Strong Clutter Research Articles

MRPT: Millimeter-Wave Radar-Based Pedestrian Trajectory Tracking for Autonomous Urban Driving

Pedestrians represent agile and low-observable targets, especially under adverse weather conditions, whose trajectory tracking plays a crucial role in determining pedestrian behavior in autonomous urban driving. Thus, this article presents a millimeter-wave radar-based pedestrian trajectory-tracking (MRPT) system that enables all-weather trajectory perception with high precision. More specifically, to improve the tracking performance in the presence of strong background clutters, a track-before-detection-based algorithm is proposed to address pedestrian detection and tracking jointly, instead of regarding them as two separate phases in conventional methods. After that, a continuous detection method based on the integration of target existence probability and the Markov transition matrix is proposed to achieve superior pedestrian detection performance by directly using unthresholded radar data. In addition, based on the binary-phase-multiplex (BPM) strategy, the proposed system is validated using a low-cost automotive frequency-modulated-continuous-waveform (FMCW) multiple-input–multiple-output (MIMO) radar sensor. Consequently, extensive simulation and experimental results demonstrate that MRPT exhibits better pedestrian detection and tracking performance under low signal-noise-ratio (SNR) conditions compared with traditional methods.

Infrared Small Target Detection Based on Smoothness Measure and Thermal Diffusion Flowmetry

Infrared (IR) small target detection in a complex environment has become a hot topic. Due to the dim intensity and small scale of the target and the strong background clutter, the existing algorithms cannot achieve satisfactory results, namely poor detection performance and poor real-time performance. In this letter, a new real-time small target detection algorithm based on smoothness measure and thermal diffusion (STD) is proposed. First, the smoothness measure is proposed to extract smoothness features of small targets and suppress the clutter background. Then, we improve the existing thermal diffusion equation and design a thermal diffusion operator to extract the target’s thermal diffusion feature. The operator will further enhance the small and dim target’s energy. Finally, the fusion result image—in which the background is suppressed and the target is enhanced—is acquired after combining smoothness and thermal diffusion flowmetry. The experimental results demonstrate that the detection speed of the proposed algorithm is at least four times faster than that of existing algorithms under the same detection conditions. In addition, the proposed algorithm can achieve outstanding detection performance for IR images with different types of complex backgrounds and targets’ sizes within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times7$ </tex-math></inline-formula> .

A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

The main challenge of foreign objects debris (FOD) monitoring based on millimeter-wave (MMW) radar is the interference of strong ground clutter. The clutter-map constant false alarm rate (CM-CFAR) method is commonly utilized for FOD monitoring, but detection results are usually accompanied by many false alarms in the complex ground clutter environment. To solve this problem, this work proposed a false alarm elimination algorithm. Specifically, the CM-CFAR method is firstly used to separate the suspicious targets (including FOD and false alarms) from the background clutter. Then, the Duffing detection system is constructed to detect the sinusoidal frequencies associated with suspicious targets. Finally, according to the detection results of Duffing system, suspicious targets can be classified as FOD and false alarms. In addition, in order to detect the sinusoidal frequencies efficiently, a quantitative detection method based on Poincare section points analysis is proposed to identify the state transition of Duffing oscillator. The results from both simulation and field case show the excellent false alarm elimination performance of the proposed algorithm.

Multichannel SAR Moving Target Detection via RPCA-Net

Ground moving target indication (GMTI), as a challenging task for synthetic aperture radar (SAR) systems, keeps drawing considerable attention. Robust principal component analysis (RPCA) aiming at separating low-rank and sparse components has been successfully employed in SAR systems for GMTI recently. However, its practical application is limited by the heavy computational burden as well as the requirement of manual parameter modification. To cope with this problem, a fast and free of presetting parameters RPCA network (RPCA-Net) is proposed for SAR-GMTI under strong clutter background. In the proposed method, a novel RPCA model is first introduced, where not only the low-rank and sparse terms but also the errors in practical SAR systems are taken into account. Moreover, the low-rank factorization plus scaled gradient descent (ScaledGD) is also employed to acquire low-rank clutter background rather than singular value decomposition (SVD). Then, we parameterize our proposed RPCA model and unfold it as a feedforward neural network (FNN) to acquire the iterative parameters through backpropagation. Compared to the GMTI methods based on traditional RPCA models, our proposed RPCA-Net can provide a higher detection ability and faster convergence without presetting parameters empirically. Experiments on two groups of measured data collected by airborne SAR systems validate the superior performance of the proposed RPCA-Net.

