogeneous clutter environments where the number of secondary samples is extremely limited.

Adaptive ground moving target indication (GMTI) algorithms based on the sample matrix inversion require the availability of a secondary data (training data) set to determine the adaptive filter. A polarization-aided GMTI method is devised in this letter for selecting this training data, which could improve the detection performance in heterogeneous environments. In particular, improved classification results are first obtained with the proposed polarization-space 2-D Wishart classifier, which are then employed in a generalized inner product algorithm to select the secondary data. The proposed scheme is able to provide a better choice of secondary data, resulting in considerable improvement in detection performance. Numerical results are provided to show the effectiveness of the proposed method.

In this paper, we consider the problem of space-time adaptive processing (STAP) for airborne radar in the presence of direction-of-arrival (DOA) and Doppler frequency uncertainties, either of which would result in steering vector mismatch. A robust STAP method is devised by introducing an accurate steering vector estimator. In particular, by considering the mismatched DOA and Doppler frequency, a spatial-temporal integral covariance matrix including the actual steering vector component is first constructed. The subspace corresponding to the clutter-plus-noise is then extracted from the so-obtained matrix and used to impose an appropriate constraint to estimate the actual steering vector. The resultant problem is a non-convex quadratically constrained quadratic program (QCQP), which is solved using the semidefinite programming (SDP) relaxation technique. Numerical examples are presented to demonstrate the performance of the proposed approach in different hypothetical scenarios. HighlightsA spatial-temporal integral covariance matrix is constructed by considering the mismatched DOA and Doppler frequency.The subspace corresponding to the clutter-plus-noise is extracted from the spatial-temporal integral covariance matrix.Clutter-plus-noise subspace is used for impose an appropriate constraint to estimate the actual steering vector.

Adaptive methods for signal detection and direction finding are considered in the framework of the maximum likelihood method. The possibility of application of adaptive methods for solution of the problem of coherent passive radar is shown.

To fully appreciate the benefits of arbitrary waveform design capability for transmit adaptive systems, the trade space between constraints (employed to increase the measure of practicality for radar) and the usual performance driver (signal-to-interference-plus-noise ratio) needs to be better defined and understood.We address this issue by developing performance models for radar waveforms with cumulative-modulus and energy constraints. Radar waveforms typically require a constant-modulus (constant-amplitude) transmit signal to efficiently exploit the available transmit power. However, recent hardware advances and the capability for arbitrary (phase and amplitude) designed waveforms have forced a reexamination of this assumption in order to quantify the impact of the nonconstant modulus property.We develop performance models for the signal-to-interference-plus-noise ratio as a function of the cumulative modulus for a random colored interference environment and validate the models against measured data.

Space time adaptive processing (STAP) is an excellent technique for improving ground moving target detection performance of airborne radar. However, covariance matrix estimation required by STAP is commonly corrupted by the presence of target-like signals (i.e. outliers), and thus resulting severe performance degradation. To overcome this problem, a robust non-homogeneity detector based on reweighted adaptive power residue is developed, where an adaptively reweighted scheme is employed to training data set. Therefore, the deleterious effect of outliers on the covariance estimation is eliminated and the robustness of the non-homogeneity detector is guaranteed. Performance analysis using the simulated and measured data validates that the proposed method can effectively remove outliers from the training data and improves the radar detection performance in a dense target environment.

In this paper, the performance of a universal mobile telecommunication system (UMTS)-based passive multistatic radar in a line-of-sight (LoS) environment is studied. The presence of LoS component from the target considerably alters the received signal model, therefore, its characterization is necessary and is the main subject of this work where the transceivers and a target are localized in a three-dimensional Euclidean space. The probability density function (PDF) of the received signal in the presence of LoS is derived and the closed-form expressions of the modified Cramer–Rao lower bounds (MCRLBs) on the Euclidean coordinates of target's position and velocity are found. It is shown that modified Fisher information matrix (MFIM) is a combination of MFIMs due to non-LoS (NLoS) components and LoS component. With the aid of numerical examples, it is verified that by exploiting LoS, the target radar cross section (RCS) increases, which improves the accuracy of target's detection and parameter estimation. In addition, it is also shown that by exploiting LoS component, the performance limits of a waveform can be determined for a generalized radar cross section model (GRCSM), which provides the characterization of a waveform for a broader range of radar applications.

In this paper, we consider the problem of detecting a multichannel signal in the presence of interference. The signal lies in a known subspace but with unknown coordinate. The interference is assumed to be orthogonal to the signal in the whitened space. We propose an effective detector, according to the criterion of the generalized likelihood ratio test (GLRT). The proposed GLRT has better detection performance than the corresponding Rao andWald tests. Remarkably, it is found that the GLRT in the presence of orthogonal interference has the same form as that in the absence of interference. However, the orthogonal interference does affect the detection performance of the detector. We derive closed-form expressions of the probabilities of detection and false alarm for the proposed detector, which are verified by Monte Carlo simulations.

This paper considers the joint design of transmit waveforms and receive filters for airborne multiple-input-multiple-output (MIMO) radar systems. The aim is to maximize the output signal-to-interference-plus-noise ratio (SINR) such that we can achieve enhanced detection performance for a slow-moving target that might be obscured by clutter and jamming. We devise a cyclic algorithm to tackle the non-convex joint design problem. Considering practical implementation issues, we extend the algorithm to deal with constrained designs (i.e., the design of waveforms under a constant-envelope constraint and a similarity constraint, respectively). The convergence of all the devised algorithms is guaranteed. We provide several numerical examples to demonstrate the effectiveness of the proposed algorithms.

Conventional space-time adaptive processing (STAP) is difficult to effectively suppress nonhomogeneous clutter in practical airborne phased array radar systems owing to the requirements of large independent and identically distributed training samples and the high complexity involved in the inversion of a high dimensional matrix. In order to improve the clutter suppression performance with a small training sample supports, a fast STAP method based on projection approximation subspace tracking (PAST) with sparse constraint is proposed in this paper. In the proposed method, based on the low-rank property of the clutter covariance matrix, a sparse constraint is imposed in the cost function of PAST, and the adaptive weight vector is then derived iteratively. Because of the sparse constraint in PAST, the proposed method provides a more robust and stable estimation of the clutter subspace, especially when only a small set of training samples is available. Moreover, the proposed method not only achieves lower computational complexity compared with existing sparsity constrained STAP methods, but also better performance and faster convergence compared with conventional STAP methods without sparse constraint. The effectiveness of the proposed method is verified based on simulated and actual airborne phased array radar data.