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The paper explores the potential to enhance aviation safety, particularly in militarized regions, by outfitting aircraft with Side Looking Airborne Radar (SLAR) and employing space-time adaptive processing (STAP) algorithms. The research objective revolves around implementing a model of side-looking airborne radar and the corresponding STAP algorithms. This technology enables the detection of slow-moving targets amidst strong interference, encompassing both passive (clutter) and active (jammer) elements. Slow-moving targets relative to the aircraft's speed include tanks, combat vehicles, command vehicles, artillery, and logistical assets of enemy forces. The theoretical framework of space-time adaptive processing is presented, elucidating the sequential steps of the classical Sample Matrix Inversion Space-Time Adaptive Processing (SMI STAP) algorithm. The paper underscores the significance of characteristic parameters delineating a linear STAP processor. The proposed solution facilitates the detection of enemy combat measures and enhances aviation safety. It outlines a radar model installed beneath the aircraft's fuselage and elucidates algorithms for space-time adaptive processing of radar signals. The simulations conducted within the article were executed using the MATLAB environment. The simulation results indeed suggest that the proposed solution holds promise for deployment in equipping aircraft of one's own military and those engaged in operations within conflict zones. This paper stands as one of the few contributions in the literature addressing the augmentation of aircraft safety through radar and space-time adaptive processing. [ABSTRACT FROM AUTHOR]

For high‐resolution radar, traditional direction of arrival (DOA) angle measuring algorithms for point‐like targets cannot be directly applied to extended targets. Instead, pixel‐based detection and clustering must be performed first, followed by angle estimation. However, the presence of factors such as clutter and system noise makes it impossible to guarantee a one‐to‐one correspondence between the multiple scattering points of an extended target and the real target, which may result in significant discrepancies between the angle estimation and localization results and the real target. To address this issue, this paper proposes and discusses a radar extended target arrival angle estimation method based on effective phase clustering. The proposed method selects effective target scatterers through the interferometric phase information between channels, clusters these points, and then estimates the DOA angle of the extended target, effectively reducing the impact of interference scatterers on DOA angle estimation and improving the angular measurement accuracy. The effectiveness of the proposed method was validated by the measured data obtained from an airborne digital beamforming radar system. [ABSTRACT FROM AUTHOR]

In this article, a new statistical method for estimating the clutter covariance matrix in space–time adaptive radar signal processing (STAP) is presented and studied. The new method was designed for multiple-input–multiple-output (MIMO) radar with time division multiplexing (TDM). An extensive analysis of statistical and non-statistical methods for estimating the clutter covariance matrix in STAP is presented in this paper. In addition, the STAP algorithm for the standard statistical SMI clutter covariance matrix estimation method, which is based on QR distribution, has been presented. The new method is based on LU distribution with partial pivoting. Simulation results confirm the validity of the presented model and theoretical assumptions. In addition, more accurate object detection results were demonstrated for specific computational examples than for other statistical methods. Considering the current analysis of the literature, it is noted that attention has now been focused worldwide on the study of non-statistical methods for estimating clutter covariance matrices in heterogeneous environments. Hence, it should be emphasized that the posted study fills a gap in current research on STAP. [ABSTRACT FROM AUTHOR]

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

Parameter estimation for space‐time adaptive processing (STAP) usually encounters conflicts between estimation accuracy and algorithmic computation complexity in practical applications. In addition, due to the complication of mobile characteristics in STAP, it is often necessary to detect many prominent surface targets. Herein, we propose a sequential approach based on coprime sampling structures for off‐grid angle‐Doppler estimation in the framework of STAP, where both the accuracy and efficiency are well balanced. The estimation is accomplished via three sequential stages, where the initialized relative spatial–temporal map and closed‐form solutions to spatial and temporal parameters based on the rotational invariant technique are subsequently formulated, eventually paired by a Euclidean distance minimization‐based principle. Benefiting from such sequential implementation, the computational complexity can be greatly reduced, and the basis mismatch problem can be avoided in the meantime. Besides, coprime sampling structures enable expanded degrees of freedom in the case of fixed physical cost, adapting to more complicated actual scenarios. Simulation results demonstrate the effectiveness of the proposed algorithm from the aspects of both accuracy and efficiency along with the advance of DOFs. [ABSTRACT FROM AUTHOR]

