Effective Clutter Suppression Research Articles

Sea clutter suppression using neural network

Sea clutter suppression is a critical task in radar systems to enhance target detection performance in complex naval environments and at coastlines. This paper discusses the use of neural networks for marine clutter suppression and coastal surveillance radar clutter suppression. Effective maritime clutter suppression is made possible by the Feed Forward Neural Network (FFNN) and Principal Component Analysis (PCA) based clutter reduction method, which uses neural network deep learning capabilities to automatically identify and extract features and patterns from raw radar data. Support Vector Machine (SVM) is utilized for clutter suppression along the shoreline. To train and test the network model, a sizable collection of radar measurements, including clutter and target echoes, is gathered. After pre-processing, the gathered data is used in a specially created model, which uses its underlying patterns to distinguish between target echoes and clutter. Then, clutter in real-time radar signals is suppressed using the learned neural network models, improving the detection of targets on the sea and at the coastline. Performance measures Structural Similarity (SSIM) and Signal to Noise Ratio (SNR) shows that the proposed method provides improved clutter reduction. Highlights Maritime clutter suppression is required for better visibility of targets Neural Networks provides better clutter suppression Clutter reduction using PPI images helps in Surveillance applications Objective performance measures SNR and SSI are used for performance analysis Better training and good dataset helps in improved clutter reduction for neural network approaches

A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding

Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar.

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter’s Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme’s performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression.

Effective Non-Stationary Clutter Suppression Method via Elevation Oblique Subspace Projection for Moving Targets Detection with a Space-Based Surveillance Radar

The clutter becomes non-stationary for a space-based surveillance radar (SBSR), which is harmful for the moving targets detection due to the earth’s rotation. The non-stationarity will degrade the accuracy of clutter covariance matrix (CCM) estimation and increase the clutter degree of freedom (DOF), thereby degrading the performance of clutter suppression. To solve this problem, this paper proposes a novel non-stationary clutter suppression method using an elevation oblique subspace projection method. After analyzing the range ambiguity and non-stationarity of the clutter, the proposed method utilized the oblique projection matrix to project the signal onto the subspace spanned by the near-range and far-range clutter components along the subspace spanned by the main lobe clutter component. Then, the projected signal was used to estimate the elevation covariance matrix and calculate the optimal weight vector for the elevation adaptive filter. The proposed method can suppress the non-stationary clutter effectively with a higher improvement factor (IF) and a narrower main lobe width. Finally, the simulation results were given to verify the correctness and effectiveness of the proposed method.

LEO-Based Satellite Constellation for Moving Target Detection

With the rapid development of cooperative detection technology, target fusion detection with regard of LEO satellites can be realized by means of their diverse observation configurations. However, the existing constant false alarm ratio (CFAR) detection research rarely involves the space-based target fusion detection theory. In this paper, a novel multi-source fusion detection method based on LEO satellites is presented. Firstly, the pre-compensation function is constructed by employing the range and Doppler history of the cell where the antenna beam center is pointed. As a result, not only is the Doppler band broadening problem caused by the high-speed movement of the satellite platform, but the Doppler frequency rate (DFR) offset issue resulted from different observation configurations are alleviated synchronously. Then, the theoretical upper and lower limits of DFR are designed to achieve the effective clutter suppression and the accurate target echo fusion. Finally, the CFAR detection threshold based on the exponential weighted likelihood ratio is derived, which effectively increases the contrast ratio between the target cell and other background cells, and thus to provide an effective multi-source fusion detection method for LEO-based satellite constellation. Simulation results verify the effectiveness of the proposed algorithm.

An Effective Clutter Suppression Approach Based on Null-Space Technique for the Space-Borne Multichannel in Azimuth High-Resolution and Wide-Swath SAR System

In this article, an effective clutter suppression algorithm is presented for the space-borne azimuth multiantenna high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, which is based on the null-space technique. First, the echo of bistatic geosynchronous-low earth orbit (GEO-LEO) azimuth multiantenna HRWS SAR-ground moving target indication (GMTI) system is utilized to deduce the coarse-focused image of moving targets and clutter, where the Chirp Fourier transform (CFT) in azimuth is involved. Then, the matrix form can be utilized to describe the coarse-focused image of the multiantenna SAR system and the corresponding covariance matrix can be estimated. After that, the null-space is constructed using the covariance matrix corresponding to clutter, where at least a redundant channel freedom is required. Since the null-space vector is orthogonal to signal-space vector, it can be used to suppress the clutter. As an equivalent phase is brought by the slant velocity, the moving targets’ echo can be preserved during clutter suppression. Then, the optimization and suboptimization vectors for clutter suppression are introduced. It is worth noting that the proposed clutter suppression algorithm is robust for the antenna mismatch, that is, the antenna mismatch in phase and the corresponding position error. Finally, the theoretical investigations are validated using some simulation experiments, where the experiments for bistatic GEO-LEO and single-platform azimuth multiantenna HRWS SAR-GMTI system are both included. In addition, the real measured single-platform azimuth multiantenna HRWS SAR data experiments are also performed.