Near-Range Clutter Suppression With Elevation Element Multifrequency Subpulse Coding Array Radar

It is hard to tackle the near-range clutter when range ambiguity exists in the non-sidelooking moving target detection (MTD) application. The angle-Doppler spectra of the far- and near-range clutter cannot be aligned simultaneously in this case due to the range dependence, which would significantly degrade the performance of the space–time adaptive processing (STAP) technology. To address this problem, an elevation element multifrequency subpulse coding (EMFSPC) array framework is proposed in this article. For each pulse duration, the main-lobe beam of the proposed framework can automatically sweep the full space by coding the subpulses and the transmitting elements, which steers the subpulses to different directions. Besides, these multiple subpulses occupy different range-frequency bands, and thus corresponding bandpass filters could be carefully designed to extract the unambiguous signals. After that, one can align the clutter spectra centers and employ the full-dimensional or dimension-reduced STAP techniques to achieve clutter cancellation. Furthermore, the simulation experiments are conducted to demonstrate the validity of the proposed EMFSPC system in near-range strong clutter suppression.

A joint clutter suppression method based on robust principal component analysis and total variation denoising

In the scene of ground moving target detection, the target echo information is often corrupted by strong clutter. In this letter, a joint clutter suppression algorithm is proposed based on robust principal component analysis and total variation denoising for pulse Doppler radar. The novel method named total variation- robust principal component analysis replaces the l1-norm in robust principal component analysis algorithm with a total variation seminorm. Total variation robust principal component analysis retains the clutter suppression ability of robust principal component analysis, and effectively separates the target from the noise in range-Doppler domain.

A CA-CFAR-Like Detector Using the Gerschgorin Circle Theorem for Bistatic Space Based Radar

The problem of the constant false alarm rate (CFAR) detection of bistatic space based radar (B-SBR) is addressed under the background of strong and nonhomogeneous clutter. We propose a novel and simple cell averaging (CA)-CFAR-like (CA-CFAR-L) detection method based on the Gerschgorin circle theorem for B-SBR. First, we investigate the potential of a novel test statistic TCA-CFAR-L exhibiting better properties about discrimination and CFAR using the Gerschgorin circle theorem, and the test statistic TCA-CFAR-L is constructed as the ratio of the sum of the Gerschgorin radii of the covariance matrix of the cell under test to the sum of the Gerschgorin radii of the mean covariance matrix of the reference cells. Second, we investigate the CA-CFAR-L detector for B-SBR using the constructed test statistic TCA-CFAR-L. Under the background of the K-distribution clutter model and the clutter model for B-SBR, respectively, we verify the performance of the proposed CA-CFAR-L detector, the LE mean matrix CFAR detector and the Fast Fourier Transform (FFT)–CFAR detector. Also, we analyze the experimental data for the IPIX radar. The simulation results and experimental data analysis demonstrate that the detection performance of the detector is better than the LE mean matrix CFAR detector and the FFT–CFAR detector, and the proposed method has less computational complexity. These will provide an important theoretical basis and new ideas for radar target detection in the future.

M-FCN based sea-surface weak target detection

This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network (M-FCN) in strong sea clutter. Firstly, the constant false alarm rate (CFAR) detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration. Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate, and how to suppress a large number of false alarms is the key to improve the performance of weak target detection. Then, the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form. Finally, the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames. For improving the detection performance, the historical multi-frame information is memorized by the network, and the end-to-end structure is established to detect sea-surface weak target automatically. Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection (TBD) method and reduces false alarm tracks by 35.1%, which greatly improves the track quality.

Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.

