Clutter Suppression Algorithm Research Articles

Adaptive time-space-varying clutter suppression algorithm for handheld IR-UWB radar

Adaptive time-space-varying clutter suppression algorithm for handheld IR-UWB radar

Reweighted Extreme Learning Machine-Based Clutter Suppression and Range Compensation Algorithm for Non-Side-Looking Airborne Radar

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages.

A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.

A Non-stationary Clutter Suppression Algorithm for Space-based Radar Based on Presequence Pulse Echoes

A Non-stationary Clutter Suppression Algorithm for Space-based Radar Based on Presequence Pulse Echoes

Implementation of Digital Signal Processor for Pulse Doppler Radar

This paper presents the design and implementation of a digital signal processor (DSP) board, utilizing a TMS320C50 family DSP chip, for pulse-Doppler radar systems. Pulse-Doppler radar systems often encounter challenges such as strong clutter, noise, and jamming in dealing with echoes. To overcome these challenges, advanced digital signal processing techniques are employed. The main objective of this paper is to introduce a cost-effective signal processing solution that significantly enhances the performance of the radar system and brings it up to speed with modern radar technologies. The hardware described in this work can also be effectively utilized for implementing various types of signal processing algorithms. Additionally, as a secondary objective, the paper presents the digital realization of a radar detector. Traditionally, this detector was constructed using an analog Doppler filter bank. However, in this work, it is digitally implemented using N digital filters in place of the analog bank. By utilizing the designed DSP board and implementing the radar detector digitally, this paper demonstrates the potential for improved performance and efficiency in pulse-Doppler radar systems. The advancements made in this work contribute to the development of cost-effective and technologically advanced radar systems. The research results are presented for four different wind conditions, showcasing the effectiveness of the proposed approach. Furthermore, the paper suggests an algorithm that combines parametric and non-parametric techniques and provides a detailed explanation of its implementation. Finally, using the non-parametric technique, the probability of detection curves (PD) are simulated with respect to the signal-to-clutter ratio (SCR) for each wind condition, and the simulation results are depicted and compared. The achievements of this paper include the proposal of an efficient approach for clutter suppression in ground surveillance pulse Doppler radar, the comparison of different clutter suppression algorithms, and the development of an algorithm that combines parametric and non-parametric techniques. The simulation results provide valuable insights into the performance of the proposed algorithms under different wind conditions.

Extended least squares (ELS) and generalized least squares (GLS) for clutter suppression in hyperspectral images: A theoretical description

Hyperspectral imaging (HSI) has been demonstrated to be useful for estimating spatial distributions of different chemical constituents on surfaces and in imaged scenes, as well as for detecting anomalies and known targets that may be present. In target detection, the objective is to find pixels that contain significant signal attributable to a known “target” spectrum. Unfortunately, measured images are often dominated by “non-target” interference signals with unknown spectra that vary image-to-image. To be effective, target detection algorithms must account for the non-target signal, commonly referred to as “clutter.” The utility of HSI for target detection in spatially complex scenarios can be attributed to two aspects associated with the sensing system: 1) The target can dominate the measured signal in a few individual pixels, although it may have only a minor contribution to the overall signal, and 2) HSI measures many pixels relevant for characterizing clutter that can be used in clutter suppression algorithms to account for interferences.Clutter suppression enables the detection of minor signal sources in HSI, resulting in a sensing system uniquely suited to highly variable environmental scenarios. Popular clutter suppression methods such as extended least squares (ELS) and generalized least squares (GLS) can be described as extensions of the classical least squares (CLS) model. The main advantage of these methods is that they are fast and easy to train on an image-to-image basis providing highly sensitive, image-specific target detection systems. However, the optimization of such models affects the sensitivity and selectivity of the detection. In an effort to assist practitioners interested in clutter suppression, this paper presents a detailed description of the theoretical aspects of ELS and GLS methods used for target detection on hyperspectral images. The concepts are demonstrated with practical examples of the detection of an acrylonitrile butadiene styrene (ABS) particle in sand based on a single near infrared hyperspectral image.