Space-time adaptive detection (STAD) is an important research aspect of the bistatic multiple-input multiple-output (MIMO) radar system. The signal and clutter space-time models of the bistatic MIMO radar are described in this study. The knowledge-aided (KA) transform, which utilises the structural properties of the target-free data and the sample covariance matrix, is applied to increase the independent secondary data fourfold in the STAD frame. The performance assessment of the KA–STAD detectors with generalised likelihood ratio test (GLRT), adaptive matched filter (AMF), and Rao rules is presented. The simulation experiments show that the detection and constant false alarm rate performances of the presented KA–GLRT, KA–AMF, and KA–Rao detectors outperform the GLRT, AMF, and Rao detectors for the bistatic MIMO radar system STAD frame in clutter background with limited secondary data. [ABSTRACT FROM AUTHOR]

Clutter presents considerable heterogeneity in forward-looking airborne radar (FLAR) applications and conventional space-time adaptive processing (STAP) methods are sensitive to model mismatch. As a result, when a strong target signal contaminates the training samples, despite the use of guard cells, the performance of conventional STAP methods degrades significantly. In this study, a robust method, which involves reconstructing a target-free covariance matrix and correcting the presumed steering vector to prevent target cancellation in FLAR, is proposed. First, the target-free covariance matrix is reconstructed through integrating the spatial–temporal spectrum over a sector separated from the desired frequency and direction of targets. Subsequently, the mismatch between presumed steering vector and actual steering vector is corrected via quadratic optimisation. In addition, the processing scheme is applied to real-measured clutter data, and the experimental results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A novel bi-iterative dimension-reduced space–time adaptive processing (STAP) algorithm for clutter suppression and moving target detection in airborne radar system is proposed. A dimension-reduced processing in both space and time is firstly performed. Then a bi-quadratic cost function is constructed to estimate the dimension-reduced spatiotemporal filter coefficients, which are used for suppressing the clutter and keeping the target signal. An optimal solution to the cost function can be obtained efficiently by a bi-iteration algorithm. Theoretical analysis and computer simulation results illustrate that the algorithm has fast convergence, low computation load and sampling requirements. The results of experiment by using the measured data show the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study presents a low-altitude wind shear speed estimation method based on combined space–time main channel adaptive processing. The method first reduces the dimension of the radar echo data of the measured range unit by constructing a dimension-reduced joint space–time transform matrix, and then constructs the optimal weight vector of the dimension-reducing processor to adaptively filter the reduced-dimensional data. Furthermore, accurate estimation of the speed of the wind field is achieved in the presence of the amplitude–phase error. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, the authors consider the problem of direction of arrival (DOA) estimation in compressive sensing multiple-input and multiple-output (CS-MIMO) radars with co-prime receive arrays. A sparse aperture completion scheme is proposed to fill the 'holes' that in the difference co-array, achieving the full virtual receive aperture. Structured measurement matrices are devised in order that the output data of match filters can be seen as space–time compressed signal of a virtual uniform linear array. By employing the space–time recovery scheme, the sparse target scene can be accurately recovered while the amount of samples is further reduced. Numerical results demonstrate that the proposed scheme can achieve accurate DOA estimation with space–time compressed data and outperform the CS-based difference co-array methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Compared with ground radars, space-borne bistatic radar shows great advantages in higher signal-to-noise ratio (SNR) and robuster security. Firstly, this study establishes space-borne geosynchronous satellite-low orbiting satellite (GEO-LEO) space–time two-dimensional model considering the influence of the rotation of the earth together with the curvature of the earth surface. Then the Doppler trajectory distance dependence of the clutter angle is analysed. Finally, the characteristics of the space-borne GEO-LEO model are proved to be beneficial to clutter suppression and ground-moving targets indication. Simulation results show that the space-borne GEO-LEO model has good performance. [ABSTRACT FROM AUTHOR]