A refocusing iterative optimization method based on the quad-beam mode for accurate estimation of the azimuth velocity of slow-moving targets using SAR

ABSTRACT SAR slow-moving target detection and velocity estimation in ultra-high speed platform and strong clutter are a serious problem. The existing slow-moving target velocity estimation methods including fractional Fourier transform (FrFT), bidirectional imaging mode (DiBi) and the dual-beam interferometry mode (DBI) cannot suppress the clutter, which reduces the accuracy of velocity estimation. To solve the problem, we propose an accurate azimuth velocity estimating method refocusing iterative optimization (RIO) based on the quad-beam mode. Firstly, we use the back projection (BP) algorithm for the quad-beam imaging, and perform accurate registration and clutter suppression for the two forward (fore-) and two afterward (aft-) beams respectively. After that, the azimuth offset in fore- and aft-beam images is utilized to achieve the coarse azimuth velocity estimation. Then we put forward the RIO algorithm to compensate the error in velocity estimation. Through iterative search and compensation the azimuth velocity, the azimuth offset is gradually reduced to disappear, thus a more accurate estimation of azimuth velocity is realized. The experiments validate the accuracy of azimuth velocity estimation of the proposed method and the error is less than 0.01 m , which is much better than the other three typical methods (FrFT, DiBi, DBI).

A track-before-detect algorithm for UWB radar sensor networks

Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by a multistatic radar network with localization errors of 25.4 cm and 19.7 cm, a detection rate of the two targets of 88.75%, and no false alarm trajectory.

Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging.

Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS CF ). PSAP demonstrated improved target detectability with a higher generalized contrast-to-noise (gCNR) ratio in vasculature simulations where PSAP produces 19.61% and 19.53% higher gCNRs than DAS and DAS CF , respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (CR) (e.g., PSAP produces 89.26% and 11.90% higher CR than DAS and DAS CF in vasculature simulations) and improved clutter suppression.

Track Prediction for HF Radar Vessels Submerged in Strong Clutter Based on MSCNN Fusion with GRU-AM and AR Model

High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability. However, the accurate detection of HF radar vessels is severely restricted by strong clutter and interference, causing the echo of vessels completely submerged by clutter. As a result, the target cannot be detected and tracked for a period of time under the influence of strong clutter, which causes broken trajectories. To solve this problem, we propose an HF radar-vessel trajectory-prediction method based on a multi-scale convolutional neural network (MSCNN) that combines a gated recurrent unit and attention mechanism (GRU-AM) and a fusion with an autoregressive (AR) model. The vessel’s latitude and longitude information obtained by the HF radar is sent into the convolutional neural network (CNN) with different window lengths in parallel, and feature fusion is performed on the extracted multi-scale features. The deep GRU model is built to learn the time series with the GRU structure to preserve historical information. Different weights are given to the features using the temporal attention mechanism (AM), which helps the network learn the key information. The linear information on latitude and longitude at the current timestep is forecast by combining the AR model with the trajectory output from the AM to achieve a combination of linear and nonlinear prediction models. To make full use of the HF radar tracking information, the broken trajectory prediction is carried out by forward and backward computation using data from before and after the fracture, respectively. Weights are then assigned to the two predicted results by the entropy-value method to obtain the final ship trajectory by weighted summation. Field experiments show that the proposed method can accurately forecast the trajectories of vessels concealed in clutter. In comparison with other mainstream methods, the new method performs better in estimation accuracy for HF radar vessels concealed in clutter.

Multiple Infrared Small Targets Detection Based on Hierarchical Maximal Entropy Random Walk

The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is essential for infrared search and tracking systems. In this letter, we establish a multiple small targets detection method derived from hierarchical maximal entropy random walk (HMERW). The HMERW revolves the limitation of strong bias to the most salient target of the primal maximal entropy random walk (MERW) based on a proposed graph decomposition theory. To enhance the characteristics of small targets and suppress strong clutters, we design a specific weight matrix for HMERW instead of using the conventional weight matrix in MERW. First, a stationary distribution map is obtained by importing the filtered infrared image into the HMERW. Second, a coefficient map is constructed based on the designed weight matrix to fuse the stationary distribution map. Then, an adaptive threshold is used to segment multiple small targets from the fusion map. Extensive experiments on practical datasets demonstrate that the proposed method is superior to the state-of-the-art methods in terms of target enhancement, background suppression, and multiple small targets detection.

PDF-ED-based adaptive waveform optimization for small slow target detection in strong clutter

This paper addresses the problem of small slow target detection in a strong clutter. By exploiting the characteristics of radar cross section fluctuation, the complex Gaussian procedure is utilized to model the matching filter output, introduce the Euclidean distance between the probability density functions (PDF-ED) to represent the resolvability of different hypotheses, and propose an adaptive waveform optimization algorithm that maximizes the PDF-ED between different hypotheses. Compared to the traditional ED between hypotheses, which only takes the mean value of the probability density into consideration, the PDF-ED metric considers the shape of the PDF curve and specifies the essential difference between two hypotheses in terms of information geometry. In this way, the proposed algorithm enhances the target and suppresses the clutter by reducing the side lobes at specified delay-Doppler units, resulting in the improvement of detection performance. The numerical experiments validate the effectiveness of the algorithm.

