Clutter Suppression Performance Research Articles

A STAP method based on atomic norm minimization for transmit beamspace-based airborne MIMO radar

The output signal-to-clutter-plus-noise ratio (SCNR) of space-time adaptive processing (STAP) decreases due to the dispersion of the transmit energy for traditional airborne multiple-input-multiple-output (MIMO) radar. Moreover the sufficient training samples cannot be provided to estimate the clutter covariance matrix (CCM) in the non-stationary environment. To solve these problems, a novel STAP method based on the atomic norm minimization (ANM) for transmit beamspace-based (TB-based) airborne MIMO radar is proposed. Firstly, the signal model of TB-based MIMO-STAP is established, then the optimizing principle based on the ANM is presented to design TB matrix used to focus the transmit energy in a certain spatial sector. Moreover, the beampattern corresponding to the TB matrix is close to the desired beampattern in different working modes. Meanwhile, to further reduce the number of the training samples, an accurate CCM estimation with the low-rank property is yielded by applying the ANM theory into the TB-based MIMO-STAP. Compared with the conventional MIMO-STAP with the orthogonal waveforms, simulation results show that the output SCNR of TB-based MIMO-STAP is improved. Moreover, since the CCM of the proposed method is estimated in continuous domain, the off-grid problem can be avoided. Thus, the clutter suppression performance is superior over the conventional sparse representation based STAP (SR-STAP). Meanwhile, simulation results show that the performance of the proposed method is close to that of the statistical classical MIMO-STAP and outperforms that of SR-STAP. Furthermore, it can be shown from the simulation results that the clutter suppression performance is still robust when the low-rank property of the CCM is destroyed.

Effective Sea Clutter Suppression via MIMO Radar Space–Time Adaptive Processing Strategy

Due to nonlinear varying motions of the physical sea surface, sea clutter suppression is challenging with the broadened and rapidly changed Doppler spectrum content. In this article, an effective sea clutter suppression scheme based on multiple-input–multiple-output (MIMO) radar is presented via studying sea clutter features and reconstructing clutter spatial-Doppler spectrum accurately. To depict complicated Doppler features more precisely, the multistage Gaussian mixture model (GMM) is first developed in this article. In terms of exploring the dominant Doppler characteristics, GMM is more applicable in dynamic sea environments, including main swell and breaking waves. Afterward, for accurate reconstruction of clutter spectrum, the MIMO radar Gaussian mixture prior space–time adaptive processing (GMP-STAP) scheme is proposed for accurate estimation of clutter features. The Kullback–Leibler (KL) divergence is developed, and approximate distribution of posterior probability density functions (PDF) can be explored with low information loss based on complicated prior structure. Closed-form solutions of variables and parameters can be formulated through the minimization of KL divergence. The robust multistage Doppler power spectrum of sea clutter can be reconstructed accordingly. Moreover, considering that the independent and identically distributed (IID) property is easily broken among reference samples, the effective cross-correlation analysis is launched to keep statistical characteristics identical and reduce statistical errors. Finally, according to the experiments, accurate reconstruction of sea clutter features can be realized, and effective sea clutter suppression performance can be illustrated.

Direct data domain space-time adaptive processing method based on joint sparse representation with single snapshot for MIMO radar

To improve the performance of exiting sparse recovery-based space-time adaptive processing (STAP) methods in nonstationary and heterogeneous environments, a STAP method, called the joint sparse representation (JSR)-based direct data domain STAP method using MIMO subarraying radar, is proposed. First, by exploiting the property of waveforms orthogonal diversity of MIMO subarraying radar, the received single snapshot signal of the range cell under test is expressed as multiple snapshot signals. Second, the JSR model of clutter plus target spatiotemporal spectrum estimation is established and the joint sparsity is proved. The spatiotemporal spectrum of clutter and target is then estimated by the typical JSR algorithm. At last, the clutter covariance clutter matrix and the corresponding weight vector of the STAP processor are calculated based on the clutter space-time distribution extracted by the rough priori knowledge of the target. By utilizing the data of the range cell under test only, the proposed method could achieve accurate spatiotemporal spectrum estimation and great performance of clutter suppression. Experimental simulations demonstrate the validity of the proposed method.

