Nonhomogeneous Clutter Environments Research Articles

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.

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.

Adaptively truncated clutter-statistics-based variability index constant false-alarm-rate detector in SAR imagery

Since sea clutter samples are often contaminated by high-intensity outliers, target detection in nonhomogeneous sea clutter environments is challenging. An adaptively truncated clutter-statistics-based variability index constant false-alarm-rate (TSVI-CFAR) detector of Gaussian clutter in synthetic aperture radar (SAR) imagery is proposed. The proposed method is designed to improve the CFAR detection performance in a heterogeneous multitarget environment. TSVI-CFAR consists of three stages, i.e., clutter truncation, statistical parameter estimation, and CFAR detection. In the clutter truncation stage, the high-intensity outliers in the background window are eliminated through an adaptive threshold-based clutter truncation method. In the statistical parameter estimation stage, the parameters can be accurately estimated through the maximum-likelihood estimator. In the detection stage, the clutter background is categorized using the adaptively truncated clutter. Different types of CFAR detection methods are applied to the pixels under test of the categorized backgrounds. TSVI-CFAR has a higher detection rate and a low observed false alarm rate. The effectiveness of the proposed algorithm is demonstrated using Gaofen-3 SAR data and Envisat-ASAR data.

Knowledge Aided Covariance Matrix Estimation via Gaussian Kernel Function for Airborne SR-STAP

In practical airborne radar, the interference signals in training snapshots usually lead to inaccurate estimation of the clutter covariance matrix (CCM) in space-time adaptive processing (STAP), which seriously degrade radar performance and even occur target self-nulling phenomenon. To solve this problem, a knowledge-aided sparse recovery (SR) STAP algorithm based on Gaussian kernel function is developed. The proposed method distinguishes clutter components and interference signals in training snapshots by the priori knowledge that the clutter components are distributed along the clutter ridge, which dislodges interference signals from training snapshots by Gaussian kernel similar degree. Thus, the CCM is estimated by utilizing these snapshots. Finally, the proposed STAP weight vector is built, which is convenient for the subsequent signal processing. The experimental results were performed to verify the effectiveness and superiority of the proposed method. The test results also show that the proposed algorithm completely removes the interference signals, accurately estimates CCM, and improves the moving target detection performance in small-sample and non-homogeneous clutter environments.

Robust nonhomogeneous training samples detection method for space–time adaptive processing radar using sparse-recovery with knowledge-aided

The performance of space–time adaptive processing (STAP) may degrade significantly when some of the training samples are contaminated by the signal-like components (outliers) in nonhomogeneous clutter environments. To remove the training samples contaminated by outliers in nonhomogeneous clutter environments, a robust nonhomogeneous training samples detection method using the sparse-recovery (SR) with knowledge-aided (KA) is proposed. First, the reduced-dimension (RD) overcomplete spatial–temporal steering dictionary is designed with the prior knowledge of system parameters and the possible target region. Then, the clutter covariance matrix (CCM) of cell under test is efficiently estimated using a modified focal underdetermined system solver (FOCUSS) algorithm, where a RD overcomplete spatial–temporal steering dictionary is applied. Third, the proposed statistics are formed by combining the estimated CCM with the generalized inner products (GIP) method, and the contaminated training samples can be detected and removed. Finally, several simulation results validate the effectiveness of the proposed KA-SR-GIP method.

Non‐parametric detector in non‐homogeneous clutter environments with knowledge‐aided permutation test

In this study, a new kind of knowledge-aided (KA) permutation test (PT) detector is proposed for the problem of performance deterioration of the PT in complex non-homogeneous clutter environments. The proposed detector consists of two parts, a data selector and a conventional PT. The data selector uses the geographic information system (GIS) to select the reference cells which have similar clutter characteristics with the cell under test. Samples from the reference cells selected by the data selector are used to determine the detection threshold of the PT. Because the PT is a non-parametric method, the proposed detector can maintain a constant false alarm rate without knowing the clutter's distribution type, which is very important in complex clutter environments. Considering the computation complexity of the PT, the authors also provide an implementation algorithm for the PT called ‘valid strategy method’, which is more efficient than the direct calculation method. In order to prove the excellent performance of the KA-PT detector, this detector is compared with some conventional detectors in terms of the false-alarm number and detection probability with the real data collected by IPIX radar. The results show that incorporating the non-parametric method with the prior GIS can significantly improve detectors’ performance in practice.

