Space-time Adaptive Processing Research Articles

A Long-Time Coherent Integration STAP for GEO Spaceborne-Airborne Bistatic SAR

A geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR) system is an important technique to achieve long-time moving target monitoring over a wide area. However, due to special bistatic configuration of GEO SA-BSAR, two major challenges, i.e., severe range migration and space-variant Doppler parameters for moving targets, hinder the moving target indication (MTI) processing. Traditional SAR MTI methods, which do not take the challenges into consideration, will defocus the moving targets, leading to a loss of the signal-to-noise ratio (SNR). To focus moving targets and estimate motion parameters accurately, long-time coherent integration space-time adaptive processing (LTCI-STAP) is proposed for GEO SA-BSAR MTI in this paper. First, a modified adaptive spatial filtering based on the bistatic signal model is performed to suppress the clutter. Then, an LTCI filter bank is constructed to achieve range migration correction and moving target focusing, which yields the optimal output signal and filtering parameters. Finally, constant false alarm rate (CFAR) detection is carried out to determine the targets, and the space-variant Doppler parameters, solved from the filtering parameters, are used for estimating moving target positions and velocities. Simulations verify the effectiveness of our method.

Sub-CPI STAP based clutter suppression and target refocusing with airborne radar system

Under the interference of airborne radar clutter, it is a challenge for fast maneuvering target detection, because of the nonstationary echo signal involved within the long coherent processing interval (CPI). To tackle this issue, a robust clutter suppression and small maneuvering target detection method is proposed in airborne radar system, which is referred to as the sub-CPI space-time adaptive processing (STAP) based refocusing. For robust clutter suppression, the whole CPI is divided into a series of equal-length and overlapped sub-CPIs with the sliding window technique. In each sub-CPI, the STAP algorithm is adopted for robust clutter suppression, in which the consistent covariance matrix estimation is derived to guarantee the linear phase response within the whole CPI. However, as endo-clutter weak target is considered, the signal-to-clutter-plus-noise ratio (SCNR) after sub-CPI STAP still cannot meet the performance requirement. Thus, the target refocusing is applied for further SCNR improvement based on the semi-searching mechanism. However, the range walking (RW) and Doppler frequency migration problems defocus the energy of fast maneuvering target over range and Doppler frequency dimensions, respectively. To circumvent such problems, Keystone transform is utilized to correct the RW, followed by the Lv's distribution to achieve target refocusing. Numerical simulations and measured results with sum and difference channels validate the effectiveness of the proposed method.

Suppression of Jammer Multipath in GNSS Antenna Array Receiver

Interference multipath is an important factor to affect the anti-jamming performance for the global navigation satellite system (GNSS) antenna array receiver. However, interference multipath must be considered in practical application. In this paper, the antenna array model for interference multipath is analyzed, and an equivalent model for interference multipath is proposed. According to the equivalent interference multipath model, the influence of interference multipath on anti-jamming performance is analyzed from the space only processing (SOP) and space-time adaptive processing (STAP). Interference multipath can cause loss of the degree of freedom (DoF) of SOP. Through analysis of the equivalent model and STAP mechanism, it further reveals how the STAP can solve the interference multipath. The simulation experiments prove that the equivalent model is effective, and the analysis conclusion is correct. This paper also points out that the interference bandwidth is wider and more taps in STAP are required, under the same experiment conditions.

Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment

Towed array sonars are preferred for detecting stealthy underwater targets that emit faint acoustic signals in the ocean, especially in shallow waters. However, the towing ship being near to the array behaves as a loud target, introducing additional interfering signals to the array, severely affecting the detection and classification of potential targets. Canceling this underlying interference signal is a challenging task and is investigated in this paper for a shallow ocean operational scenario where the problem is more critical due to the multipath phenomenon. A method exploiting the eigenvector analysis of spatio-temporal covariance matrix based on space time adaptive processing is proposed for suppressing tow ship interference and thus improving target detection. The developed algorithm learns the interference patterns in the presence of target signals to mitigate the interference across azimuth and to remove the spectral leakage of own-ship. The algorithm is statistically analyzed through a set of relevant metrics and is tested on simulated data that are equivalent to the data received by a towed linear array of acoustic sensors in a shallow ocean. The results indicate a reduction of 20-25dB in the tow ship interference power while the detection of long-range low SNR targets remain largely unaffected with minimal power-loss. In addition, it is demonstrated that the spectral leakage of tow ship, on multiple beams across the azimuth, due to multipath, is also alleviated leading to superior classification capabilities. The robustness of the proposed algorithm is validated by the open ocean experiment in the coastal shallow region of the Arabian Sea at Off-Kochi area of India, which produced results in close agreement with the simulations. A comparison of the simulation and experimental results with the existing PCI and ECA methods is also carried out, suggesting the proposed method is quite effective in suppressing the tow ship interference and is immensely beneficial for the detection and classification of long-range targets.

Signal Model and Parameter Estimation for Colocated Mimo Radar

In this paper, we consider the signal model and parameter estimation for multiple-input multiple-output (MIMO) radar with colocated antennas on stationary platforms. Considering internal clutter motion, a closed form of the covariance matrix of the clutter signal is derived. Based on the proposed closed form and low rank property of the clutter covariance matrix and by using the singular value decomposition, we have proposed a subspace model for the clutter signal. Following the proposed signal model, we have provided maximum likelihood (ML) estimation for its unknown parameters. Finally, the application of the proposed ML estimation in space time adaptive processing (STAP) is investigated in simulation results. Our ML estimation needs no secondary training data and it can be used in scenarios with nonhomogeneous clutter in range.

Clutter Suppression for Wideband Radar STAP

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or “zero-bandwidth,” where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

Robust STAP Detection Based on Volume Cross-Correlation Function in Heterogeneous Environments

The performance of moving target detection in heterogeneous environments with the traditional space-time adaptive processing (STAP) may degrade when the real clutter environments deviate from the prior assumption on the clutter distribution. In this letter, a new detector for STAP applications based on volume cross-correlation function (VCF), namely VCF-STAP, is proposed to achieve robust performance of moving target detection in heterogeneous environments. In the new VCF-STAP, the VCF is used to form a distance measure between the sample signal subspace and the target subspace without modeling the clutter distribution. Then, a new robust STAP detection statistic is constructed using this distance measure. Simulation and experimental results show that the proposed VCF-STAP achieves robust performance of moving target detection in heterogeneous environments, especially it achieves much superior detection performance compared with existing STAP methods when the real clutter environments do not satisfy their prior assumptions. Besides, it is also shown that VCF-STAP has the constant false alarm rate (CFAR) property.

A Novel Dimension-Reduced Space–Time Adaptive Processing Algorithm for Spaceborne Multichannel Surveillance Radar Systems Based on Spatial–Temporal 2-D Sliding Window

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data.

Multi-Task Learning STAP via Spatial Smoothness and Group Sparsity Regularizations

In practical applications, limited independent and identically distributed training snapshots brings a serious challenge in space-time adaptive processing (STAP), especially in the nonhomogeneous environments. Motivated by the significant spatial smoothness and sparsity commonality of weight vectors among related STAP tasks, we propose a novel STAP algorithm based on multi-task learning. In the proposed algorithm, the weight vectors corresponding to neighboring range bins of interest are kept consistent, and all weight vectors are constrained to share a common feature. Then, an alternating direction method of multipliers (ADMM) is used to solve the proposed algorithm, and the convergence of the algorithm is guaranteed. In addition, in case that the feature matrix is unknown or we want to learn a better feature matrix so that the associations among STAP tasks can be enhanced, we also provide an extension of the proposed algorithm to jointly optimize the feature matrix and weight matrix. Simulation results demonstrate the effectiveness of the proposed strategies.