Reduced-DOF Three-Dimensional STAP via Subarray Synthesis for Nonsidelooking Planar Array Airborne Radar

Compared with conventional two-dimensional space-time adaptive processing (2D-STAP) methods, the elevation-azimuth-Doppler three-dimensional space-time adaptive processing (3D-STAP) method has the advantage of suppressing nonstationary clutter. Thereby, it is suitable for nonsidelooking airborne radar (non-SLAR) applications. However, its huge training data requirements and computational load are often beyond radar's ability in practical clutter environments. In this correspondence, we develop a simple but efficient reduced-degree-of-freedom (DOF) 3D-STAP method that significantly reduces the required training data and the computational complexity while maintaining the suboptimal clutter suppression performance. The proposed method transforms the planar array data into linear array data in azimuth and in elevation, respectively, thereby beamforming an equivalent cross-shape array prior to STAP. In consequence, only a few spatial DOFs, including azimuth and elevation dimension, are used for STAP directly resulting in the potential advantage for nonstationary clutter suppression and drastically reducing training data requirements and computational load. Furthermore, the clutter rank estimation rules of the planar array and the transformed cross-shape array are derived, and the required elevation DOFs of the proposed method are further discussed in detail. Simulations for clutter suppression of non-SLAR show that the proposed STAP method outperforms the state-of-the-art 3D-STAP method in terms of convergence and computational complexity.

A Recursive Angle-Doppler Channel Selection Method for Reduced-Dimension Space-Time Adaptive Processing

This article considers the problem of target detection for applications with limited training samples. Dimensionality reduction in the angle-Doppler domain is considered. An algorithm for selecting the best angle-Doppler channels that maximize the output signal-to-clutter-plus-noise ratio in space-time adaptive processing (STAP) is proposed. Compared with several existing STAP methods, the proposed method can achieve better clutter suppression performance at lower computational complexities when the degrees of freedom of the STAP system are fixed. Moreover, the proposed method can help address the issue of training data shortage, which may be particularly attractive for heterogeneous scenarios. Simulations are conducted for validating the proposed method and demonstrating their high performance.

Clutter suppression algorithm for non‐side looking airborne radar with high pulse repetition frequency based on elevation–compensation–prefiltering

Clutter suppression for non-side-looking airborne radar operating at high pulse repetition frequency is an intractable problem because the clutter statistical properties vary with the range, especially in the near range. In addition, the near-range clutter is always coupled with the far-range clutter. Elevation prefiltering is recognised as an effective method to suppress the range-dependent near-range clutter, but the elevation training samples required must be independent and identically distributed (IID) and not contaminated by the far-range clutter. Here, an elevation-compensation-prefiltering space-time adaptive processing (STAP) algorithm is developed. In the proposed algorithm, the elevation training samples are selected from the elevation snapshots corresponding to the first pulse, which are not contaminated by the far-range clutter. An elevation compensation method is presented to alleviate the range dependence, and then there are adequate IID training samples to estimate the elevation covariance matrix. The performance of the near-range clutter suppression is improved. As for the residual range-independent far-range clutter, it can be suppressed by the cascaded azimuth-Doppler STAP. Numerical examples demonstrate the effectiveness of the proposed algorithm.

Waveform Design for MIMO Radar Detection in a Heterogeneous Clutter Environment

In this study, the problem of transmitted waveform design for a multiple-input-multiple-output (MIMO) radar in a heterogeneous clutter environment is considered. Under a Bayesian framework, the waveform design problem is used to maximize the generalized likelihood ratio for a given false alarm probability. In addition, the proposed problem is nonconvex under the constant modulus constraint. Hence, the alternating direction method of multipliers-based and the Broyden–Fletcher–Goldfarb–Shanno method are adopted to solve this nonconvex optimization problem. Finally, the clutter suppression performance of the proposed algorithms is evaluated in real-world data by numerical simulations. The simulation results show that the proposed methods have better performance on clutter suppression and target detection.