Automatic Censoring CFAR Detector Based on Ordered Data Difference for Low-Flying Helicopter Safety.

Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in nonhomogeneous clutter environments, this paper proposes a new automatic censored cell averaging CFAR detector. The proposed CFAR detector does not require any prior information about the background environment and uses the hypothesis test of the first-order difference (FOD) result of ordered data to reject the unwanted samples in the reference window. After censoring the unwanted ranked cells, the remaining samples are combined to form an estimate of the background power level, thus getting better radar signal detection performance. The simulation results show that the FOD-CFAR detector provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments. Furthermore, the measured results of a low-flying helicopter validate the basic performance of the proposed method.

Fractal detector design and analysis for multi‐input multi‐output radar system

The fractal dimension has been used in the monostatic radar to improve the target detection performance, especially in non-homogeneous clutter environments. In this study, the application of fractal dimension is extended to multi-input multi-output (MIMO) radar. A target detection method for MIMO radar using the fractal dimension measurements is developed. Two types of fractal dimension definitions, i.e. Hausdorf dimension and box-counting dimension are explained and the characteristics of the box-counting dimension are analysed in detail. Then, a fractal dimension estimation-based detector is presented and its performance is analysed for colocated MIMO and statistical MIMO. The simulations results verify the theoretical founding of the proposed target detector for MIMO radar based on the fractal dimension estimation.

A Segmentation-Based CFAR Detection Algorithm Using Truncated Statistics

Target detection in nonhomogeneous sea clutter environments is a complex and challenging task due to the capture effect from interfering outliers and the clutter edge effect from background intensity transitions. For synthetic aperture radar (SAR) measurements, those issues are commonly caused by multiple targets and meteorological and oceanographic phenomena, respectively. This paper proposes a segmentation-based constant false-alarm rate (CFAR) detection algorithm using truncated statistics (TS) for multilooked intensity (MLI) SAR imagery, which simultaneously addresses both issues. From our previous work, TS is a useful tool when the region of interest (ROI) is contaminated by multiple nonclutter pixels. Within each ROI confined by the reference window, the proposed scheme implements an automatic image segmentation algorithm, which performs a finite mixture model estimation with a modified expectation-maximization algorithm. Data truncation is applied here to exclude all possible statistically interfering classes, and sample modeling is based upon the truncated two-parameter gamma model. Next, CFAR detection is conducted pixel by pixel, utilizing the statistical information obtained from the segmentation process within the local reference window. The segmentation-based CFAR detection scheme is examined with real Radarsat-2 MLI SAR imagery. Compared with the conventional CFAR detection approaches, our proposal provides improved background clutter modeling and robust detection performance in nonhomogeneous clutter environments.

Design of LMS Adaptive Radar Detector for Non-homogeneous Interferences

ABSTRACTThe recent years have seen an unprecedented increase in the demand for superior performance of radar signal processor. The main challenge in a radar system is to maintain the probability of a false alarm rate constant even under an uncertain time-varying clutter environment. The objective of this paper is to develop a method for enhanced clutter/interference suppression by redesigning the radar detector in such a way to emphasize the target response and de-emphasize the clutter response. To achieve this, we have proposed a hardware architecture that implements least mean square algorithm-based adaptive filter along with a constant false alarm rate (CFAR) technique for the purpose of better detection of target under a non-homogeneous clutter environment. An adaptive design of the CFAR-filter technique has been used to improve probability of detection by increasing signal-to-noise ratio. The efficacy of the proposed architecture is corroborated with MATLAB simulations and hardware synthesis.

Performance of CA-CFAR detectors in nonhomogeneous positive alpha-stable clutter

This paper studies the performance of a cell-averaging constant false alarm rate (CA-CFAR) receiver that operates in a nonhomogeneous clutter environment with impulsive, heavy-tailed statistics. The clutter distribution is modeled by a positive alpha-stable distribution, which is described by the characteristic exponent α, where 0 < α <1. Analytical expressions for the false alarm and detection probabilities are derived for arbitrary values of α assuming a nonhomogeneous clutter background. The analytical results are validated with computer simulation.