An Efficient Maritime Target Joint Detection and Imaging Method with Airborne ISAR System

In this paper, a joint maritime moving target detection and imaging approach, referred to as the fast inverse synthetic aperture radar (ISAR) imaging approach, based on the multi-resolution space−time adaptive processing (STAP), is proposed to improve the target detection performance and the target imaging efficiency in an airborne radar system. In the target detection stage, the sub-band STAP is introduced to improve the robustness of clutter suppression and to enhance the target output power with the decreased range resolution, by which the coarse estimation of target range-Doppler (R-D) location is obtained as the prior knowledge. In the following target imaging stage, the ISAR imaging is applied in the localized R-D zone surrounding with the target location. However, it is difficult to directly apply ISAR imaging with the conventional R-D algorithm because the slow-moving maritime target cannot be separated from the clutter interference in the Doppler frequency dimension. In this regard, the full-band STAP is applied in the R-D two-dimensional frequency domain for the simultaneous clutter suppression and high-resolution ISAR imaging, in which the envelope alignment and phase compensation are achieved by adaptive match filtering with the target Doppler frequency coarse estimation. Moreover, the reduced-dimension STAP applied in the target-surrounded localized Doppler frequency zone gives facilities for alleviating the computation burden. Simulation results corroborate the effectiveness of the proposed method.

Beam-Space Reduced-Dimension 3D-STAP for Nonside-Looking Airborne Radar

The space–time adaptive processing (STAP) technique has achieved good clutter suppression performance for a side-looking airborne radar; however, it suffers from severe performance degradation in nonside-looking airborne radar. This is because the clutter distribution varies considerably with range. The 3D STAP can provide better performance compared with the traditional STAP methods in such a nonstationary clutter environment, but it requires high computational complexity and sample support. In this letter, the characteristic of azimuth–elevation–Doppler 3-D beam pattern for the planar array is explored, and a novel reduced-dimension scheme combined with this characteristic is proposed. We further developed three basic reduced-dimension structures according to this scheme. Furthermore, we also prove that the first and third structures are just the direct extension of the traditional 2-D generalized multibeam and joint-domain localized methods. The second one is an original structure that performs local reduced-dimension processing with three orthogonal planar windows. Numerical results verify that the proposed structures have significant advantages in reducing computational load and training sample numbers compared with existing 3D STAP methods. In particular, the second structure shows the best comprehensive performance in the bad samples environment.

A Low-Complexity MIMO Radar STAP Strategy for Efficient Sea Clutter Suppression

When sparse reconstruction (SR) space-time adaptive processing (STAP) strategy is employed for sea clutter suppression, the computational burden imposed by the spectrum reconstruction process is still a challenge. Hence, a low-complexity MIMO radar SR STAP strategy is developed via reducing model dimension and optimizing the sparse reconstruction scheme. On one hand, STAP echo model is projected into space and time domains owing to the uncoupled relationship, thus the computation burden involved with large measurement matrix can be lessened by low-dimension structure. Meanwhile, a coprime scheme is exerted to improve the degree of freedom (DOF) in sub models and ensure lower sampling consumption. On the other hand, in order to realize efficient spectral reconstruction, an improved SR algorithm is formulated via developing a prior matrix factor and a tradeoff factor. Convergence speed of the algorithm is promoted by tuning the factors reasonably, clutter spectrum is quickly accessible with less iterations so that adaptive filter can be constructed efficiently. According to the experiment results, high-efficiency spectral reconstruction is realized and effective clutter suppression performance can be ensured.

Frequency Diverse Array Auto-Scanning Beam Characteristics and Potential Radar Applications

Different from the phased-array with the same carrier frequency for all elements providing only angle-dependent beampattern, frequency diverse array (FDA) using a small frequency offset across its array elements offers new application capabilities due to its both angle and range dependent beampattern, which allows auto beam-scanning capability even without the employment of phase shifters, which are required in phased-array. This paper studied FDA auto-scanning beam characteristics and several potential radar applications. The FDA auto-scanning beam characteristics including range and angle coupling, beam auto-scanning rate and integral array again are analyzed. To fully utilize FDA specific auto-scanning beam characteristics, several potential applications such as radio frequency (RF) stealth or RF localization deception, range-ambiguous clutter suppression and mainlobe interference suppression are discussed. Moreover, several discussions about space-time adaptive processing and possible future investigations are provided. This paper is formulated from a top-level system description, which aims to introduce this promising FDA technique to radar community so that more investigations can be facilitated.