Robust Two-Stage Reduced-Dimension Sparsity-Aware STAP for Airborne Radar With Coprime Arrays

Space-time adaptive processing (STAP) algorithms with coprime arrays can provide good clutter suppression potential with low cost in airborne radar systems as compared with their uniform linear arrays counterparts. However, the performance of these algorithms is limited by the training samples support in practical applications. To address this issue, a robust two-stage reduced-dimension (RD) sparsity-aware STAP algorithm is proposed in this work. In the first stage, an RD virtual snapshot is constructed using all spatial channels but only $m$ adjacent Doppler channels around the target Doppler frequency to reduce the slow-time dimension of the signal. In the second stage, an RD sparse measurement modeling is formulated based on the constructed RD virtual snapshot, where the sparsity of clutter and the prior knowledge of the clutter ridge are exploited to formulate an RD overcomplete dictionary. Moreover, an orthogonal matching pursuit (OMP)-like method is proposed to recover the clutter subspace. In order to set the stopping parameter of the OMP-like method, a robust clutter rank estimation approach is developed. Compared with recently developed sparsity-aware STAP algorithms, the size of the proposed sparse representation dictionary is much smaller, resulting in low complexity. Simulation results show that the proposed algorithm is robust to prior knowledge errors and can provide good clutter suppression performance in low sample support.

Transmitter polarization optimization for space-time adaptive processing with diversely polarized antenna array

This paper considers the performance optimization of space-time adaptive processing (STAP) based on a diversely polarized antenna (DPA) array. A transmitter polarization design to enhance the detection performance is proposed. Accounting for the special structure of the polarization signal, we formulate the problem as a quasi-convex form based on the block majorization-minimization (MM) principle and solve it in an iterative framework. Compared with several existing methods, the proposed method has a quasi-convex form and can achieve better clutter suppression performance. We analyze the performance of conventional STAP and polarization-STAP (PSTAP), which indicates that the polarization diversity can help improve the performance of STAP for targets with low velocities, especially stationary ones. Simulations are conducted for validating the proposed methods and demonstrating their high performance.

A novel KA-STAP method based on Mahalanobis distance metric learning

The estimation accuracy of the clutter covariance matrix will be degraded when using the heterogeneous and contaminated training samples. To improve the performance of clutter suppression in heterogeneous environments, a novel knowledge aided space-time adaptive processing method is proposed in this paper, which is based on Mahalanobis distance metric learning (MML-KA STAP). Exploiting the difference of the Mahalanobis distances between the actual echo data and the synthesized data, the data is divided into the pure part and non-pure part. Taking the pure data as training samples, the interference covariance matrix of the cell under test (CUT) is reconstructed. Finally, by learning the Mahalanobis distance function between the CUT and training samples, the proper training samples can be chosen and reweighted to construct the interference covariance matrix. When the termination condition of iteration is satisfied, a relatively accurate interference covariance matrix can be estimated. The MML-KA method avoids the effect of target self-nulling and has superior performance in heterogeneous environments. Using the measured data, numerical simulations validate the effectiveness of the proposed method.

An adaptive coding-angle-Doppler clutter suppression approach with extended azimuth phase coding array

The range ambiguity degrades the clutter suppression performance and incurs parameters estimation ambiguities in application of ground moving target indication (GMTI). Thus, in this paper, an adaptive clutter suppression approach based on extended azimuth phase coding (EAPC) for multiple-input multiple-output (MIMO) radar system is presented. The EAPC array encode the transmit pulses for each transmit element, which can introduce additional controllable degrees-of-freedom (DOFs) in coding dimension at the transmit elements. Then, with the DOFs in transmit, receive and pulse dimensions, the coding-angle-Doppler processing can be performed. The echoes from different range-ambiguous regions can be well separated in joint transmit-receive spatial frequency domain by properly designing the EAPC shift factor. In the sequel, the three-dimensional localization (3DL) technique is employed to suppress the range ambiguous clutters, which can evidently reduce the dimensions of the processor. Finally, the results of the experiments based on simulated data demonstrate that the proposed framework can effectively alleviate the space-time adaptive processing (STAP) performance degradation resulting from the range ambiguity.

