Nonhomogeneous Clutter Research Articles

Space time adaptive processing for airborne MIMO radar based on space time sampling matrix

Different from the traditional airborne single-input multi-output (SIMO) radar, each array element of airborne multiple-input multiple-output (MIMO) radar transmits mutually orthogonal waveforms, and multiple array elements receive at the same time. A set of matched filters are used to separate the transmitted waveforms in the echo signal. When the elements of the transmitting antenna are close to each other, the airborne MIMO radar can gain the benefit of waveform diversity and produce a large spatial virtual receiving array. It has certain advantages in signal-to-noise ratio, spatial degree of freedom (DoF) and spatial resolution. In this paper, the clutter DoF estimation formula of airborne MIMO radar is given and can accurately estimate the clutter DoF values under different parameters. On this basis, this paper proposes a space-time adaptive processing (STAP) method based on space-time sampling matrix. This method utilizes the low rank characteristic of clutter covariance matrix, constructs the clutter covariance matrix based on space-time sampling matrix, and estimates the clutter power through subaperture smoothing. In the non-homogeneous clutter background, the method only needs a single sample to effectively suppress clutter. In the homogeneous clutter background, as the space-time equivalent array is a dense non-uniform array, the robustness of the method is further improved by adding a small amount of training sample information.

Fast Heterogeneous Clutter Suppression Method Based on Improved Sparse Bayesian Learning

In order to deal with the problem space-time adaptive processing (STAP) performance degradation of an airborne phased array system caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method based on sparse Bayesian learning is proposed in this paper, which only uses a single snapshot data of a cell under test (CUT) to suppress the clutter and has fast computational speed. Firstly, three hyper-parameters required to obtain the sparse solution are derived. Secondly, the comparative analysis of their iterative formulas is made, and the piecewise iteration of hyper-parameter that has an obvious influence on the computational complexity of obtaining sparse solution is presented. Lastly, with the approximate prior information of the target, the clutter sparse solution is given and its covariance matrix is effectively estimated to calculate the adaptive filter weight and realize the clutter suppression. Simulation results verify that the proposal can dramatically decrease the computational burden while keeping the superior heterogeneous clutter suppression performance.

End-to-End Moving Target Indication for Airborne Radar Using Deep Learning

Moving target indication (MTI) based on space–time adaptive processing (STAP) has been widely used in airborne radar due to its ability for clutter suppression performance. However, the existing MTI methods suffer from the problems of insufficient training samples and low detection probability in a non-homogeneous clutter environment. To address these issues, this paper proposes a novel deep learning framework to improve target indication capability. First, combined with the problems of target indication caused by the non-homogeneous clutter, the clutter-plus-target training dataset was modeled by simulation, where various non-ideal factors, such as aircraft crabbing, array errors and internal clutter motion (ICM), were considered. The dataset considers various realistic situations, making the proposed method more robust. Then, a five-layer two-dimensional convolutional neural network (D2CNN) was designed and applied to learn the clutter and target characteristics distribution. The proposed D2CNN can predict the target with a high resolution to implement an end-to-end moving target indication (ETE-MTI) with a higher detection accuracy. In this D2CNN, the input was obtained by the clutter-plus-target angle-Doppler spectrum with a low-resolution estimated only by a few samples. The label was given by the target angle-Doppler spectrum with a high-resolution obtained by the target’s exact angle and Doppler. Thirdly, the proposed method used a few samples to improve the target indication and detection probability, which solved the problem of insufficient samples in the non-homogeneous clutter environments. To elaborate, the proposed method directly implements ETE-MTI without the support of the conventional STAP algorithm to suppress the clutter. The results verify the validity and the robustness of the proposed ETE-MTI with a few samples in the non-homogeneous and low signal-to-clutter ratio (SCR) environments.