The fast Fourier transform FFT processor is an important part of the space real-time signal processing system based on field programmable gate array (FPGA). Since occupying a large amount of logical resources and storage resources, FFT processor is more vulnerable to high-energy particles in space, resulting in single event upset (SEU). This paper presents a novel FPGA scrubbing framework base on dynamic partial reconfiguration technique for a FFT processor to mitigate SEU. The proposed scheme is compared with the blind scrubbing, the reconfiguration time is reduced by 78%. Then, the resource utilisation is 61.5% less than triple modular redundancy scheme. This paper also presents a DPR controller for FFT processor, which is evaluated in terms of hardware resources and reconfiguration time. A comparison to the Xilinx PRC IP shows that multipath delay feedback FFT controller saves 38.6% resources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) for airborne radar with co-prime arrays is shown to have excellent superiority compared to traditional STAP with uniform linear array radar. However, high arithmetic computational complexity and large amount of training data are required in this approach. This motivates the authors to present a new approach which is relatively low computational load and fast convergence with satisfactory performance. Specifically, a reduced-dimension transformation in Doppler domain is incorporated into the existing approach. The reduced-dimension interference covariance matrix and target steering vector are then achieved by performing the reduced-dimension process, and hence two reduced-dimension STAP filters are designed using the derived interference covariance matrix and target-steering vector. Numerical simulations are carried out to reveal the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

The issue of robust orthogonal frequency division multiplexing (OFDM) multi-input multi-output (MIMO) radar waveform optimization is considered here with clutter uncertainty to enhance the worst-case detection probability of the MIMO-OFDM radar-based space-time adaptive processing (STAP). Robust optimization is necessary on account of the fact that waveform design is generally sensitive to the initial parameter estimation errors. According to the max-min approach, the issue of robust waveform design is constructed with the criterion of maximizing the worst-case output SINR, namely signal-to-interference-noise ratio, under the constraints of the clutter covariance matrix (CCM) errors and constant modulus to ease this sensitivity systematically, and hence the robustness of STAP's detection to the uncertainty in the CCM estimate can be enhanced. To handle the acquired nonlinear and sophisticated issue, an iterative approach based on diagonal loading (DL) is presented, in which the inner and outer optimization issues can be recast into semidefinite programming ones by employing DL method, and thus both of them can be tackled in an efficient way. As compared to uncorrelated waveforms and the non-robust algorithm, numerical simulations reveal that the developed approach can enhance the robustness of STAP's detection to the uncertainty in the CCM considerably. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is an efficient solution for road traffic monitoring due to its high spatial resolution and independence from daylight and weather conditions. In this sense, a number of ground moving target indication (GMTI) algorithms have been developed, whereas their robustness is often achieved with high costs, increased hardware complexity and high computational burden. This paper presents a fast GMTI processor that blends the powerful post-Doppler space-time adaptive processing (PD STAP) with an a priori known road map and digital elevation model (DEM). The algorithm presents great potential for real-time processing, decreased hardware complexity and low costs compared to state-of-the-art systems. It is tested using real 4-channel X-band radar data acquired with the DLR's airborne F-SAR. [ABSTRACT FROM AUTHOR]