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

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

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

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

A Knowledge-Based Auxiliary Channel STAP for Target Detection in Shipborne HFSWR

The broadened first-order sea clutter in shipborne high frequency surface wave radar (HFSWR), which will mask the targets with low radial velocity, is a kind of classical space–time coupled clutter. Space–time adaptive processing (STAP) has been proven to be an effective clutter suppression algorithm for space-time coupled clutter. To further improve the efficiency of clutter suppression, a STAP method based on a generalized sidelobe canceller (GSC) structure, named as the auxiliary channel STAP, was introduced into shipborne HFSWR. To obtain precise clutter information for the clutter covariance matrix (CCM) estimation, an approach based on the prior knowledge to auxiliary channel selection is proposed. Auxiliary channels are selected along the clutter ridge of the first-order sea clutter, whose distribution can be determined by the system parameters and regarded as pre-knowledge. To deal with the heterogeneity of the spreading first-order sea clutter, an innovative training samples selection approach according to the Riemannian distance is presented. The range cells that had shorter Riemannian distances to the cell under test (CUT) were chosen as training samples. Experimental results with measured data verified the effectiveness of the proposed algorithm, and the comparison with the existing clutter suppression algorithms showed the superiority of the algorithm.

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.

Ground Moving Target Indication for the Geosynchronous-Low Earth Orbit Bistatic Multichannel SAR System

In this article, an effective clutter suppression and moving target imaging approach is proposed for the geosynchronous-low earth orbit (GEO-LEO) bistatic multichannel synthetic aperture radar (SAR) system, which is robust for the fast moving target with Doppler centroid ambiguity. For the GEO-LEO bistatic multichannel SAR system, the characteristic of baseline with azimuth invariant in Doppler Fourier transform domain is not tenable. It is difficult to implement clutter suppression by the unified baseline compensation in azimuth Doppler Fourier transform domain. Fortunately, we discover that the baseline can be approximately regarded as a constant in chirp Fourier transform domain for the GEO-LEO bistatic multichannel SAR system. Hence, the corresponding baseline compensation can be achieved in the azimuth chirp Fourier transform domain for the clutter. With an orthogonality vector of clutter, the clutter can be well suppressed and the moving target is extracted. After that, the moving target imaging approach is developed with the baseband Doppler centroid estimation and Doppler ambiguity number estimation. Finally, the theoretical investigations and the proposed approach in this article are validated by some experiments, where the experiments for clutter suppression and experiments for fast moving targets are included. Especially, an experiment with real SAR scattering scene is involved. In addition, some discussions for blind velocity targets are presented.

Dual-Cross-Polarized GPR Measurement Method for Detection and Orientation Estimation of Shallowly Buried Elongated Object

Detecting a shallowly buried and elongated object and estimating its orientation using a commonly adopted co-polarized GPR system is challenging due to the presence of strong ground clutter that masks the target reflection. A cross-polarized configuration can be used to suppress ground clutter and reveal the object reflection, but it suffers from inconsistent detection capability which significantly varies with different object orientations. To address this issue, we propose a dual-cross-polarized detection (DCPD) method which utilizes two cross-polarized antennas with a special arrangement to detect the object. The signals reflected by the object and collected by the two antennas are combined in a rotationally invariant manner to ensure both effective ground clutter suppression and consistent detection irrespective of the object orientation. In addition, we present a dual-cross-polarized orientation estimation (DCPOE) algorithm to estimate the object orientation from the two cross-polarized data. The proposed DCPOE algorithm is less affected by environmental noise and performs robust and accurate azimuth angle estimation. The effectiveness of the proposed techniques in the detection and orientation estimation and their advantages over the existing method have been demonstrated using experimental data. Comparison results show that the maximum and average errors are 22.3{\deg} and 10.9{\deg} for the Alford rotation algorithm, while those are 4.9{\deg} and 1.8{\deg} for the proposed DCPOE algorithm in the demonstrated shallowly buried object cases. The proposed techniques can be unified in a framework to facilitate the investigation and mapping of shallowly buried and elongated targets.