Diver tracking in unknown structured clutter background using a force-based GM-PHD filter

This paper considers the problem of tracking multiple human divers using a high-resolution 2D active sonar. The proposed solution can handle the real-world challenges of a time-varying number of targets, complex correlated diver dynamics affected by external factors (e.g., water current, activities of neighbouring divers and the intent of the divers themselves), and unknown spatial non-homogeneous clutter intensity due to the complex non-stationary underwater environment with structured clutter. The proposed algorithm uses the probability hypothesis density (PHD) filter with a novel force-based diver state evolution model, along with a log-Gaussian Cox process (LGCP) model for a global clutter spatial intensity estimation.In addition, the posterior Cramér-Rao lower bound (PCRLB), which quantifies the best possible accuracy in the presence of kinematic interactions among targets in an uncertain underwater structured environment, is derived as the benchmark for performance evaluation. Simulation results demonstrate the improved performance of the proposed method over the standard PHD filter.

Unsupervised Learning Discriminative MIG Detectors in Nonhomogeneous Clutter

Principal component analysis (PCA) is a commonly used pattern analysis method that maps high-dimensional data into a lower-dimensional space maximizing the data variance, that results in the promotion of separability of data. Inspired by the principle of PCA, a novel type of learning discriminative matrix information geometry (MIG) detectors in the unsupervised scenario are developed, and applied to signal detection in nonhomogeneous environments. Hermitian positive-definite (HPD) matrices can be used to model the sample data, while the clutter covariance matrix is estimated by the geometric mean of a set of secondary HPD matrices. We define a projection that maps the HPD matrices in a high-dimensional manifold to a low-dimensional and more discriminative one to increase the degree of separation of HPD matrices by maximizing the data variance. Learning a mapping can be formulated as a two-step mini-max optimization problem in Riemannian manifolds, which can be solved by the Riemannian gradient descent algorithm. Three discriminative MIG detectors are illustrated with respect to different geometric measures, i.e., the Log-Euclidean metric, the Jensen–Bregman LogDet divergence and the symmetrized Kullback–Leibler divergence. Simulation results show that performance improvements of the novel MIG detectors can be achieved compared with the conventional detectors and their state-of-the-art counterparts within nonhomogeneous environments.

A STAP clutter suppression algorithm based on sparse representation for small-array HFSWR

ABSTRACT In high-frequency surface wave radar (HFSWR) systems, the main-lobe of the receiving angle spectrum suffers from severe broadening due to a small-array aperture effect. This broadening causes more clutter components to be mixed into the main-lobe, resulting in target vessels more prone to be submerged in the clutter. This problem severely affects the target detection performance. A reduction in the number of array elements decreases the degrees of freedom (DOF), making clutter suppression more difficult. Space-time adaptive processing (STAP) has recently become a crucial tool for clutter suppression in advanced HFSWR systems, using two-dimensional data to increase DOF. Focusing on low DOF and typical nonhomogeneous clutter with high energy, this study proposes a STAP clutter suppression method by estimating clutter components accurately in the space-time domain based on sparse representation in few snapshots. The experimental results verify that the proposed algorithm provides high suppression performance for measured clutter.

Robust STAP for nonhomogeneous clutter suppression with a single snapshot

In this paper, in order to enhance the robustness of nonhomogeneous clutter suppression in airborne radar system, the fast iterative adaptive approach (FIAA) based space-time adaptive processing (STAP) is proposed. At the design stage, each single cell under test is processed individually with the FIAA-STAP instead of selecting the training samples from the secondary data. Although the single-snapshot FIAA-STAP improves clutter suppression performance in the presence of nonhomogeneous clutter and multipath interferences, compared with the conventional statistical methodologies based STAP, the possible presence of the target signal will corrupt the reconstruction of clutter covariance matrix (CCM), leading to the problem of target pollution. To solve this problem, the target-pollution-free CCM reconstruction using weighted spectral filtering based FIAA is further derived with a priori knowledge, where the computational efficiency is improved owning to the specific Hermitian Toeplitz-block-Toeplitz (HTBT) matrix structure. However, such a structure formation is strictly limited by the phased-array (PA) based radar system. To tackle such problem, a novel block-wise formation is extended from the spatial-temporal 2-D HTBT covariance matrix reconstruction, enhancing the systemic adaptability in both PA based and sum-and-difference channels based radar systems. Numerical and measured results corroborate the effectiveness of the considered FIAA-STAP method in nonhomogeneous clutter scenarios.