Performance analysis of ordered CFAR detectors for MIMO radars

Ordered statistics is one of the proposed solutions to improve the detection in a multiple target environment. Some variants of this technique have been proposed for the SISO (Single Input Single Output) radars such as the OS-CFAR (Ordered Statistics CFAR), the GOSCA-CFAR (Generalized Ordered Statistics, Cell Averaging CFAR), the OSGO-CFAR (Ordered Statistics Greatest Of CFAR) and the OSSO-CFAR (Ordered Statistics Smallest Of CFAR) to deal with multiple target situations. In this paper, we generalize these CFAR detectors for the MIMO (Multi Input Multi Output) radars with three different schemes. We derive closed-form expressions of the probability of false alarm (Pfa) and the probability of detection (Pd) in a homogeneous environment for two schemes. The performance of these detectors for a non-homogeneous clutter environment (presence of interfering targets and clutter edge) has been assessed and compared. The results obtained showed that the best performance is obtained by the OSSO-CFAR.

Robust non‐homogeneity detection algorithm based on prolate spheroidal wave functions for space‐time adaptive processing

The estimated clutter covariance matrix is always corrupted by the interference target signals (outliers) in non-homogeneous clutter environments, which leads the performance of space-time adaptive processing (STAP) to be degraded significantly for clutter suppression. Therefore a robust non-homogeneity detection algorithm by utilising the prolate spheroidal wave functions (PSWF) is proposed to eliminate the outliers from the training samples set in this study, which can estimate the clutter covariance matrix more accurately for STAP. In the proposed method, the basis vectors of PSWF according to the system parameters are first calculated, which can be computed offline and stored in memory beforehand, and then the corresponding clutter covariance matrix is constructed. In the following, the constructed covariance matrix is combined with the generalised inner products (GIP) method to obtain the corresponding statistics. The training samples contaminated by the outliers are eliminated based on the comparison of the statistics and the designated threshold. By analysing the sensitive coefficients and the simulation results, it is found that the proposed method (PSWF-GIP) can more effectively eliminate the outliers and improve the performance of STAP in non-homogeneous clutter environments.

Statistical Interpretation of a Data Adaptive Clutter Subspace Estimation Algorithm

Detection algorithms in a nonhomogeneous clutter environment have to contend with the problem of excessive false alarm rates when the clutter is not sufficiently suppressed. Prior to signal detection, adaptive receivers must either or whiten the clutter. The former approach requires estimates of the basis vectors of the clutter subspace while the latter requires estimates of the covariance matrix of the clutter. It may be preferable to null the clutter when the dimensionality of the clutter subspace (M) is small in comparison to the length of the data vector coherently processed (N) and also when the signal to be detected is not substantially within the clutter-plus-noise subspace. Depending on the scenario, radar clutter data may have a variety of effects such as clutter discretes, data dropouts due to terrain masking that cause shadowing, multipath effects, and non-Gaussian amplitude statistics. We consider a statistical model which can account for some of these effects and apply the model to design an algorithm to obtain a maximum likelihood estimate (MLE) of the orthonormal basis vectors of the clutter-plus-noise subspace. We show that the problem can be reformulated to develop a recursive approach for MLE based on the expectation-maximization (EM) algorithm. Results of the analysis show that each training vector must be scaled by a nonnegative scale factor from the interval [0,1] prior to subspace estimation. The scale factor applied to each training vector increases monotonically with the subspace clutter-plus-noise power of the vector. Conditions for the existence and uniqueness of the solution are considered. A statistical interpretation of the solution is provided along with sample simulation results.

Moving Target Detection Using Distributed MIMO Radar in Clutter With Nonhomogeneous Power

In this paper, we consider moving target detection using a distributed multiple-input multiple-output (MIMO) radar on stationary platforms in nonhomogeneous clutter environments. Our study is motivated by the fact that the multistatic transmit-receive configuration in a distributed MIMO radar causes nonstationary clutter. Specifically, the clutter power for the same test cell may vary significantly from one transmit-receive pair to another, due to azimuth-selective backscattering of the clutter. To account for these issues, a new nonhomogeneous clutter model, where the clutter resides in a low-rank subspace with different subspace coefficients (and hence different clutter power) for different transmit-receive pair, is introduced and the relation to a general clutter model is discussed. Following the proposed clutter model, we develop a generalized-likelihood ratio test (GLRT) for moving target detection in distributed MIMO radar. The GLRT is shown to be a constant false alarm rate (CFAR) detector, and the test statistic is a central and noncentral Beta variable under the null and alternative hypotheses, respectively. Simulations are provided to demonstrate the performance of the proposed GLRT in comparison with several existing techniques.