STAP Training Samples Selection Based on GIP and Volume Cross Correlation

Space-time adaptive processing requires reliable training samples to support clutter covariance matrix calculation of the cell under test in the process of moving target detection. However, the heterogeneous clutter and the existence of outliers in the training samples violate the assumption of independent identically distributed with the cell under test, which is crucial for statistical estimation. To solve this problem, this letter suggests a new strategy to select training samples that are homogeneous with the cell under test. First, the generalized inner product method is used to filter training samples with outliers and generate the corresponding load matrix. Second, a distance metric between the training sample clutter subspace and the cell under test clutter subspace is formed by the volume cross-correlation function, the distance metric is used to judge the clutter similarity between the training sample and the cell under test. Finally, the training samples with similar clutter distribution characteristics to the cell under test are selected. Experimental results show that Space-time adaptive processing performance can be improved effectively, especially in heterogeneous environments.

A Novel Sea Clutter Rejection Algorithm for Spaceborne Multichannel Radar Systems

Due to the high-speed movement of a spaceborne radar (SBR) platform, the geographic clutter spectrum expands severely, resulting in the useful moving target signal submerged by the main-lobe clutter background. To deal with this issue, the equipped multichannel arrays in an SBR system provide sufficient spatial degrees, and as a consequence, the space-time adaptive processing (STAP) technology is often preferred to achieve the moving target detection, even in the main-lobe clutter regions. However, for the moving target detection under the sea scene, due to the complex internal motion of sea clutter, the clutter signal received by an SBR system may possess the space- and time-varying characteristics, worsening the multichannel clutter rejection performance using the traditional STAP techniques. In this article, a novel sea clutter suppression method based on the joint space-time-frequency adaptive filtering is proposed. In the proposed algorithm, according to the coherent time analysis of sea clutter, the subaperture time-domain sliding window is employed to alleviate the clutter decorrelation effect, and then, a modified subspace projection technique is applied to accomplish the first-stage clutter rejection. After realizing the effective signal recovery with respect to these residual subaperture clutter data, the second-stage spatial filtering method is applied to realize the final clutter suppression with respect to the relatively high Doppler resolution clutter returns. The effectiveness of the proposed algorithm is verified by both simulated multichannel sea clutter data and real-measured sea clutter data.

Multistage Least Squares Algorithms for Clutter Suppression in Airborne Passive Radar Based on Subband Operation

This article firstly analyzes the influence of utilizing the space-time adaptive processing (STAP) approaches in airborne passive radar (APR) systems on the dimension of the clutter subspace, and the signal-plus-noise ratio loss of the target, and illustrates that it is necessary to propose clutter cancellation algorithms suitable for APR systems. Combined with the property of APR systems, the multistage least squares algorithm based on subband operation (MLS-S) and the multistage least squares algorithm based on subband operation in temporal domain (MLS-ST) are proposed for the Doppler-shift clutter cancellation. The first method, in the case of the clutter with integer Doppler-shift bins, proves to be a simple solution. The second approach, at the expense of an additional stage, is suitable for more situations. Due to the use of Doppler-angle-dependence and subband operation to coarsen the range resolution, the proposed methods not only suppress effectively clutter along the clutter ridge, but also cancel the fractional-order-range clutter. In addition, the computational complexity of the proposed algorithms is low, and the clutter cancellation operations can be performed in parallel, which provide the possibility of real-time clutter suppression for APR systems. The feasibility of the proposed approaches is verified by diverse simulated scenarios.