Subspace Detection for Range-Spread Target to Suppress Interference: Exploiting Persymmetry in Non-Homogeneous Scenario

This paper deals with subspace detection for range-spread target in non-homogeneous clutter with unknown covariance matrix where structured interference is presented in the received data. Through exploiting the persymmetry of the clutter covariance matrix, we propose two adaptive target detectors, which are referred to as persymmetric subspace Rao to suppress interference and persymmetric subspace Wald to suppress interference (“PS-Rao-I” and “PS-Wald-I”), respectively. The persymmetry-based design brings in the advantage of easy implementation for small training sample support. The signal flow analysis of the two detectors shows that the PS-Rao-I rejects interference and integrates signals successively through separated matrix projection, while the PS-Wald-I jointly achieves interference elimination and signal combination via oblique projection. In addition, both detectors are shown to be constant false alarm rate detectors, significantly improving the detection performance with other competing detectors under the condition of limited training.

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.

A Robust Variability Index CFAR Detector for Weibull Background

The Weibull distribution is commonly used for clutter modeling as it can provide a good fit to the experimental data over a wide range of conditions, and as such there is considerable attention to the development of constant false alarm rate (CFAR) detectors under such a clutter model assumption. The detection backgrounds of some radar systems are nonhomogeneous, which is a challenging task for CFAR detection. The variability index CFAR (VI-CFAR) detector is proposed for the nonhomogeneous Gaussian clutter in the literature. However, it might increase the false alarm rate significantly in the Weibull clutter due to the target-like clutter samples, and its performance degrades in the nonhomogeneous clutter with the presence of interfering targets at both sides of the cell under test. To address these problems, we propose a robust variability index CFAR detector for the Weibull background (RWVI-CFAR) with known shape parameters in this paper. In the RWVI-CFAR, we extend the VI-CFAR to the Weibull clutter and design an automatic outlier censoring maximum likelihood CFAR (AOCML-CFAR) strategy to improve the detection performance in multiple target situations. The proposed AOCML-CFAR strategy exploits the sparsity of interfering targets to censor the outliers adaptively. By selecting the AOCML-CFAR strategy, the anti-interference capability of the RWVI-CFAR is significantly improved. Simulated and experimental results show the favorable and robust performance of the RWVI-CFAR in the Weibull background.

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.

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors.

In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method.

CFAR Detection Based on the Nonlocal Low-Rank and Sparsity-Driven Laplacian Regularization for HFSWR

Target detection in high-frequency surface-wave radar (HFSWR) systems is a challenging task due to various sources of interference. In this article, we propose a robust constant false alarm rate (CFAR) detector to solve the problem of target detection for the HFSWR by exploiting the prior information on the detection environment. Clutter amplitudes in the HFSWR are commonly assumed to follow the Weibull distribution. Additionally, distribution parameters of the clutter have nonlocal self-similarity, and targets have sparsity as well as a certain geometric structure. We design an objective function based on the Bayesian framework, which consists of a data fidelity term and some regularization terms. Specifically, we use the nonlocal low-rank (NLR) regularization to represent the prior information regarding distribution parameters of clutter, and we adopt the combination of the sparsity and Laplacian regularization to represent the prior information regarding targets. The proposed objective function inherits advantages of the NLR, sparsity, and Laplacian regularization, thereby improving the performance of the proposed detector in the nonhomogeneous clutter. By optimizing the proposed objective function, we obtain estimates of clutter statistics for every cell under test and perform the CFAR detection. Experiments on both simulated data and experimental HFSWR data demonstrate that the proposed detector has a good false alarm regulation property and detection performance in the nonhomogeneous clutter.