Range-Ambiguous Sea Clutter Suppression for Multichannel Spaceborne Radar Applications Via Alternating APC Processing

In this article, a novel range ambiguous cluter suppression method, i.e., alternating azitmuth phase coding (AAPC), based on azimuth phase coding (APC) is proposed, where the blind areas of target detection are solved. Based on the motion model of spaceborne radar (SBR) system, the sea clutter motion property is analyzed, and a multichannel sea clutter signal model with the consideration of range ambiguity is established. After that, the novel method AAPC is proposed. Finally, a time-domain sliding window projection method with the consideration of clutter internal motions is proposed to realize the sea clutter rejection. Compared with the current range-ambiguous sea clutter suppression algorithms, the improvements of this method are: 1) the range-ambiguous clutter can be separated and most of the clutter components in the main-lobe region can be rejected; and 2) the repetitive detection blind regions can be avoided by applying the proposed AAPC technique. Simulation results are presented to validate the feasibility of the proposed algorithm.

Sea clutter suppression algorithm in low SCR based on improved fractional Fourier transform

Sea clutter suppression and target detection under complex sea states in low signal-to-clutter ratio (SCR) is the key of current maritime detection research. The target extraction ability of most algorithms is mostly based on accurate modeling of sea clutter, but sea clutter is complex and unstable, which is difficult to accurately model, especially in poor sea states. According to the characteristics of the target echo signal, this paper introduces intrinsic time-scale decomposition (ITD) to preprocess the echo signal to improve the algorithm effect under low SCR. Secondly, the too much cycles in searching for optimal transformation in fractional Fourier transform (FRFT) is improved to reduce the calculation cost and extract the target signal accurately. The effectiveness and reliability of the proposed method are verified by simulation and measured experiments under different SCR and different transform order interval Δp, and the sea clutter suppression ability and target extraction accuracy are measured by parameters such as the detection rate, the gain of SCR and the running time. Compared with FRFT-related algorithms, the results indicates that the proposed algorithm can effectively suppress sea clutter and accurately extract target signal even at SCR = −25 dB in less than half the running time of the comparison algorithms.

A sea clutter suppression algorithm for over-the-horizon radar based on dictionary learning and subspace estimation

Detecting a ship target by over-the-horizon radar (OTHR) is seriously affected by sea clutter. This paper proposes a clutter suppression algorithm based on dictionary learning and subspace estimation (DLSE). First, the K-singular value decomposition (K-SVD) is used for dictionary learning and sparse representation of the sea clutter. Then, the orthogonal matching pursuit (OMP) is used to reconstruct the sea clutter component to suppress the outliers. Finally, the clutter covariance matrix is calculated using the reconstructed clutter spectrum, and the clutter subspace of the covariance matrix is estimated to achieve clutter suppression. In this paper, the K-SVD is improved to preserve the phase information of the complex signal. The sea clutter spectrum, after sparse representation and reconstruction, is not impacted by outliers in neighboring cells. Therefore, the clutter covariance matrix has a better estimate accuracy than existing subspace decomposition algorithm. The adaptive parameter estimate method also enhances the adaptability of the DLSE. The experimental results of real data demonstrate that the DLSE is as sophisticated as existing subspace decomposition algorithms while having a higher output signal-to-clutter-plus-noise ratio (SCNR) and peak-to-side lobe ratio (PSLR), improving about 2-5 dB. Results of outlier suppression attest to the robustness of the DLSE. Under various coherent integration times (CIT) and frequencies, the DLSE has the best clutter suppression performance among the comparison algorithms.

A Clutter Suppression Algorithm via Enhanced Sparse Bayesian Learning for Airborne Radar

The traditional space-time adaptive processing (STAP) method based on sparse Bayesian learning (SBL) has the problems of low computational efficiency and slow convergence speed. In this paper, a novel SBL approach based on statistical threshold is proposed to address these issues. To discriminate between the active and inactive atoms of the dictionary, we first develop an adaptive decision test. Next, the adaptive decision test is integrated into the SBL method, which increases its accuracy and convergence rate. Additionally, the detection threshold has an important property that is independent of signal and noise power. Therefore, the adaptive decision test is robust to external changes. Numerous simulations demonstrate that the proposed algorithm has excellent clutter suppression performance and fast convergence rate.

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

In an airborne passive radar, multipath (MP) clutter, which is caused by MP signals contained in the contaminated reference signal, degrades the space-time adaptive processing (STAP) performance. The MP clutter suppression algorithm before STAP can mitigate the influence of impure reference signals. However, the performances of the existing MP clutter suppression methods deteriorate when the intrinsic clutter motion (ICM) exists because the sparse model of MP clutter is disturbed. To eliminate the impacts of ICM on MP clutter suppression, a joint optimization algorithm is developed for airborne passive radar. Firstly, the sparse model of MP clutter is modified by taking ICM fluctuation into account. Subsequently, the joint optimization function of the ICM fluctuation and MP clutter profile is derived. Finally, based on the local search technique, MP clutter is suppressed with ICM error calibration and off-grid effects mitigation. A range of simulations verify the reliability and superiority of the proposed method.