Adaptive signal detection algorithms in unknown interference are generally formulated under the assumption that training sets are available for the estimation of the test cell interference characteristics. A significant problem occurs in applications such as radar surveillance when the interference covariance matrices of vectors from the training cells and that of the test cell vector are mismatched. False alarm rates may increase beyond acceptable levels and thus overwhelm a receiver if the interference in the test cell is not adequately cancelled. A number of adaptive detection algorithms with the constant false alarm rate (CFAR) property have been designed assuming the availability of training data that have the same covariance matrix as the interference in the test cell (although the adaptive coherence estimator algorithm requires the two covariance matrices to be related by a positive multiplicative constant that is not required to be known). The detection threshold in these CFAR detectors can be selected without knowledge of the interference-plus-noise covariance matrix. To the best of our knowledge, an analytical characterization of the performance effects of a receiver using a mismatched training set (perhaps inadvertently) in adaptive detection is currently not available. This paper addresses the problem analytically. An exact analysis of the effects of interference covariance matrix mismatch on the performance of the adaptive matched filter test is carried out. Results provide insights to the specific aspects of covariance matrix mismatch that cause the increase in false alarms when compared to the matched training set case. Analytical results are illustrated with an example and verified independently with simulations. These results are useful in developing CFAR algorithms under conditions of mismatched training. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) for airborne radar employs training samples to estimate clutter covariance matrix (CCM). However, the target-like signals contained in the training samples severely corrupt the accuracy of the CCM. This paper proposes a novel non-homogeneous STAP algorithm for target-like signal elimination based on reduced-dimension sparse reconstruction (RDSR) to overcome this issue. The proposed algorithm exploits the high-resolution angle-Doppler spectrum obtained by RDSR to estimate and eliminate target-like signals. Theoretical analysis and simulation results show that the proposed algorithm effectively suppresses clutter and improves the performance of STAP in non-homogeneous environments. [ABSTRACT FROM AUTHOR]

In this paper, performance analysis is conducted to demonstrate that the combination of employing ultrawideband (UWB) throb signals and array beamforming can enhance the resolution performance as well as the robustness of imaging radar systems, i.e., airborne and spaceborne synthetic aperture radar. A mathematical model for the response of an array beamforming system, with systematic faults and channel instability, to received UWB-throb signals is derived. The effect of parametric random errors associated with this array beamforming system on the processing gain is analyzed for the UWB-throb signal and the linear FM chirp signal. Space–time resolution function, an essential tool for waveform design, is derived for the UWB-throb signal corrupted by parametric random errors. By using the Monte Carlo computer simulation technique, plots are generated for the space–time resolution function, temporal profile, array factor, and energy beampattern, for different values of the signals’ design parameters and the statistical parameters of the random errors. The plots reveal the degradations in the beamforming performance due to the presence of parametric random errors, i.e., drop in the central peak amplitude, rise of sidelobe level, generation of spurious sidelobes, and beam-shape loss affecting beamwidth and beam-pointing accuracy. In the presence of parametric random errors, the robustness of array beamforming using the UWB-throb signal is superior to that achievable by array beamforming using the linear-FM chirp signal. The resolution angle obtained from the energy pattern of the UWB-throb signal provides a tradeoff between signal design parameters and array size for improving the angular-resolution capability. Such a tradeoff is desirable in practice to achieve a balance between system complexity and cost effectiveness. [ABSTRACT FROM AUTHOR]

Intentional or unintentional interference experienced by the Global Positioning System (GPS) is a significant concern for GPS-based critical infrastructures and services including aviation. An effective way to mitigate GPS interference is to utilize a GPS antenna array capable of electronically changing its gain pattern. Although a conventional GPS antenna array consists of single-polarized antenna elements, a dual-polarized antenna array has the potential to mitigate approximately twice the number of interference signals in the spatial domain as a single-polarized array because of the additional degrees of freedom provided. This paper proposes an adaptive beamforming algorithm using a dual-polarized GPS antenna array for mitigation of interference signals with various polarizations. In this paper, a dual-polarized antenna element specifically refers to two co-located crossed linearly polarized dipole antennas. The proposed minimum-variance-distortionless-response (MVDR)-based space–time adaptive processing (STAP) method utilizes a novel constraint vector that is specially designed for a dual-polarized GPS array. As the proposed constraint vector considers realistic radiation patterns of the antenna, the performance of the proposed method in terms of the signal-to-interference-plus-noise power ratio (SINR) is noticeably superior to that of previous methods under a representative interference scenario. The performance of the proposed method is compared with three previous methods utilizing dual-polarized antenna arrays. [ABSTRACT FROM AUTHOR]