A CA-CFAR-Like Detector Using the Gerschgorin Circle Theorem for Bistatic Space Based Radar

The problem of the constant false alarm rate (CFAR) detection of bistatic space based radar (B-SBR) is addressed under the background of strong and nonhomogeneous clutter. We propose a novel and simple cell averaging (CA)-CFAR-like (CA-CFAR-L) detection method based on the Gerschgorin circle theorem for B-SBR. First, we investigate the potential of a novel test statistic TCA-CFAR-L exhibiting better properties about discrimination and CFAR using the Gerschgorin circle theorem, and the test statistic TCA-CFAR-L is constructed as the ratio of the sum of the Gerschgorin radii of the covariance matrix of the cell under test to the sum of the Gerschgorin radii of the mean covariance matrix of the reference cells. Second, we investigate the CA-CFAR-L detector for B-SBR using the constructed test statistic TCA-CFAR-L. Under the background of the K-distribution clutter model and the clutter model for B-SBR, respectively, we verify the performance of the proposed CA-CFAR-L detector, the LE mean matrix CFAR detector and the Fast Fourier Transform (FFT)–CFAR detector. Also, we analyze the experimental data for the IPIX radar. The simulation results and experimental data analysis demonstrate that the detection performance of the detector is better than the LE mean matrix CFAR detector and the FFT–CFAR detector, and the proposed method has less computational complexity. These will provide an important theoretical basis and new ideas for radar target detection in the future.

Nonhomogeneous clutter suppression based on terrain elevation interferometric phase compensation in multi-satellite formation systems

In multi-satellite formation synthetic aperture radar (SAR) systems, the nonhomogeneous clutter problem arising due to the terrain elevation interferometric phase is a crucial factor that degrades clutter suppression performance. We propose a clutter suppression method based on terrain interferometric phase compensation (TIPC) that can overcome this problem. The method can be summarised as a rough-and-refined two-step phase compensation framework. In the rough-compensation stage, the terrain interferometric phase is estimated using similar phase samples in a local area. To ensure that more candidate samples are considered, a novel sample-selection window is designed based on fringe direction and density; its shape and size can be adjusted adaptively based on the fringe morphology. For refined topographic phase compensation, an error feedback weighting correction structure is designed to compensate for the fringe detail loss. The weighting coefficient is constructed based on coherence, phase residual distribution, and fringe structure features, which can be adapted to adjust the contribution of the phase estimation error. Moreover, to improve the topographic phase compensation performance, the proposed method can be applied iteratively. Finally, clutter suppression was performed after the TIPC processing. The experimental results demonstrate that the proposed method can obtain higher topographic phase compensation precision and better clutter suppression performance than conventional methods.

Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar

This paper deals with the problem of detection and direction of arrival (DOA) estimation of slowly moving targets against clutter in multichannel mobile passive radar. A dual cancelled channel space-time adaptive processing (STAP) scheme is proposed, aiming at reducing the system computational complexity, as well as the amount of required training data, compared to a conventional full array solution. The proposed scheme is shown to yield comparable target detection capability and DOA estimation accuracy with respect to the corresponding full array solution, despite the lower computational cost required. Moreover, it offers increased robustness against adaptivity losses, operating effectively even in the presence of a limited set of training data, as often available in the highly non-homogeneous clutter scenarios experienced in bistatic passive radar. The effectiveness of the proposed scheme and its suitability for passive GMTI are demonstrated against both simulated and experimental data collected by a DVB-T-based multichannel mobile passive radar.

