Clutter Suppression Algorithm Research Articles

Water surface-clutter suppression method based on infrared polarization information.

Targeting star-like water surface clutter, a clutter suppression method based on infrared polarization information is proposed. First, the clutter is suppressed from a global perspective using infrared polarization imaging technology, and a basic clutter-suppressed image is obtained. Then, using the Reed-Xiaoli anomaly detection algorithm, the remaining clutter positions in the basic image are determined from the polarization intensity image and basic image. Finally, an image filtering algorithm is utilized to further suppress the remaining clutter in the basic image. In experiments, the proposed method can not only improve the signal-to-clutter ratio as much as 152%, but also preserve the target information and background texture features effectively, indicating clear superiority of our method over existing clutter suppression algorithms. Clutter suppression and target detail preservation can enhance observer understanding of a scene significantly, so this method is applied to the detection and recognition of targets on the water surface.

Research on Strong Clutter Suppression for Gaofen-3 Dual-Channel SAR/GMTI.

In spaceborne synthetic aperture radar (SAR), moving targets are almost buried in ground clutter due to the wide clutter Doppler spectrum and the restricted pulse repetition frequency (PRF), which increases the difficulty of moving target detection. Clutter suppression is one of the key issues in the spaceborne SAR moving target indicator operation. In this paper, we describe the clutter suppression principle and analyze the influence of amplitude and phase error on clutter suppression. In the following, a novel dual-channel SAR clutter suppression algorithm is proposed, which is suitable for the Gaofen-3(GF-3) SAR sensor. The proposed algorithm consists of three technique steps, namely adaptive two-dimensional (2D) channel calibration, refined amplitude error correction and refined phase error correction. After channel error is corrected by these procedures, the clutter component, especially a strong clutter component, can be well suppressed. The validity of the proposed algorithm is verified by GF-3 SAR real data which demonstrates the ground moving-target indication (GMTI) capability of GF-3 SAR sensor.

Knowledge-Based Generalized Side-Lobe Canceller for Ionospheric Clutter Suppression in HFSWR

High frequency surface wave radar (HFSWR) has been successfully developed for early warning and remote sensing. However, the ionospheric clutter is a difficult challenge that can make HFSWR system inefficient. The Generalized Side-lobe Canceller (GSC) has been proved to be an effective algorithm for clutter suppression in theory, but it suffers from the performance degradation for some non-ideal conditions in practice. The most intolerable shortcoming is the signal to noise ratio (SNR) loss caused by the residual signal in the secondary data. In this paper, a knowledge-based GSC (KB-GSC) method has been proposed via an adaptive single notch filter design to reject the residual signal for reducing the SNR loss. The feasibility and availability has been demonstrated by measured data.

Radio and Clutter Suppression Algorithm Based on Spatial Multiple Beams for Small-Aperture HFSWR

Radio and clutter that cover a certain number of range-Doppler-angle cells have a major impact on the detection performance of a high-frequency surface wave radar (HFSWR) system. Especially for a small-aperture array, the angle spectrums of radio and clutter suffer from severe broadening, so the targets that are more easily submerged in the broadened radio and clutter can be hardly detected. To tackle this issue, this paper proposes an algorithm for radio decontamination and clutter suppression to enable detection of the submerged targets. First, the spatial correlation of the array is derived, and the characteristics of radio and clutter are analyzed based on angle-Doppler joint eigenvector. Then owing to the analysis, the information of radio and clutter in the main beam can be accurately estimated from that in the auxiliary beams and eliminated by subtracting it. The results of simulations and measured data indicate that the proposed method offers a significant performance improvement and has a strong robustness against the array amplitude-phase errors.

Sparsity‐based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar

To improve the performance of the recently developed parameter-dependent sparse recovery (SR) space-time adaptive processing (STAP) algorithms in real-world applications, the authors propose a novel clutter suppression algorithm with multiple measurement vectors (MMVs) using sparse Bayesian learning (SBL) strategy. First, the necessary and sufficient condition for uniqueness of sparse solutions to the SR STAP with MMV is derived. Then the SBL STAP algorithm in MMV case is introduced, and the process for hyperparameters estimation via expectation maximisation is given. Finally, a computational complexity comparison with the existing algorithms and an analysis of the proposed algorithm are conducted. Results with both simulated and the Mountain-Top data demonstrate the fast convergence and good performance of the proposed algorithm.