We develop in this paper a new adaptive low-rank (LR) filter for MIMO-space time adaptive processing (STAP) application based on a tensorial modeling of the data. This filter is based on an extension of the higher order singular value decomposition (HOSVD) (which is also one possible extension of singular value decomposition to the tensor case), called alternative unfolding HOSVD (AU-HOSVD), which allows us to consider the combinations of dimensions. This property is necessary to keep the advantages of the STAP and the MIMO characteristics of the data. We show that the choice of a good partition (as well as the tensorial modeling) is not heuristic but have to follow several features. Thanks to the derivation of the theoretical formulation of multimode ranks for all partitions, the tensorial LR filters are easy to compute. Results on simulated data show the good performance of the AU-HOSVD LR filters in terms of secondary data and clutter notch. [ABSTRACT FROM AUTHOR]

As passive radar becomes more popular, modeling its behavior and physics becomes more critical. Particular to space-time adaptive processing (STAP) for ground moving target indication, the way in which clutter is modeled will directly impact performance expectations and results. Furthermore, modeling Doppler modulation from uncooperative waveforms can also impact performance assessments. This paper introduces a three-dimensional model that incorporates a novel method for modeling clutter and waveform impacts in passive STAP. [ABSTRACT FROM AUTHOR]

In a recent paper, the problem of inverse synthetic aperture radar (ISAR) imaging moving ground targets was discussed and an effective solution based on a novel joint space-time adaptive processing (STAP)-ISAR formulation was proposed. In the case of bistatic geometry, ISAR imaging can be simply and effectively enabled via the bistatically equivalent monostatic theory, which allows for monostatic ISAR processing to be used to form bistatic ISAR images. However, the clutter nonstationary introduced by the bistatic geometry strongly degraded STAP performances if a monostatic STAP processing is used. In this paper, a new joint STAP-ISAR processing is introduced that works in the bistatic case. Theoretical figures and numerical simulations are used to prove the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Training samples contaminated by target-like signals is one of the major reasons for inhomogeneous clutter environment. In such environment, clutter covariance matrix in STAP (space-time adaptive processing) is estimated inaccurately, which finally leads to detection performance reduction. In terms of this problem, a STAP interference detection method based on simplified TT (time-time) transform is proposed in this letter. Considering the sparse physical property of clutter in the space-time plane, data on each range cell is first converted into a discrete slow time series. Then, the expression of simplified TT transform about sample data is derived step by step. Thirdly, the energy of each training sample is focalized and extracted by simplified TT transform from energy-variant difference between the unpolluted and polluted stage, and the physical significance of discarding the contaminated samples is analyzed. Lastly, the contaminated samples are picked out in light of the simplified TT transform-spectrum difference. The result on Monte Carlo simulation indicates that when training samples are contaminated by large power target-like signals, the proposed method is more effective in getting rid of the contaminated samples, reduces the computational complexity significantly, and promotes the target detection performance compared with the method of GIP (generalized inner product). [ABSTRACT FROM AUTHOR]

STAP-2 is an adaptor molecule regulating several signaling pathways, including TLRs and cytokine/chemokine receptors in immune cells. We previously reported that STAP-2 enhances SDF-1α-induced Vav1/Rac1-mediated T-cell chemotaxis. However, the detailed mechanisms of STAP-2 involvement in enhancing T-cell chemotaxis remain unknown. In the present study, we demonstrate that STAP-2 directly interacts with Pyk2, which is a key molecule in the regulation of SDF-1α/CXCR4-mediated T-cell chemotaxis, and increases phosphorylation of Pyk2. Pyk2 itself can induce STAP-2 Y250 phosphorylation, and this phosphorylation is critical for maximal interactions between STAP-2 and Pyk2. Finally, SDF-1α-induced T-cell chemotaxis is inhibited by treatment with Pyk2 siRNA or AG17, an inhibitor of Pyk2, in Jurkat cells overexpressing STAP-2. Taken together, the Pyk2/STAP-2 interaction is a novel mechanism to regulate SDF-1α-dependent T-cell chemotaxis. [ABSTRACT FROM AUTHOR]