FOD Detection Method Based on Iterative Adaptive Approach for Millimeter-Wave Radar

Using millimeter-wave radar to scan and detect small foreign object debris (FOD) on an airport runway surface is a popular solution in civil aviation safety. Since it is impossible to completely eliminate the interference reflections arising from strongly scattering targets or non-homogeneous clutter after clutter cancellation processing, the consequent high false alarm probability has become a key problem to be solved. In this article, we propose a new FOD detection method for interference suppression and false alarm reduction based on an iterative adaptive approach (IAA) algorithm, which is a non-parametric, weighted least squares-based iterative adaptive processing approach that can provide super-resolution capability. Specifically, we first obtain coarse FOD target information by data preprocessing in a conventional detection method. Then, a refined data processing step is conducted based on the IAA algorithm in the azimuth direction. Finally, multiple pieces of information from the two steps above are used to comprehensively distinguish false alarms by fusion processing; thus, we can acquire accurate FOD target information. Real airport data measured by a 93 GHz radar are used to validate the proposed method. Experimental results of the test scene, which include golf balls with a diameter of 43 mm, were placed about 300 m away from radar, which show that the proposed method can effectively reduce the number of false alarms when compared with a traditional FOD detection method. Although metal balls with a diameter of 50 mm were placed about 660 m away from radar, they also can obtain up to 2.2 times azimuth super-resolution capability.

Target Detection Within Nonhomogeneous Clutter Via Total Bregman Divergence-Based Matrix Information Geometry Detectors

Information divergences are commonly used to measure the dissimilarity of two elements on a statistical manifold. Differentiable manifolds endowed with different divergences may possess different geometric properties, which can result in totally different performances in many practical applications. In this paper, we propose a total Bregman divergence-based matrix information geometry (TBD-MIG) detector and apply it to detect targets emerged into nonhomogeneous clutter. In particular, each sample data is assumed to be modeled as a Hermitian positive-definite (HPD) matrix and the clutter covariance matrix is estimated by the TBD mean of a set of secondary HPD matrices. We then reformulate the problem of signal detection as discriminating two points on the HPD matrix manifold. Three TBD-MIG detectors, referred to as the total square loss, the total log-determinant and the total von Neumann MIG detectors, are proposed, and they can achieve great performances due to their power of discrimination and robustness to interferences. Simulations show the advantage of the proposed TBD-MIG detectors in comparison with the geometric detector using an affine invariant Riemannian metric as well as the adaptive matched filter in nonhomogeneous clutter.

Low complexity single dataset STAP for nonstationary clutter suppression in HF mixed-mode surface wave radar

ABSTRACT The nonstationary clutter is one of the biggest challenge to ocean remote sensing radar system such as high frequency (HF) mixed-mode surface wave radar. The performance of space-time adaptive processing (STAP) degrades badly with limited homogeneous secondary training data support. Single dataset algorithms overcome the problem by working on primary data solely. But the heavy computational complexity as well as the inaccurate estimation of the clutter covariance matrix restricts the practical application of these methods. In this letter, we propose a novel reduce-rank-based single dataset STAP to suppress the nonhomogeneous clutter in practical HF radar system. A fast implementation of subspace tracking algorithm is introduced to estimate the clutter subspace as well as reduce the computational cost via pulse iteration method. The effectiveness of the proposed method is verified by both simulated and experimental data. The results show it outperforms traditional single dataset STAP methods and the nonstationary clutter can be greatly suppressed.

Information Geometric Means-Based STAP for Nonhomogeneous Clutter Suppression in High Frequency Hybrid Sky-Surface Wave Radar

The nonhomogeneous clutter is one of the biggest challenge for ocean remote sensing radar system such as high frequency hybrid sky-surface wave radar. Space-time adaptive processing (STAP) is a powerful method to suppress the clutter but the training samples in adjacent range gates are not always independent and identically distributed with the cell under test in the heterogeneous environment. So the estimation of the clutter covariance matrix will be inaccurate and degrade the STAP performance. In this paper, a novel training sample selection method based on information geometric means is proposed to choose the more homogeneous secondary data containing the precise clutter information of cell under test. The simulation results of selection performance is conducted to show the advantages of the Information geometric mean based estimators compared with the existing estimators. Combined with joint domain localized STAP, the effectiveness of the proposed method is verified by experimental data. The results show the proposed method outperforms conventional STAP method and the nonhomogeneous clutter can be greatly suppressed.