Image-Based Target Detection and Radial Velocity Estimation Methods for Multichannel SAR-GMTI

In order to enhance the performance of spaceborne synthetic aperture radar–ground moving target indication (SAR-GMTI) systems, multichannel systems with large and preferably nonuniform baselines are required. In this paper, SAR-GMTI algorithms for multichannel SAR systems, which we call multichannel displaced phase center antenna (DPCA), multichannel along track interferometry (ATI), and multichannel DPCA-ATI, are presented. Multichannel DPCA is a deterministic algorithm for clutter and azimuth ambiguity suppression. It successfully suppresses not only uniform azimuth ambiguities but also nonuniform isolated ones, since it does not require uniform clutter covariance assumption as adaptive algorithms do. Multichannel ATI and multichannel DPCA-ATI are the algorithms for target radial velocity estimation. Both of them reduce the target radial velocity ambiguities, which arise with the long baseline systems, by exploiting the multiple receive channel signals. And multichannel DPCA-ATI further achieves robust performance to clutter influence by suppressing the clutter and the azimuth ambiguity in advance. The performances of the proposed algorithms are shown through airborne Ku-band three-channel SAR experiments. It is shown that the multichannel DPCA suppresses strong azimuth ambiguity up to more than 20 dB, and the accuracy of the radial velocity estimation of the multichannel DPCA-ATI is on the order of 0.1 m/s. Furthermore, statistical performance analysis is presented to discuss the potential performance on the spaceborne system.

Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar

Adapting the space-time adaptive processing (STAP) filter with finite number of secondary data is of particular interest for airborne phased-array radar clutter suppression. Sparse representation (SR) technique has been introduced into the STAP framework for the benefit of drastically reduced training requirement. However, most SR algorithms need the fine tuning of one or more user parameters, which affect the final results significantly. Sparse Bayesian learning (SBL) and multiple sparse Bayesian learning (M-SBL) are robust and user parameter free approaches, but they converge quite slowly. To remedy this limitation, a fast converging SBL (FCSBL) approach is proposed based on Bayesian inference along with a simple approximation term, then, it is extended to the multiple measurement vector case, and the resulting approach is termed as M-FCSBL. To improve the performance of STAP in finite secondary data situation, the M-FCSBL is utilized to estimate the clutter plus noise covariance matrix (CCM) from a limited number of secondary data, and then the resulting CCM is adopted to devise the STAP filter and suppress the clutter. Numerical experiments with both simulated and Mountain-Top data are carried out. It is shown that the proposed algorithm has superior clutter suppression performance in finite secondary data situation.

Optimal waveform-based clutter suppression algorithm for recursive synthetic aperture radar imaging systems

A computational method for suppressing clutter and generating clear microwave images of targets is proposed in this paper, which combines synthetic aperture radar (SAR) principles with recursive method and waveform design theory, and it is suitable for SAR for special applications. The nonlinear recursive model is introduced into the SAR operation principle, and the cubature Kalman filter algorithm is used to estimate target and clutter responses in each azimuth position based on their previous states, which are both assumed to be Gaussian distributions. NP criteria-based optimal waveforms are designed repeatedly as the sensor flies along its azimuth path and are used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. Thus, with fewer disturbances from the clutter response, we can generate the SAR image with traditional azimuth matched filters. Our simulations show that the clutter suppression filter significantly reduces the clutter response, and our algorithm greatly improves the SINR of the SAR image based on different clutter suppression filter parameters. As such, this algorithm may be preferable for special target imaging when prior information on the target is available.

In-Wall Clutter Suppression Based on Low-Rank and Sparse Representation for Through-the-Wall Radar

For through-the-wall-radar signal processing, there exist extensive studies on removing the wall surface reflection signal, while how to eliminate/alleviate the in-wall structure reflection is not well addressed. In many building structures, a layer of reinforced steel bars and utility pipes exist inside the wall which can cause strong clutter to overwhelmingly mask the reflection signal from the targets under test behind the wall. Such clutter cannot be mitigated using the conventional wall clutter removal methods. Thus, a new effective technique to remove the strong inside-wall rebar or pipe reflection is indispensable. Considering the correlated features of the in-wall rebar or pipes and the spatial sparsity of the behind-wall targets under test, a low-rank and sparse representation model-based in-wall clutter suppression algorithm is developed in this letter for target feature enhancement and detection. Experiments on both simulation data and field test data are performed for performance evaluation and validation.