Because of the transmit power dispersion, the output signal-to-clutter-plus-noise ratio (SCNR) of the traditional airborne multiple-input multiple-output (MIMO) radar space-time adaptive processing (STAP) is severely restricted. To address this problem, a transmit beamspace (TB)-based tri-iterative MIMO-STAP method is proposed. First, the signal model of the TB-based MIMO radar STAP is established, and the TB matrix is designed based on spheroidal sequences to focus the transmit power within the spatial sector of desired targets. Then, the clutter-to-noise ratio (CNR) of the TB-based MIMO radar is analysed to show its relationship with the total transmit power. The calculated value is provided to illustrate that the CNR of the TB-based MIMO radar is reduced compared with the traditional MIMO radar with uniform omnidirectional transmission. Next, in order to decrease the training sample requirement and the computational complexity of the TB-based MIMO-STAP, the tri-iterative algorithm (TRIA) is employed to resolve the reduced-dimension weight vectors. The proposed TB-based tri-iterative MIMO-STAP method can achieve superior SCNR performance at lower computational cost, compared with the tri-iterative STAP for the traditional MIMO radar. Simulation results demonstrate the effectiveness and superiority of the proposed TB-based tri-iterative MIMO-STAP method, which is valuable for engineering application. [ABSTRACT FROM PUBLISHER]

The target detection performance decreases in airborne multiple-input multiple-output (MIMO) radar space-time adaptive processing (STAP) when the training samples contaminated by interference-targets (outliers) signals are used to estimate the covariance matrix. To address this problem, a knowledge-aided (KA) generalized inner product non-homogeneity detector (GIP NHD) is proposed for MTMO-STAP. Firstly, the clutter subspace knowledge is constructed by the system parameters of MIMO radar STAP. Secondly, the clutter basis vectors are utilized to compose the clutter covariance matrix offline. Then, the GIP NHD is integrated to realize the effective training samples selection, which eliminates the effect of the outliers in training samples on target detection. Simulation results demonstrate that in non-homogeneous clutter environment, the proposed KA-GIP NHD can eliminate the outliers more effectively and improve the target detection performance of MIMO radar STAP compared with the conventional GIP NHD, which is more valuable for practical engineering application. [ABSTRACT FROM AUTHOR]

A cascaded reduced-dimension (RD) space-time adaptive processing (STAP) method for airborne multiple-input multiple-output (MIMO) radar in the presence of jammers is proposed in this paper. The proposed MIMOSTAP method for clutter plus jamming suppression proceeds in two steps. Firstly, the jamming is suppressed by its orthogonal complementary subspace obtained in the passive radar mode, while the receive dimension is reduced. Secondly, the tri-iterative algorithm (TRIA) is utilized to suppress the clutter combining the remaining receive degree o f freedom (DOF) with the transmit DOF and the Doppler DOF, and further dimension reduction is implemented. The proposed method can effectively realize the separate jamming and clutter elimination. Moreover, the training sample number and the computational complexity are significantly decreased. Simulation results verify the validity of the proposed cascaded RD MIMO-STAP method under jamming condition. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents a novel a priori knowledgebased algorithm for traffic monitoring applications. The powerful post-Doppler space-time adaptive processing (PD STAP) is combined with a known road network obtained from the freely available OpenStreetMap (OSM) database. The road information is applied after the PD STAP for recognizing and rejecting false detections, and moreover, for repositioning the vehicles detected in the vicinity of the roads. The algorithm presents great potential for real-time processing, decreased hardware complexity and low costs compared to state-of-the-art systems. The processor was tested using real multi-channel data acquired by DLR's airborne system F-SAR. The experimental results are shown and discussed, and the novelties are highlighted (e.g., the benefits of using a priori knowledge information). [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparsity-based direct data domain space-time adaptive processing (D3-STAP) algorithm for airborne radar that considers the intrinsic clutter motion (ICM). The proposed D3-STAP scheme models the received returns in the presence of ICM as a sparse measurement model. Then, we derive the principle of the sparsity-based D3-STAP that uses the focal underdetermined system solution (FOCUSS) method. The proposed D3-STAP algorithm estimates the clutter covariance matrix by a Hadamard product of the covariance matrix taper (CMT) and the clutter covariance matrix estimate with the FOCUSS technique. In addition, we develop a CMT adaptation approach for the proposed D3-STAP algorithm to automatically select the best CMT. Simulation results show that the proposed algorithm outperforms the existing D3-STAP using the least-squares technique and the sparsity-based D3-STAP algorithm without CMT. [ABSTRACT FROM AUTHOR]