Passive Radar DPCA Schemes With Adaptive Channel Calibration

This article addresses the problem of direct signal interference (DSI) and clutter cancellation for passive radar systems on moving platforms employing displaced phase centre antenna (DPCA) approach. Attention is focused on the development of signal processing strategies able to compensate for the limitations deriving from amplitude and phase imbalances that affect the two channels employed on receive. First, we show that using the signal received from the illuminator of opportunity as a source for channels calibration might be ineffective when DSI and clutter echoes have different directions of arrival, due to the effect of angle-dependent channel imbalance. Then, a two-stage strategy is proposed, consisting of a preliminary DSI removal stage at each receive channel, followed by a clutter-based calibration approach that basically enables an effective DPCA clutter suppression. Different strategies for channel calibration are proposed, aimed at compensating for potential angle and range-dependent channel errors, based on the maximization of the cancellation performance. Effectiveness of this scheme is shown against the experimental data from a DVB-T based moving passive radar, in the presence of both real and synthetic moving targets.

Sea Clutter Cancellation for Passive Radar Sensor Exploiting Multi-Channel Adaptive Filters

Sea clutter suppression in passive radar sensor is a challenging problem because the Doppler frequencies of low-velocity sea-surface targets are typically close to the spectrum of the sea clutter. Conventional approaches based on single-channel high-pass filters are effective for clutter suppression only when the clutter is concentrated in low Doppler region. For sea clutter that has a spread spectrum, however, these approaches have to compromise target signal reception. That is, they either form a narrow notch which does not effectively suppress clutter, or generate a broadened null that simultaneously mitigates low-velocity target signals. Therefore, it is desirable to design a filter that forms a notch broad enough to cover the entire clutter spectrum, with the frequency response rising sharply to a high gain outside the clutter band. Toward this end, in this paper, we develop a generalized multi-channel adaptive filter which, by forming multiple sharp notches over a set of discrete frequencies within the clutter spectrum, achieves effective clutter suppression and target signal preservation. We focus on the fast frequency-domain implementation, and the performance analysis in terms of the frequency response, signal energy loss, and computational complexities is also presented. The effectiveness of the proposed approaches is verified using real-data results.

A Clutter-Analysis-Based STAP for Moving FOD Detection on Runways.

Security risks and economic losses of civil aviation caused by Foreign Object Debris (FOD) have increased rapidly. Synthetic Aperture Radars (SARs) with high resolutions potentially have the capability to detect FODs on the runways, but the target echo is hard to be distinguished from strong clutter. This paper proposes a clutter-analysis-based Space-time Adaptive Processing (STAP) method in order to obtain effective clutter suppression and moving FOD indication, under inhomogeneous clutter background. Specifically, we first divide the radar coverage into equal scattering cells in the rectangular coordinates system rather than the polar ones. We then measure normalized RCSs within the X-band and employ the acquired results to modify the parameters of traditional models. Finally, we describe the clutter expressions as responses of the scattering cells in space and time domain to obtain the theoretical clutter covariance. Experimental results at 10 GHz show that FODs with a reflection higher than −30 dBsm can be effectively detected by a Linear Constraint Minimum Variance (LCMV) filter in azimuth when the noise is −60 dBm. It is also validated to indicate a −40 dBsm target in Doppler. Our approach can obtain effective clutter suppression 60dB deeper than the training-sample-coupled STAP under the same conditions.

Periodic clutter suppression in iron road structures for automotive radar systems

In this study, the authors propose a periodic clutter suppression method in iron road structures for automotive radar systems. Specific road structures made of iron such as tunnels, soundproof walls, and guardrails have periodic steel frames that generate unwanted reflected signals called radar clutter. This clutter degrades the detection performance of an automotive radar and leads to misdetection of targets. Therefore, efficient signal processing is required to mitigate the adverse effect of clutter within those iron structures. Since the spacings between steel frames are uniform, beat frequencies corresponding to them also appear at regular intervals. This phenomenon is maintained in each radar scan when a vehicle travels in the iron structures. Thus, they suggest an effective periodic clutter suppression method using the relationship between adjacent radar scans. Their proposed clutter suppression scheme was applied to radar signals obtained from the iron road structures and successfully suppressed the periodic clutter.