Multi‐mode clutter suppression of multiple‐input–multiple‐output over‐the‐horizon radar based on blind source separation

Spread-Doppler clutter caused by multi-mode propagation seriously affects the over-the-horizon radar detection performance for slow ships. To solve this problem, the authors propose to suppress multi-mode clutter using spatial information in the multiple-input–multiple-output over-the-horizon radar system. Traditional processing algorithms need prior information of the target direction. However, it is often not readily available. In this study, they propose a novel multi-mode clutter suppression algorithm based on the second-order blind identification (SOBI) technique. The proposed algorithm does not need prior information of the target direction and can effectively suppress the multi-mode clutter. Considering that the SOBI algorithm affected by the similarity in sources spectra, they improve the proposed algorithm using the spatial smoothing technique. Simulation results show that, in the single target scene, the improved algorithm can effectively eliminate the spectrum burrs caused by incomplete separation, making separated signals smoother. Signal-to-clutter-noise ratio is greatly improved. The good multi-mode clutter suppression performance is obtained. In the multi-mode scenario that double targets with the same radial velocity, that is, sources spectra have similarities, the algorithm based on SOBI cannot separate all targets. Although the improved algorithm can make all targets visible and effectively suppresses the multi-mode clutter.

Radar signatures of furniture elements

Through-the-wall radar (TTWR) systems designed for detecting, locating, and tracking humans are plagued by harsh clutter scenarios caused by the variety and abundance of furniture elements within the antenna’s field of view. This paper investigates the signatures of indoor clutter using radar cross section (RCS) and linear depolarization ratios to implement clutter suppression algorithms. The paper focuses on the analysis of common individual and composite pieces of indoor clutter for TTWR. The characterization of the wideband spectral properties of indoor clutter elements is accomplished using finite-difference time-domain (FDTD) techniques. Using FDTD, the spectral characteristics of clutter elements are obtained over a wide range of frequencies, different polarizations, and aspect angles.We compare and contrast the RCS responses between the different objects for analysis and use in TTWR. RCS results obtained from the simulations are compared to experimental data using a network analyzer and reasonable agreement is obtained. Clutter signatures are also compared to those of humans in order to design appropriate waveforms for maximizing the signal-to-clutter ratio in TTWR systems.

Robust fast maximum likelihood with assumed clutter covariance algorithm for adaptive clutter suppression

The mismatch between the clutter and noise power of the prior knowledge and true interference covariance matrix degrades the performance of fast maximum likelihood with assumed clutter covariance (FMLACC) algorithm significantly. By introducing a scale parameter to flexibly adjust the prior power, the authors propose an algorithm which is more robust to the power mismatch than FMLACC algorithm. They also develop a more straightforward method to derive the maximum likelihood covariance matrix estimator under this scaled knowledge constraint. Moreover, they study the problem of automatically determining the scale parameter. The authors provide two parameter selection methods, the first of which is based on estimating the minimum eigenvalue of the prewhitened sample covariance matrix, and the second is based on cross validation. To reduce the computational complexity, they also develop fast implementations for the parameter selection based on cross validation. Numerical simulations demonstrate the performance enhancement of the proposed algorithm compared with FMLACC algorithm in cases of mismatched prior knowledge.

A Clutter Suppression Algorithm for GPR Data Based on PCA Combining with Gradient Magnitude

Ground penetrating radar (GPR) is a powerful tool for detecting defects behind reinforced concrete (RC) structures. However, the received data from GPR includes a large number of clutters which are easy overwhelming the signal of target. In order to successfully extract the target signature, these clutters effects need to be eliminated. In this article, a clutter suppression algorithm based on Principal Component Analysis (PCA) combining with gradient magnitude is presented. PCA clutter suppression algorithm is applied to the data and removes most of the echoes from ground surface and portion of other clutters with weak energy. Then gradient magnitude clutter suppression is used to remove majority of the residue clutters. It is demonstrated from simulation that the proposed algorithm is able to significantly suppress the clutters and is superior to the PCA clutter suppression, magnitude clutter suppression and means subtraction method.

Dimension-Reduced Space-Time Adaptive Clutter Suppression Algorithm Based on Lower-Rank Approximation to Weight Matrix in Airborne Radar

In this paper, we address the dimension-reduced space-time adaptive processing (STAP) techniques for ground clutter suppression in airborne radar from the viewpoint of approximation theory. The weights in the optimum STAP technique can be naturally expressible as the weight matrix. An efficient dimension-reduced space-time adaptive clutter suppression (STACS) algorithm based on lower-rank approximation to weight matrix is established, which finds a set of space-time separable filters to approximate the optimum STAP processor. By exploiting a lower-rank approximation to weight matrix, we make the quadratic cost function used in the classical optimum STAP processor be converted into a biquadratic cost function. To seek a minimum point of the biquadratic cost function, this paper develops an efficient multistage bi-iterative algorithm and the corresponding multistage dimension-reduced technique with a modular structure, where each stage finds an orthogonal component for approximating to the weight matrix. The effectiveness of the STACS algorithm is tested via several experiments.