We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability. [ABSTRACT FROM PUBLISHER]

The performance of antijamming is limited by channel mismatch in global navigation satellite system (GNSS) antenna arrays receivers. Only when the amplitude and phase characteristics of each array channels are the same is the interference likely to be completely suppressed. This paper analyzes the impact on antijamming performance of channel mismatch. We built the model of channel mismatch and derived the impact on transfer function with space-time adaptive processor (STAP) of channel mismatch in theory. The impact factor of channel mismatch is proposed by the fuzzy transfer function, which could directly reflect the antijamming performance under channel mismatch. In addition, every characteristic in the channel mismatch model is analyzed. The analysis results show that the greatest impacts on antijamming performance are the range of amplitude wave and the group delay bias, while the influence of the number of amplitudes is next. As for the effect of group delay fluctuation is the smallest. [ABSTRACT FROM AUTHOR]

The angle and Doppler estimation performance of the traditional space-time adaptive processing (STAP) method usually degrades in terms of estimation accuracy, especially for slowly moving targets. In this paper, a novel sum and difference beamforming approach with STAP-based radar is proposed, where multiple constraints, including the magnitude and phase constraints, derivative constraints, and zero-point constraints, are incorporated. Since the derivative constraints are independent of the array geometry, the proposed sum and difference beamforming method is applicable for both symmetric and asymmetric arrays. Also, the coupling relationship between angle and Doppler estimates is resolved by properly modifying the zero-point constraints. Moreover, the proposed method is robust against the coherent jamming contamination. Numerical examples indicate that the proposed method offers a significant performance improvement in angle and Doppler parameters estimation. [ABSTRACT FROM AUTHOR]

This paper proposes a spatiotemporal decomposition for the detection of moving targets in multi-antenna synthetic aperture radar (SAR). As a high-resolution radar imaging modality, SAR detects and localizes nonmoving targets accurately, giving it an advantage over lower-resolution ground-moving target indication (GMTI) radars. Moving target detection is more challenging due to target smearing and masking by clutter. Space-time adaptive processing (STAP) is often used to remove the stationary clutter and enhance the moving targets. In this work, it is shown that the performance of STAP can be improved by modeling the clutter covariance as a space versus time Kronecker product with low-rank factors. Based on this model, a low-rank Kronecker product covariance estimation algorithm is proposed, and a novel separable clutter cancelation filter based on the Kronecker covariance estimate is introduced. The proposed method provides orders of magnitude reduction in the required number of training samples as well as improved robustness to corruption of the training data. Simulation results and experiments using the Gotcha SAR GMTI challenge dataset are presented that confirm the advantages of our approach relative to existing techniques. [ABSTRACT FROM AUTHOR]

Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID$2011$ , iLIDS-VID, and HDA+ image sequence datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-/multi-shot ReID methods. [ABSTRACT FROM AUTHOR]

In the conventional space–time signal model, the statistical amplitude of the clutter is assumed to be a specific distribution. For sea clutter, a pulse-to-pulse correlation matrix is added to the temporal covariance matrix to describe the motion of the sea surface. However, such sea clutter model cannot reflect the property of the sea clutter. In this paper, a space–time model for the sea clutter is presented based on the physical sea surface model, in which the clutter amplitude is deterministic instead of statistic. The reflectivity and the radial velocity of the sea clutter for any specific position and time are computed based on the physical sea surface. Both the aforementioned two factors vary with time, which corresponds to the time variation of the sea surface. Moreover, the spatial channel decorrelation is modeled, which has an effect on the spatial covariance matrix of the sea clutter. The simulated angle-Doppler spectra and the signal-to-clutter-plus-noise-ratio loss show that the reflectivity of the sea clutter makes the space–time-adaptive-processing performance degrade, and the radial velocity of the sea clutter results in a more significant spread of the clutter power spectrum in sea state 4 than sea state 2. The Doppler spectrum can be acquired by the space–time model proposed in this paper instead of the experimental Doppler model for sea clutter. For a given sea state, the low range resolution has a more prominent effect on the power spectrum because of severer spatial channel decorrelation. [ABSTRACT FROM PUBLISHER]