Adaptive suppression of main‐lobe spread Doppler clutter with high directivity for HFSSWR using oblique projection

The performance of high-frequency hybrid sky-surface wave radar (HFSSWR) is known to suffer from the spread Doppler clutter (SDC). As a major source of the SDC, ionospheric clutter generally possesses high directivity within the main detection range. Classical coherent sidelobe cancellation method provides an efficient way to suppress the highly directional clutter. However, it performs poorly when the directions of a target and the clutter are both in the main lobe. To address this problem, a novel clutter suppression method using oblique projection is proposed, which does not require priori knowledge of the targets, such as directions, Doppler frequencies, strength etc. By combining training data selection based on oblique projection with SDC suppression in the Doppler domain, this method is implemented in the interested range cell for all target directions. Experimental results indicate that the proposed method can achieve better clutter suppression performance based on real data.

Spatial‐temporal correlated sea clutter simulation based on the probability definition with extended samples for airborne radar

Sea clutter is often examined to be of spatial-temporal correlation which highly affects the detection performance on ship targets for airborne early warning radar. Therefore, the performance of spatial-temporal correlation sea clutter suppression is of significance for assessing ability of radar and radar system design. This study proposes a new method to simulate spatial-temporal correlated sea clutter with specific distribution form. The new method is presented based on the probability definition with extended samples, and its core operation is the mapping without distorting the distribution form to obtain 2-D correlated sea clutter. Therefore, it is more effective with comparison of conventional zero memory non-linear transform and spherically invariant process. The effectivity of proposed method is verified by means of measured data and simulation data simultaneously.

General space‐time clutter model for multi‐channel airborne radar

Ground clutter of looking-down airborne radar is spread in Doppler because of the platform motion, and thus the interested moving targets may be masked by the ground clutter. In order to enhance the performance of clutter suppression, the clutter characteristics should be analysed in detail. So here, a general clutter model which suits for the conventional-phased array radar, multiple-input-multiple-output (MIMO) radar, and hybrid MIMO-phased array radar, all with arbitrary array shape, whether in monostatic or bistatic applications, is developed. Clutter characteristics under two typical scenarios are presented by simulation examples, simultaneously; the effectiveness of the general clutter model is demonstrated.

Training Data Selection and Update Strategies for Airborne Post-Doppler STAP

Space–time adaptive processing (STAP) of multichannel radar data is an established and powerful method for detecting ground moving targets, as well as for estimating their geographical positions and line-of-sight velocities. Crucial steps for practical applications are: 1) the appropriate and automatic selection of the training data and 2) the periodic update of these data to take into account the change of the clutter statistics over space and time. Improper training data and contamination by moving target signals may result in a decreased clutter suppression performance, an incorrect constant false alarm rate threshold, and target cancelation by self-whitening. In this paper, two conventional and two novel methods for training data selection are evaluated and compared using real four-channel X-band radar data acquired with DLR’s airborne sensor F-SAR. In addition, a module for rejecting potential moving target signals and strong scatterers from the training data is proposed and discussed. All methods are evaluated for a conventional post-Doppler (PD) STAP processor and for a particular PD STAP that uses an a priori known road map.