A Comparative Study on Radio Frequency Interference Suppression and foF2 Scaling for Ionograms

In this paper, we compare the performances of radio frequency interference (RFI) suppression between radio frequency interference mitigation (RFIM) and frequency-domain adaptive clutter suppression (FACS) algorithms that apply to ionograms for foF2 scaling through true height analysis. We find that the use of RFIM and FACS can both effectively suppress RFIs in ionograms to improve the signal-to-clutter (SCR) of the original ionogram. Overall, the RFI clutter suppressed by FACS is about 5–7 dB better than that by RFIM. In addition, the SCR improvement made by FACS with neighboring range cells in a range of 10–40 is also better than that made by RFIM, which is about 5–10 dB for FACS and 4–8 dB for RFIM, respectively. However, the signal-to-noise ratios (SNRs) of the ionospheric echoes of the ionograms with FACS are about 3–5 dB (2–3 dB) smaller than those of RFIM during daytime (nighttime). Intercomparisons reveal that the correlations of the scaled foF2 values between the original and RFIM ionograms are slightly higher than those between the original and FACS ionograms, while the root-mean-square-error (RMSE) and mean bias of the former are slightly larger than the latter. These results indicate that the use of RFIM and FACS algorithms to suppress RFIs in ionograms can conditionally improve the quality of scaled foF2 using Ke et al.’s autoscaling algorithm. Statistical results reveal that the use of FACS algorithms can result in about a 5–12% improvement in uncertainty and a 9–80% improvement in accuracy, provided that 5–20 neighboring range cells are selected. As for the RFIM algorithm with a threshold of 2–2.5 times standard deviation, the improvements in uncertainty and accuracy are, respectively, about 0.9–10% and 11–90%.

Unsupervised Affinity Propagation Clustering Based Clutter Suppression and Target Detection Algorithm for Non-Side-Looking Airborne Radar

Aimingat non-side-looking airborne radar, we propose a novel unsupervised affinity propagation (AP) clustering radar detection algorithm to suppress clutter and detect targets. The proposed method first uses selected power points as well as space-time adaptive processing (STAP) weight vector, and designs matrix-transformation-based weighted input data, with which the first unsupervised weighted AP clustering is proposed by means of their similarity matrix, responsibility values and availability values. Then, new reconstructed weighted power inputs are designed, and the second weighted AP clustering is proposed. Finally, with their cluster results, a detection-discriminant criterion is designed for the judgment of target detection, and simultaneously, the clutter is suppressed. Compared with the conventional and important STAP, ADC and JDL algorithms, and several SO-based, GO-based and OS-based CFAR algorithms, the proposed unsupervised algorithm achieves much higher probability of detection and provides distinctly superior target-detection performance. With reasonable computation time, it can better conquer the range dependence in characteristic of clutter and better process non-independent identically distributed (non-IID) samples of non-side-looking radar. Sufficient simulations are performed, and they demonstrate that the proposed unsupervised algorithm is preferable and advantageous.

Dimensionality reduction bistatic radar clutter suppression technique based on three-dimensional cross-beam

Aiming at the problem that the non-stationary of airborne bistatic radar clutter reduces the performance of traditional clutter suppression algorithm, this paper first formulates airborne bistatic radar clutter model, and then analyzes the bistatic radar clutter characteristics in four typical combat scenarios. In addition, in order to reduce the computation burden of the STAP algorithm, a three-dimensional cross-beam clutter suppression algorithm is proposed, which uses the transformation matrix to convert the space-time two-dimensional data into azimuth-elevation-Doppler three-dimensional data. By eliminating auxiliary beam clutter data, only the main beam clutter data which plays a major role in dual-basis clutter suppression is retained to form a three-dimensional cross-beam for local adaptive clutter suppression. The proposed algorithm can greatly reduce the amount of calculation and the demand of training samples, while trying to ensure that the clutter suppression performance does not degrade. The effectiveness of the proposed algorithm is verified by analyzing the clutter suppression effect and computation amount of various algorithms in typical combat scenarios. Therefore, the algorithm has great potential in the applications of airborne bistatic and non-stationary clutter suppression.

