Heterogeneous Clutter Research Articles

A Two-stage Detector for First-order Sea Clutter in HF Surface Wave Radar

The detection capabilities in vertical polarized, High-frequency SurfaceWave Radar (HFSWR) are limited by the clutter spikes in first-order sea clutter. The energy, position, and Doppler frequency of first-order sea clutter depend on the sea states, which are non-homogeneous – because of that, using a fixed expression to eliminate sea clutter gives limited results. In this paper, the authors proposed a new adaptive method using a two-stage detector based on the Order Statistic Constant False Alarm Rate (OS-CFAR) principle. The approach shows the capability to eliminate heterogeneous clutter and detect targets in the vicinities of sea clutter in the Range – Doppler map.

Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression

Due to clutter inhomogeneity, the clutter suppression ability of space–time adaptive processing (STAP) is usually constrained by the insufficient number of independent and identically distributed (IID) clutter training samples and, as a result, is sacrificed to achieve the demanded sample reduction. Moreover, since clutter heterogeneity is exacerbated in the real environment, the IID training sample size can be heavily reduced, leading to the deterioration in clutter suppression. To solve this problem, a novel robust space–time joint sparse processing method with airborne active array is proposed. This method has several outstanding advantages: (1) only the single snapshot cell under test (CUT) data is used for the superior clutter suppression performance; and (2) the proposed method completely removes the dependence of the system processing ability on IID training samples. In this paper, the signal model of uniform transmitting subarray diversity is first established to obtain the single snapshot echo observed CUT data. Then, with the matched reconstruction, the single snapshot data are equivalently converted into multi-frame echo data. Finally, a fast multi-frame echo data joint sparse Bayesian algorithm is used to achieve heterogeneous clutter suppression. Numerous experiments were performed to verify the advantages of the proposed method.

A Fast Space-Time Adaptive Processing Algorithm Based on Sparse Bayesian Learning for Airborne Radar.

Space-time adaptive processing (STAP) plays an essential role in clutter suppression and moving target detection in airborne radar systems. The main difficulty is that independent and identically distributed (i.i.d) training samples may not be sufficient to guarantee the performance in the heterogeneous clutter environment. Currently, most sparse recovery/representation (SR) techniques to reduce the requirement of training samples still suffer from high computational complexities. To remedy this problem, a fast group sparse Bayesian learning approach is proposed. Instead of employing all the dictionary atoms, the proposed algorithm identifies the support space of the data and then employs the support space in the sparse Bayesian learning (SBL) algorithm. Moreover, to extend the modified hierarchical model, which can only apply to real-valued signals, the real and imaginary components of the complex-valued signals are treated as two independent real-valued variables. The efficiency of the proposed algorithm is demonstrated both with the simulated and measured data.

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.

Heterogeneous Clutter Suppression via Affine Transformation on Riemannian Manifold of HPD Matrices

Due to a serious shortage of training data, the performance of adaptive clutter suppression suffers remarkable degradation in heterogeneous environments. To address this problem, a novel clutter suppression method via affine transformation on manifolds is proposed. First, training samples in heterogeneous environments are characterized on an established manifold in which the distribution properties are analyzed. Then, a clutter classification scheme is proposed, whereby the KL divergence decision rule is derived to identify the training data as either homogenous or heterogeneous samples. Afterward, based on the distribution properties of samples and the clutter classification scheme, an affine transformation on manifolds is proposed for sample augmentation by transporting heterogeneous samples into the region of homogeneous samples. Finally, the clutter in the area of interest is suppressed on the manifold, which combines the transformed samples with the homogeneous samples, such that superior performance is obtained. Experiments on both simulated and real data validate the superiority of the proposed method in highly heterogeneous environments.

A Statistical Model Based on Modified Generalized-K Distribution for Sea Clutter

Sea clutter magnitude distribution exhibits important guiding significance for the design of marine target detection algorithms and the selection of the constant false alarm rate (CFAR) detection threshold. In this letter, to deal with the limitation of the traditional generalized-K (GK) distribution in highly heterogeneous clutter scene, a modified GK (MGK) distribution is proposed for sea clutter magnitude. By combining the traditional GK and generalized Pareto distributions, the value range of power parameter and the applicable range of the distribution model are extended. The applicability of the proposed distribution model is verified by real-measured sea clutter data.