Reduced Dimension STAP Based on Sparse Recovery in Heterogeneous Clutter Environments

For airborne-phased array radar systems, space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in the strong clutter background. However, practical application environments are always heterogeneous and have offered a severe challenge to the implementation of STAP. To address this reality, a data-dependent reduced dimension STAP approach based on sparse recovery (SR) is proposed in this paper. To take a full account of the heterogeneous environments, we consider the extremely heterogeneous case in which only one single snapshot is available. There is no doubt that, compared with the clutter covariance matrix (CCM) calculated directly by a single snapshot, the SR technique can provide a more accurate estimate. However, we should come to realize that this estimation is not accurate enough to adaptive processing according to the presented simulation results in lots of literature, and it has been demonstrated that the performance of traditional SR-based STAP degrades dramatically when only a snapshot is available. In the proposed approach, the CCM estimated by SR technique is utilized to design the reduced dimension transformation matrix rather than to calculate adaptive weights as the traditional SR-based STAP. The relatively accurate CCM can provide better support for the design of reduced dimension transformation matrix. From the simulation results, the proposed approach can achieve great performance of clutter suppression and target detection with only a single snapshot compared with several typical STAP algorithms. It is worthwhile pointing out that the proposed approach can also be applied when multiple snapshots are available and the performance improves with increasing number of available snapshots.

Space time adaptive processing based on sparse recovery and clutter reconstructing

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.

Radar Detection of Small Target in Sea Clutter Using Orthogonal Projection

Sea clutter submerges the small target echo, which is disadvantageous for radar target detection. In this letter, we propose to suppress the sea clutter by orthogonal projection (OP). We construct the clutter subspace directly by the observed data vectors of neighboring range cells of the cell under test (CUT), and then suppress the sea clutter of the CUT by projecting the observed signal of the CUT to the clutter orthogonal subspace. We design a new detector which combines the cell averaging constant false alarm rate (CFAR) detector with the OP. The clutter suppression performance and computation complexity of OP are compared with those of singular value decomposition (SVD), respectively. Theoretical analysis and experimental results with real sea clutter demonstrate that the radar CFAR detection performance improves by using OP. And the CFAR detector based on OP is convenient to implement. The CFAR detection performance based on OP is the same as that based on SVD, whereas the computation complexity of OP is much less than that of SVD.

Polarization Diversity Technology Research in Passive Radar Based on Subcarrier Processing

The work in this paper is divided into two parts. The first part is going to investigate the potential benefits of clutter suppression in a polarization filtering technology. The performance of a conventional polarization filter is limited by the complex electromagnetic environment. The subcarrier-based polarization filter (SC-PF) enhances the robustness to the environmental disturbance. The experiment results show that the SC-PF has a better clutter suppression performance compared with a conventional polarization filter. The average signal-to-noise ratio increases about 5 dB in airplane detection and about 8 dB in drone detection. The other part is the preliminary analysis of target polarization characteristics. With the extraction of the polarization amplitude ratio (PAR) of the airplane and drone, the PAR characteristic of airplane fluctuates dramatically and the PAR characteristic of drone fluctuates relatively smooth. Therefore, the difference of PAR characteristics of different targets may be helpful in target classification and recognition.

Sparse Bayesian Learning Based Space-Time Adaptive Processing Against Unknown Mutual Coupling for Airborne Radar Using Middle Subarray

Sparse-recovery-based space–time adaptive processing (STAP) methods can exhibit superior clutter suppression performance with limited training data. However, the clutter suppression performance seriously degrades when the mutual coupling is present in the STAP array elements. In this paper, a sparse Bayesian learning (SBL)-based STAP method against the mutual coupling by using the middle subarray is proposed. Specifically, the mutual coupling matrix (MCM) of the STAP uniform linear array is approximately described as a banded symmetric Toeplitz matrix since the effect of mutual coupling between two array elements is inversely related to their distance and can be negligible when their distance is larger than a few wavelengths. Utilizing this specific structure of the MCM, a mutual coupling mitigation method is developed for STAP by rearranging the received snapshots with the designed spatial–temporal selection matrix. Then, to efficiently recover the clutter angle-Doppler profile, the modified fast converging SBL (FCSBL) named adaptive FCSBL algorithm and its multiple measurement vector case adaptive MFCSBL algorithm are developed with only partial hyper-parameters being updated in a single iteration. The proposed method not only achieves the superior clutter suppression performance in the presence of unknown mutual coupling, but also has low computational load. The simulation results are implemented to validate the effectiveness of the proposed methods.