The long relative propagation delays between the underwater acoustic channels poses a challenge to the detection of the multiple-input multiple-output signals but also gives a chance for a better space-time signal processing scheme. This paper proposes a detection ordering scheme for the layered space-time detection with the successive interference cancellation (SIC) algorithm, where the channel relative delays leading asynchronous arrival of the layered signals are utilized to arrange the detection order that is quite important for a SIC detection. This delay-based ordering is demonstrated as an optimal one for minimizing the detection error probability via the geometrically based model of the SIC detection. The complexity and calculation of the ordering procedure are significantly decreased by means of the delay estimations of the sub-channels. An iterative layered spacetime detector combining the delay-base ordered SIC algorithm with the iterative block decision feedback equalizer is employed, where the iterative equalizer is utilized for the cancellation of the multipath interference and the asynchronous arrival interference. Numerical results show that up to 4 dB performance gain obtained by the delay-based ordered SIC detection for a 2×2 MIMO system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Traditional space-time adaptive processing (STAP) is a strategy for clutter suppression in airborne radar, which requires a large number of computational complexity and secondary data. In order to address the problem, reduced-dimension (RD) STAP is generally used. We propose a novel RD STAP through searching the best channels as the auxiliary channels to cancel the interference. Based on the estimation of the clutter Fourier basis vectors offline, a parameter named angle-Doppler correlation coefficient (AD C ) is constructed to evaluate the capability of each auxiliary channel in clutter suppression, and the best sets of RD channels can be selected. The proposed algorithm can achieve the best detection performance with the fixed number of auxiliary channel. When the degrees of freedom (DOF) are restricted to a small value, only one auxiliary channel is needed to guarantee the SINR loss less than 3 dB. Therefore, the requirement of the training sample can be reduced, which makes the proposed approach more suitable for the heterogeneous clutter environments. [ABSTRACT FROM AUTHOR]

Results are reported from the first detection of perturbations of space-time, gravitational waves of cosmic origin. An alternative approach to estimating the energy losses through gravitational waves in a binary system of astrophysical objects is introduced. Possibilities for further study are examined. [ABSTRACT FROM AUTHOR]

A high-pulse-repetition-frequency (PRF) radar can handle the high Doppler frequencies of clutter echoes received by a fast-moving airborne radar. However, high-PRF radar causes range ambiguity. In addition, the clutter is range dependent when the airborne radar works in a forward-looking geometry. The range ambiguity and range dependence will lead to severe performance degradation of the traditional space-time adaptive processing (STAP) methods. In this paper, a vertical frequency diverse array (FDA), which applies frequency diversity in the vertical of a planar array, is explored to circumvent the range ambiguity problem in STAP radar. A range-ambiguous clutter suppression approach is devised, which consists of vertical spatial frequency compensation and pre-STAP filtering. In the vertical-FDA radar, the vertical spatial frequency depends not only on the depression angle but also on the slant range. By using this characteristic, the range-ambiguous clutter can be separated in the vertical spatial frequency domain, and then, clutter suppression is achieved for each separated range region. As a result, both problems of range ambiguity and range dependence are solved. Simulation results are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment. [ABSTRACT FROM AUTHOR]

The maritime surveillance performance of the adaptive normalised matched filter (ANMF) detector structure against real multichannel, medium grazing angle, radar sea clutter data processed via space-time adaptive processing (STAP) is assessed and shown to exhibit constant false alarm rate (CFAR) performance characteristics which diverge from predictions over a large segment of the endo-clutter spectrum. The non-CFAR behaviour is linked to the existence of a two-component clutter model composed of contributions from Bragg and fast scattering mechanisms. [ABSTRACT FROM PUBLISHER]