D3-STMB Hybrid STAP Algorithm for Discrete Interference Suppression in Nonhomogeneous Clutter

This paper creates a new hybrid Space-Time Adaptive Processing (STAP) algorithm that combines Direct Data Domain (D3) method and Space-Time Multiple-Beam (STMB) algorithm, which can effectively suppress discrete interference in the nonhomogeneous clutter environment. In the proposed hybrid algorithm, the D3 method is applied to process the discrete interference in the primary range cell, and the residual clutter is suppressed by the STMB algorithm. The performance of the proposed hybrid STAP algorithm is demonstrated in a simulation.

Analysis of space–time adaptive processing performance using K-means clustering algorithm for normalisation method in non-homogeneity detector process

This study describes the performance analysis of the non-homogeneity detector (NHD) with various normalisation methods for the space–time adaptive processing (STAP) of airborne radar signals under the non-homogeneous clutter environments. The authors can calculate a threshold value from the statistical analysis of generalised inner product (GIP) using the normalisation method using mean, median and the K-means clustering algorithm of training data snapshots in the NHD process. The selected homogeneous data using the threshold value are used to recalculate covariance matrix of the total interference. To evaluate the performance of the covariance matrix, the authors calculated the eigenspectra and signal to interference noise ratio (SINR) loss. The accuracy of the recalculated covariance matrix is verified by the modified sample matrix inversion (MSMI) test statistic for the target detection. Projection statistics (PS) based on GIP is also used to compare the performance of detecting single and multiple targets. The authors’ simulation results demonstrate that the K-means clustering algorithm as a normalisation method for both GIP and GIP-based PS can improve the STAP performance in the severe non-homogeneous clutter environment even under the multiple targets scenarios, compared to the other normalisation methods.

Hybrid direct data domain sigma-delta space–time adaptive processing algorithm in non-homogeneous clutter

The need to deal with non-homogeneous clutter has driven much of the recent research in space–time adaptive processing (STAP). An extension of the low-complexity, sigma-delta (ΣΔ) algorithm incorporating the direct data domain (D3) processing is presented. The new algorithm is practical and improves target detection in non-homogeneous clutter environments. The algorithm employs a hybrid approach, combining D3 processing with the more traditional statistical approach, thereby obtaining advantages of both. First, a modified D3 algorithm, which maximises signal-to-interference-plus-noise ratio (SINR), is presented. Then this D3 algorithm is used as an adaptive transformer to create sum (Σ) and difference (Δ) beams. The residual interference after the D3 processing is further cancelled by ΣΔ STAP. The proposed hybrid algorithm using D3-ΣΔ STAP is tested in non-homogeneous clutter modelled using spherically invariant random variables (SIRV) and artificially injected discrete interferers. Performance of the proposed methods is compared with those of traditional statistical approaches, illustrating significant benefits of hybrid processing in non-homogeneous scenarios.

Performance analysis of some CFAR detectors in homogeneous and non-homogeneous Pearson-distributed clutter

In this paper, we analyse the performance of the Greatest Of-Constant False Alarm Rate (GO-CFAR) and the Smallest Of (SO)-CFAR detectors in the presence of clutter environments whose amplitude statistics are modelled by the Pearson V distribution and where the clutter dominates the receiver noise. The performance of these detectors are evaluated both in homogeneous and non-homogeneous clutter. The non-homogeneity is modelled as a step function discontinuity in the reference window. The target in the test cell is assumed to fluctuate according to the Swerling I model. We derive closed form expressions for the probability of false alarm (Pfa) and the probability of detection (Pd) in homogeneous and non-homogeneous Pearson V distributed clutter. The comparison of the two detectors for a non-homogeneous clutter environment showed that the best false alarm rate performance at clutter boundary is obtained for the SO-CFAR detector when the test cell is in low level clutter, while the GO-CFAR detector exhibits better performance when the test cell is from the higher level clutter as expected.