Geodesic Normal Coordinate-Based Manifold Filtering for Target Detection

Recently, the matrix information geometry (MIG) detector, which characterizes sample data as a Hermitian positive definite (HPD) matrix located on the HPD manifold, was rapidly developed and demonstrated extraordinary performance in numerous applications, especially in heterogeneous clutter backgrounds. In this paper, the geodesic normal coordinate (GNC)-based manifold filter is proposed to improve the detection performance of the MIG detector in strong clutter backgrounds. Using the GNC system, the distribution of target echoes and clutter on the high-dimensional manifold can be visualized and analyzed. Moreover, by exploiting the information concerning the distribution of matrices, the manifold filter is proposed to enhance target echoes and suppress strong clutter. Then, the manifold-filter-based MIG detector is designed, and its superiority is theoretically analyzed. The actual clutter data is utilized to verify the effectiveness of the proposed method. The results show that the proposed manifold filter achieves a signal-to-clutter ratio improvement of more than 5 dB over the existing MIG detectors.

A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions

A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed method is mainly designed to address two issues. The first one is the dilemma between the weak target detection and difficult computation originating from the high dimensions of measurement. The second issue is the suppression of inhomogeneous background clutter and various clutter regions. The extension experiment using synthetic data showcases that no false alarm track would be built in the clutter regions, and the detection rate of close targets exceeds 94%. The experiments using real 3D radar also prove that the method works well in tracking closely maneuvering extended targets even if a clutter region exists.

Method for Robust Ground Ranging Using Monopulse Radar in Heterogeneous Clutter Environment

Aircraft radar has special function which is ranging from aircraft to ground of antenna boresight. Because ranging information is used to calibrate altitude of aircraft or to drop a conventional bomb, the measuring have to be precise and robust. Therefore, we propose a simple and efficient method using monopulse radar for ground ranging. Proposed method calculates balancing weight according to linearity of monopulse ratio and mixes two ranging measurements in proportional to the weight. By exploiting balancing weight, radar is able to react to various environment as monopulse ratio contains characteristics of clutter environment. As a result, robust ranging information can be achieved. We use DEM(Digital Elevation Model) in order to simulate heterogeneous environment. In experimental result, it is shown that proposed method shows better accuracy and precision in any environment.

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

Clutter suppression in heterogeneous environments is a serious challenge for airborne radar. To address this problem, a matrix-manifold-based clutter suppression method is proposed. First, the distributions of training data in heterogeneous environments are analyzed, while the received data are characterized on a Riemannian manifold of Hermitian positive definite matrices. It is indicated that the training data with different distributions with the same power are separated, whereas data with the same distribution are closer together. This implies that the underlying geometry of the data can be better revealed by manifolds than by Euclidean space. Based on these properties, homogeneous training data are selected by establishing a binary hypothesis test such that the negative effects of the use of heterogeneous samples are alleviated. Moreover, as exploiting a geometric metric on manifolds to reveal the underlying information of data, experimental results on both simulated and real data validate that the proposed method has a superior performance with small sample support.

Space-Time Adaptive Processing by Employing Structure-Aware Two-Level Block Sparsity

Traditional radar space-time adaptive processing (STAP) cannot efficiently suppress heterogeneous clutter because of a small number of independent and identically distributed training snapshots. In the article, we propose a new STAP approach exploiting structure-aware two-level block sparsity (STBS) of radar echoes, namely STBS-STAP. It enhances the performance on clutter suppression and target detection with limited training snapshots. The clutter angle-Doppler profile always appears in a continuous diagonal clustering structure and the radar echoes at the adjacent range cells commonly share the same sparse pattern. STBS-STAP employs STBS, i.e., both the diagonal clustering structure and the common sparsity property, to acquire a precise clutter covariance matrix estimation. Thus, the new STBS-STAP achieves better performance on clutter suppression compared with existing STAP methods with a small number of training samples. Besides, STBS-STAP achieves superior target detection performance due to the precise estimation of the statistical properties of the clutter. The superiority of STBS-STAP is verified by experiments on both simulated data and measured Mountain-Top data.

Adaptive Radar Detection in Low-Rank Heterogeneous Clutter via Invariance Theory

This paper addresses adaptive detection of a range distributed target in the presence of dominant heterogeneous clutter, which is (possibly) low-rank and lies in a known subspace, plus Gaussian thermal noise. First, this problem is transformed into an equivalent binary hypothesis test with observations having block-diagonal covariance matrices. Then an invariance analysis is conducted on the resulting hypothesis test. Data and unknown parameters are compressed into a maximal invariant and an induced maximal invariant, respectively, w.r.t. a suitable transformation group. This suggests to focus attention on invariant detectors and to establish the relationship between invariance and constant false alarm rate (CFAR) property. According to this guideline, two tunable invariant detectors exploiting the aforementioned covariance structure are devised, and they are shown to ensure bounded CFAR and standard CFAR properties, respectively. Finally, the CFAR behavior of the proposed detectors as well as their detection performance is assessed via numerical simulations.