Multichannel Sea Clutter Measurement and Space-Time Characteristics Analysis with L-Band Shore-Based Radar

In order to study the space-time characteristics of sea clutter, the sea clutter is always measured by the airborne multichannel radar; however, the sea clutter shows the heterogeneity between range gates, which means the space-time covariance matrix’s correspondence to the single range gate cannot be estimated accurately. Meanwhile, the measurement of the sea clutter data by the airborne radar is usually affected by the motion of the platform, which makes the analysis results unrepresentative of the space-time characteristics of the pure sea clutter. In this paper, a sea clutter measurement method based on L-band shore-based multichannel radar is proposed, where the transmit sub-array periodically moves with the pulse repetition period to obtain multiple sets of coherent processing interval pulses for each range gate. This measurement method can exclude the influences of the moving platform. Moreover, a sea clutter space-time signal model of the single range gate is proposed, and the model is used to simulate three-dimensional sea clutter data with space-time coupling characteristics. With verification of the measured and simulated data, it can be seen that the data composed of single range gate and multiple coherent processing interval pulses can accurately estimate the space-time covariance matrix corresponding to this single range gate. Furthermore, the space-time characteristics are analyzed based on the measured data. The results show that the eigenvalue spectrum and the spread width of space-time power spectrum are influenced by the backscattering coefficient of sea clutter and the speed of sea surface motion. In comparison, the decorrelation effect caused by the backscattering coefficient of sea clutter is stronger than that caused by the speed of the surface motion. The proposed method is helpful for guiding multichannel sea clutter measurement and the analysis results are of great significance to the clutter suppression algorithms of the marine multichannel radar.

Airborne Passive Bistatic Radar Clutter Suppression Algorithm Based on Root Off-Grid Sparse Bayesian Learning

When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space–time adaptive processing (STAP) algorithm based on root off-grid sparse Bayesian learning. The proposed algorithm first models the space–time base of the dictionary as an adjustable state. Then, the positions of those dynamic bases are optimized by iterating a maximum expectation algorithm. In this way, the off-grid error in clutter estimation can be eliminated even when the modeling grid is wide. To further improve the accuracy of clutter estimation, the proposed algorithm eliminates the error caused by samples with singular values in the root off-grid sparse Bayes learning by artificially adding pseudorandom noise and using hypothesis testing. The simulation results show that the proposed algorithm achieves better performance than the existing algorithms.

Space-Time Adaptive Processing Clutter-Suppression Algorithm Based on Beam Reshaping for High-Frequency Surface Wave Radar

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter.

Clutter Suppression Algorithm of Ultrasonic Color Doppler Imaging Based on BP Neural Network

Aiming at the time complexity of singular value spectrum weighted Hankel SVD filtering algorithm, a clutter suppression algorithm for ultrasonic color Doppler imaging based on BP neural network model is proposed in this paper. Firstly, using the PRF data collected by portable ultrasound instrument, we verify the singular value weighted Hankel SVD filtering algorithm, and the results show that the algorithm has high accuracy; Then, the BP neural network model is established based on the input and output data of singular value weighted Hankel-SVD filtering algorithm; Finally, the clutter suppression algorithm of ultrasonic color Doppler imaging based on BP neural network model is established. The experimental results show that compared with Hankel SVD filtering algorithm, the clutter suppression algorithm proposed in this paper greatly shortens the operation time without reducing the accuracy, so as to improve the real-time performance of the filtering algorithm.

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

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

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering.

In marine radar target detection, sea clutter will cause a large number of missed alarms and false alarms, which will affect the accuracy of target detection. In order to suppress sea clutter effectively, a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering is proposed in this paper. The proposed method is to add a sea clutter suppression link before detecting the target. Firstly, the marine radar image sequence is transformed into three-dimensional frequency wavenumber domain by three-dimensional fast Fourier transform (3D-FFT), and then the three-dimensional image spectrum is obtained. According to the fact that the sea clutter spectrum obtained from the image spectrum satisfies the dispersion relation of linear wave theory in the three-dimensional frequency wavenumber domain, a sea clutter model is established. Then, through the established sea clutter model, a spatio-temporal domain joint sea clutter suppressor is designed to filter the image spectrum. After that, the filtered image spectrum is transformed by three-dimensional inverse fast Fourier transform (3D-IFFT) to obtain the image sequence in which sea clutter is suppressed. Finally, target detection is carried out for sea clutter suppressed image sequence. The method is validated by using the real data of X-band marine radar. Compared with the classical Empirical mode decomposition (EMD) method, the improvement of signal-to-noise ratio (SNR) is more obvious, and SNR can be increased by 15.3 db at most. In addition, compared with target detection on original images directly, the proposed method has excellent detection rate and can increase detection rates by at least 8%.