基于波原子变换的红外复杂背景杂波抑制算法

基于波原子变换的红外复杂背景杂波抑制算法

Research of Sea Clutter Suppression Algorithm Based on GPU

This paper presents an algorithm of sea clutter suppression using graphics processing unit (GPU) to meet the real-time requirement in the general radar terminal system. The main idea is to convert an infinite impulse response (IIR) filter to a finite impulse response (FIR) filter, which is suitable for the parallelization processing of GPU. Finally, the converted FIR filter algorithm is implemented on the GPU efficiently, achieving a speed approximately twice as fast as that of the previous IIR filter algorithm implemented on the CPU.

Improved Clutter Suppression Algorithm for Atmospheric Target Detection Using Polarimetric Doppler Radar

When using dual-orthogonal frequency modulation continuous wave (FMCW) polarimetric radar, the polarimetric range-Doppler spectra of the atmospheric targets are contaminated by noise, clutter, and artificial signals that disturb the analysis of atmospheric target parameters, such as reflectivity, mean Doppler velocity, and Doppler width. This paper describes an improved clutter suppression method that is able to detect most atmospheric targets while suppressing clutter, noise, and artificial signals as well as possible. By using logical decisions that are based on the spectral linear depolarization ratios (SLDRs), a binary mask matrix is constructed, and the characteristics of this original mask matrix are improved via mathematical morphology methods. To suppress clutter, noise, and artificial signals and retain atmospheric targets, we multiply the range-Doppler spectra by the improved mask matrix. The raw atmospheric data are acquired using the Polarimetric Agile Radar S- And X-band (PARSAX) radar, which is able to measure the backscattering matrix of atmospheric targets in one sweep. After calibration, the range-Doppler polarimetric spectra are processed using the proposed clutter suppression method, and the atmospheric targets are successfully detected. Compared with the unprocessed data and the data processed by noise clipping and double SLDR filtering, the data processed by the proposed method show improvement in the ability to determine the atmospheric targets in the range-Doppler spectrogram, the reflectivity, the mean Doppler velocity, and the Doppler width.

A fast-converging space-time adaptive processing algorithm for non-Gaussian clutter suppression

The normalized fractionally-lower order moment (NFLOM) algorithm differs from the normalized least mean square (NLMS) algorithm in that it minimizes the lower order moment (p<2) of the error rather than the variance (p=2). This paper first evaluates the performances of the NFLOM for space-time adaptive processing in heavy-tailed compound K clutters in terms of the excess mean square error (MSE), misalignment, beampatterns, and output signal-to-interference-and-noise-ratio (SINR). The results show that the MSE curve of a small-order NFLOM exhibits faster convergence but higher steady-state error than a large-order NFLOM. Second, this paper proposes a new variable-order FLOM algorithm to dynamically change the order during adaptation, thus achieving both fast initial convergence and low steady-state error. The new algorithm is applied to STAP for Gaussian and non-Gaussian clutter suppression. The simulation results show that it achieves the best compromise between fast convergence and low steady-state error in both types of clutters.

Clutter suppression algorithm based on bidirectional local binary pattern for moving point target detection in infrared image sequences

A clutter suppression algorithm called the rectification filter with indications of bidirectional local binary patterns (BDLBP-RF) is proposed as a resolution to the problem of detecting dim targets in infrared (IR) image sequences. First of all, a local binary pattern (LBP) operator with properties of grayscale and rotation invariance is introduced in the application of clutter suppression. Each pixel in the image is estimated by its spatial neighbor pixels and the corresponding LBPs in prior and posterior frames. The approach proposed is based on a spatiotemporal process, in which interframe and intraframe properties of the IR image sequence are both taken into account. The method is evaluated by the comparative experiments, and the LBP operator's optimum values of radius and number of neighbors are discussed. The results of the experiment prove that BDLBP-RF has excellent performance and stability in clutter suppression under various situations. The target point in images processed by our approach received high signal-to-clutter ratio gain, and the detectability of the target is enhanced.

Elevation Adaptive Algorithm for Short-Range Clutter Suppression in Airborne Phased Array Radar

摘要: 该文提出了一种基于高阶累积量矩阵组正交联合对角化的高分辨方位估计方法。该方法构造了一组高阶累积量矩阵共同来辨识阵列流型矩阵的列空间，进而进行DOA估计。并通过对高阶累积量矩阵组进行联合对角化，得到联合对角化矩阵和对角化后的矩阵组，并重新定义了空间谱。新方法可以处理相干声源，适用于有色噪声环境，且较仅使用单个高阶累积量矩阵的算法具有更高的分辨力，更低的均方根误差和更高的鲁棒性。 关键词: 信号处理; 水声信号; 高阶累积量矩阵; 正交联合对角化; Jacobi旋转; 方位估计