Robust CFAR Detector Based on Censored Harmonic Averaging in Heterogeneous Clutter

In this article, a robust constant false alarm rate detector (CFARD) is proposed for dense outlier situations such as the presence of high-density interfering targets within the reference window. Harmonic mean mitigates the impact of large outliers better than the arithmetic mean. We propose a CFARD called censored harmonic averaging (CHA)-CFARD, by using the modified harmonic averaging instead of the common arithmetic averaging in order to estimate the local clutter level. In practice, the number of cells which contain outliers is not known precisely. This poses a major challenge for censoring and truncating methods which aim to eliminate outliers from the reference window. The proposed detector reduces the undesirable effects of strong outliers without this restriction. Simulation results show that the CHA-CFARD outperforms the well-known CFARDs in the presence of interfering targets. Moreover, at the clutter edges, the proposed method shows an acceptable performance. Experimental results with TerraSAR-X images demonstrate that the proposed method is also effective and efficient for ship detection applications in nonhomogeneous sea clutter environments.

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.

Moving target detection with polarimetric distributed MIMO radar in heterogeneous clutter

In this study, the authors consider the problem of moving target detection in the presence of heterogeneous clutter. The knowledge-aided method has been extended to derive the polarimetric multiple-input multiple-output (MIMO) radar detector. And the polarimetric clutter covariance matrix was modelled as a complex inverse Wishart distribution. More precisely, the clutter covariance matrices between different transmitter–receiver (Tx–Rx) pairs are assumed to follow the distribution which shares a prior clutter covariance matrix structure while with different values of the power levels. Under this assumption, the generalised likelihood ratio test (GLRT) approach has been adopted to develop the polarimetric knowledge-aided detector without secondary data. Numerical results are shown to illustrate the effectiveness of the polarimetric knowledge-aided GLRT detector in heterogeneous clutter.

Tracking fluctuating targets in extremely heterogeneous clutter using amplitude information

This study analyses the radar fluctuating target detection in G 0 -distributed clutter using dynamic programming – track before detect (DP-TBD) algorithm. G 0 -distributed clutter is often used to fit the complex circumstances. The authors use chi-square distribution to model the fluctuating targets. The log-likelihood ratio (LLR) is taken as the scoring function of DP-TBD. This study makes contribution to simplify the double integral into single integral in LLR and applies numerical integration with variable resolution to evaluating the LLR. Simulations prove the simplified integral equals the original integral and using LLR in DP-TBD is able to improve the performance of detecting fluctuating targets in G 0 -distributed clutter.

Three‐dimensional sparse recovery space‐time adaptive processing for airborne radar

Conventional 3-dimensional (3D) space-time adaptive processing (STAP) has achieved good performance for non-stationary clutter suppression. However, the performance of 3D STAP rapidly degrades when applied to heterogeneous clutter environment due to the requirement of a large number of independent and identically distributed training samples to estimate the clutter covariance matrix. This study applies an efficient sparse recovery algorithm, i.e. multiple sparse Bayesian learning (MSBL), with tuning, to solve the limited sample problem of 3D STAP in airborne radar. Differing with the conventional 2D sparsity-based STAP, the proposed method utilises the sparsity of clutter in elevation-azimuth-Doppler domain and recovers the 3D clutter spectrum. Whereas in its’ large computational load, a fast algorithm extending the relevance vector machine is also considered.

Data‐dependent reduced‐dimension STAP

Space-time adaptive processing (STAP) is supposed to be a key technique for detection of slow-moving targets in airborne radar applications. To overcome the difficulties of obtaining lots of independent and identical distributed secondary data samples, many sub-optimal STAP algorithms are proposed such as reduced-dimension STAP, reduced-rank STAP, or the sparse-recovery-based STAP. In this study, based on the observation that the STAP filter weight is sparse, a novel clutter-dependent reduced-dimension STAP is proposed. In the proposed approach, the sparse feature of the weight vector in the angular-Doppler domain is utilised to design the reduced-dimension transform matrix. Simulation results show that the output signal-to-interference-plus-noise ratio loss is just 3 dB with only one secondary data sample in the extremely heterogeneous clutter environments.

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.

Robust Radial Velocity Estimation Based on Joint-Pixel Normalized Sample Covariance Matrix and Shift Vector for Moving Targets

The clutter suppression and target radial velocity estimation are essential in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. In reality, the heterogeneous clutter, the image coregistration error, and channel mismatch will remarkably decline the estimation performance of the target radial velocity. To address these issues, a robust radial velocity estimation algorithm is proposed in this letter. Based on the joint-pixel signal model, the joint-pixel normalized sample covariance matrix (JPNSCM) is employed to mitigate the effect of heterogeneous clutter, and the shift vector determined by JPNSCM is used to obtain the actual target steering vector. Then, the adaptive matched filtering algorithm is adopted to estimate the target radial velocity. Compared with traditional estimation algorithms, the proposed method obtains better performance in both simulations and real SAR data experiments.