A Clutter Suppression Algorithm via Weighted ${\ell }_2{\rm{ - norm}}$ Penalty for Airborne Radar

In this letter, to improve the performance of the space-time adaptive processing (STAP) filter with finite training samples, a novel algorithm with multiple measurement vectors (MMV) based on sparse recovery (SR) is proposed. Compared with traditional SR STAP algorithms, we utilize the knowledge of Capon spectrum to design a weighted <inline-formula><tex-math notation="LaTeX">${\ell }_2{\rm{ - norm}}$</tex-math></inline-formula> penalty which can better approximate the original <inline-formula><tex-math notation="LaTeX">${\ell }_0{\rm{ - norm}}$</tex-math></inline-formula>. Besides, the proposed algorithm has fast convergence performance and closed-form analytic solution in each iteration. Simulation results demonstrate the effectiveness and great performance of the proposed method.

Near-Range Clutter Suppression With Elevation Element Multifrequency Subpulse Coding Array Radar

It is hard to tackle the near-range clutter when range ambiguity exists in the non-sidelooking moving target detection (MTD) application. The angle-Doppler spectra of the far- and near-range clutter cannot be aligned simultaneously in this case due to the range dependence, which would significantly degrade the performance of the space–time adaptive processing (STAP) technology. To address this problem, an elevation element multifrequency subpulse coding (EMFSPC) array framework is proposed in this article. For each pulse duration, the main-lobe beam of the proposed framework can automatically sweep the full space by coding the subpulses and the transmitting elements, which steers the subpulses to different directions. Besides, these multiple subpulses occupy different range-frequency bands, and thus corresponding bandpass filters could be carefully designed to extract the unambiguous signals. After that, one can align the clutter spectra centers and employ the full-dimensional or dimension-reduced STAP techniques to achieve clutter cancellation. Furthermore, the simulation experiments are conducted to demonstrate the validity of the proposed EMFSPC system in near-range strong clutter suppression.

Optimally Matched Space-Time Filtering Technique for BFSAR Nonstationary Clutter Suppression

Clutter suppression in synthetic aperture radar (SAR) is one of the urgent and attractive problems in ground moving target indication (GMTI) application. With separate transmitter and receiver, ground clutter is nonstationary in bistatic forward-looking SAR (BFSAR), which directly leads to the inaccurate clutter covariance matrix (CCM) estimation. As a consequence, the traditional space-time adaptive processing (STAP) will suffer from a serious performance deterioration. In this article, an optimally matched space-time filtering (MSTF) technique is proposed to suppress nonstationary clutter for BFSAR systems. The main idea of the proposed method is to directly design and generate a suppression filter in space-time domain, whose space-time frequency response is matched with clutter spectrum. To construct the matched space-time filter, clutter modeling with arbitrary BFSAR configuration is first proposed to acquire space-time information of clutter spectrum. And then, the suppression filter can be designed and the design process is transferred into a constrained optimization problem (COP), according to the obtained clutter space-time information. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the COP and obtain the optimal solution, i.e., the desired matched space-time filter weight, for BFSAR nonstationary clutter suppression. Since the generation of the designed filter circumvents CCM estimation, the proposed method will not be affected by the nonstationary characteristic of BFSAR clutter. In October 2020, the first airborne BFSAR-GMTI experiment in the world has been successfully conducted by us, and the experimental results are given to validate the effectiveness of the proposed method.

Gridless Sparse Clutter Nulling STAP Based on Particle Swarm Optimization

Sparse clutter nulling space–time adaptive processing (STAP) methods achieve superior performance in the case of limited samples, so they are suitable for nonhomogeneous clutter environments. Traditional sparse clutter nulling STAP algorithms try to estimate the clutter subspace by selecting a suitable set of space–time steering vectors (atoms) in spatial-Doppler profile grids. However, the off-grid effect is inevitable for the nonside-looking case due to the nonlinear distribution of clutter and, thus, leads to significant performance degradation. To solve this problem, a gridless sparse clutter nulling STAP algorithm based on the particle swarm optimization (PSO) named PSO-STAP is proposed in this letter. A criterion function to evaluate the suitability of atoms is first devised. By regarding the criterion as a fitness function, PSO-STAP can select atoms in gridless spatial-Doppler profile, which is more suitable to construct the accurate clutter subspace. Numerical results verified the feasibility and superiority of the proposed algorithm.