Adaptive Radon–Fourier Transform for Weak Radar Target Detection

The Radon–Fourier transform (RFT) with a long coherent integration time has recently been proposed for detecting a moving target with an across range cell (ARC) effect. However, without effective clutter suppression, clutter will also be integrated via the RFT, which may affect weak target detection. Based on the maximal signal-to-clutter-plus-noise ratio (SCNR) criteria, a novel adaptive RFT (ARFT) is proposed in this paper to effectively detect a “low-observable” target in a clutter background. The proposed ARFT can combine RFT and adaptive clutter suppression by introducing an optimal filter weight, which is determined from the clutter's covariance matrix as well as a steering vector for a moving target with the ARC effect. In the transformed range-velocity space, the proposed ARFT can suppress background clutter and optimally integrate the target's energy. Nevertheless, with the increase in the integration time, the ARFT needs to address two difficulties in its real implementation. One is the lack of independently and identically distributed (i.i.d.) training samples in a heterogeneous clutter background, and the other is that the computational complexity is too high due to the large number of pulse samples. Therefore, a subaperture ARFT (SA-ARFT) is further proposed in this paper. It divides all coherent pulse samples into several subapertures and accomplishes adaptive clutter suppression in each subaperture. Subsequently, SA-ARFT implements coherent integration among the outputs of different subapertures. The proposed SA-ARFT method can obtain a similar SCNR improvement factor (SCNR IF) with a large number of i.i.d. training samples, while it can obtain a much higher SCNR IF than the ARFT with limited i.i.d. training samples and much lower computational complexity in a heterogeneous clutter background. Finally, some numerical results are provided to demonstrate the effectiveness of the two proposed methods.

Wall Clutter Mitigation Using Discrete Prolate Spheroidal Sequences for Sparse Reconstruction of Indoor Stationary Scenes

Detection and localization of stationary targets behind walls is primarily challenged by the presence of the overwhelming electromagnetic signature of the front wall in the radar returns. In this paper, we use the discrete prolate spheroidal sequences to represent spatially extended stationary targets, including exterior walls. This permits the formation of a linear block sparse model relating the range profile and observation vectors. Effective wall clutter suppression can then be performed prior to sparse signal image reconstruction. We consider stepped-frequency radar with two cases of frequency measurement distributions over antenna positions. In the first case, the same subset of frequencies is used for each antenna in physical or synthetic aperture arrays, whereas the other case allows different sets of few frequency observations to be available at different antennas. Using experimental data, we demonstrate that the proposed scheme enables sparsity-based image reconstruction techniques to effectively detect and localize behind-the-wall stationary targets from reduced measurements.

Spectrum Compression Space–Time Adaptive Processing for TOPS SAR System

A multichannel terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) system is capa- ble of imaging a wider swath with a higher azimuth resolution for improved moving target detection. For TOPS SAR, due to antenna beam steering, the azimuth bandwidth of background clutter is much larger than the instantaneous signal bandwidth. To overcome this problem, a method referred to as spectrum compression space-time adaptive processing (SC-STAP) is pro- posed in this letter. Through the SC process, both the Doppler spectrum and the spatial spectrum of the background clutter are simultaneously compressed. This key step achieves fully over- lapped clutter space-time spectrum lines and, as such, enables effective clutter suppression and target signal alias compensation by applying linearly constrained STAP. Furthermore, in order to avoid the target ambiguities arising from the spectral wrapping, an approach based on deramp processing is proposed to focus the moving targets for TOPS SAR mode. Simulation results validate the effectiveness of the proposed algorithm. Index Terms—Moving target, multichannel, space-time adap- tive processing (STAP), spectrum compression (SC), terrain ob- servation by progressive scans (TOPS) synthetic aperture radar (SAR).

Deramp Space–Time Adaptive Processing for Multichannel SAR Systems

This letter proposes a novel deramp space-time adaptive processing (Deramp-STAP) method for synthetic aperture radar (SAR) systems to achieve effective clutter suppression. Compared with the traditional STAP, the proposed method can overcome the spectral wrapping problem of a moving target from a Doppler shift. Furthermore, in the case of signal undersampling, the ambiguously focused position in azimuth for a moving target can be avoided by the proposed method. Moreover, the computational complexity can be drastically reduced because we only need to consider the baseband velocity of the moving target; here, the range of the baseband velocity is much smaller than that of the real target velocity, for clutter suppression in this method. Simulation results validate the effectiveness of the proposed algorithm.