Noise radar range doppler imaging via 2D generalized smoothed‐ l 0

This paper presents an algorithm to enhance the range-Doppler imaging performance for noise radar. Traditional matched filtering based method suffers from high range and Doppler sidelobes, which makes the weak targets overwhelmed by the sidelobes of strong targets or clutters in the range-Doppler map. Sparse recovery based methods have been widely used to suppress such sidelobes, but most of them assume a repetitive transmit waveform, which cannot be applied to noise radar applications. In this letter, the range-Doppler imaging problem for noise radar is formulated as a sparse recovery model and solved by a 2D generalized smoothed-l0 algorithm. The proposed method can deal with the random waveforms varying among different pulses in noise radar. The robustness of the proposed method in strong noise and clutters is validated by simulation results.

Infrared maritime dim small target detection based on spatiotemporal cues and directional morphological filtering

The existing small target detection algorithms will suffer severe performance deterioration when the weak target is submerged in heavy cluttered infrared (IR) maritime image. To settle this problem, a small target detection scheme is developed based on spatiotemporal cues and multidirectional morphological filtering. Initially, considering the small target will be isotropic because of the point spread function of the long-distance thermal imaging, while background edge clutters are generally local directional, the multidirectional structuring elements (MSEs) are constructed. By incorporating constructed MSEs and morphological operations, the directional morphological filtering (DMF) is established to measure the multidirectional differences between target region and local surroundings. Then, according to the brightness and isotropic Gaussian-like shape of small target in local spatial domain, the local intensity difference measure (LIDM) and the local Gaussian-like shape measure (LGSM) are presented to differentiate the small target from background clutters. After that, in order to integrate the two spatial properties, the multidirectional improved top-hat filter (MITHF) is proposed by fusing LIDM and LGSM to highlight potential small targets and suppress strong structural edge clutters. Finally, considering the consistency of targets and the fluctuation of sea clutters in temporal domain, a new appearance descriptor namely histogram of oriented morphological filtering (HOMF) is designed to characterize the small target by fully exploiting the advantages of DMF, and the HOMF-based multi-frame confirmation strategy (HMCS) is developed to further identify real small targets and eliminate false alarms. Extensive experiments prove that proposed algorithm is superior to the compared methods, especially for dim small target buried in intricate maritime background clutter.

Unsupervised Ship Detection for Single-Channel SAR Images Based on Multiscale Saliency and Complex Signal Kurtosis

Traditional ship detection methods for synthetic aperture radar (SAR) mainly utilize the amplitude information to distinguish ship targets from sea clutter, including constant false alarm rate (CFAR), visual attention model, and deep learning methods. The CFAR algorithms adopt the dense sliding window strategy, which is very time-consuming and may generate numerous false alarms. The deep learning methods are supervised and difficult to obtain satisfactory performance when the number of labeled samples is insufficient. The visual attention models can quickly focus on the potential target area, and however, it is still difficult to eliminate the strong clutter, such as radio frequency interference and azimuth ambiguity. In fact, as a coherent imaging system, SAR data itself are complex-valued. Compared with the amplitude information, complex information can essentially reflect the difference between ship target and sea clutter. To improve the accuracy and efficiency of ship detection, in this letter, a novel unsupervised ship detection method based on multiscale saliency and complex signal kurtosis (MSS-CSK) for single-channel SAR images is proposed, which contains the proposal extraction stage and the target discrimination stage. The experimental results based on the Radarsat-2 real SAR data show that the proposed method has high detection accuracy and efficiency.

Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

The development of autonomous vehicles and unmanned aerial vehicles has led to a current research focus on improving the environmental perception of automation equipment. The unmanned platform detects its surroundings and then makes a decision based on environmental information. The major challenge of environmental perception is to detect and classify objects precisely; thus, it is necessary to perform fusion of different heterogeneous data to achieve complementary advantages. In this paper, a robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed. The corresponding regions of interest (ROIs) are accurately calculated from the approximate position of the target detected by radar and cameras. A joint classification network is used to extract micro-Doppler features from the time-frequency spectrum and texture features from images in the ROIs. A fusion dataset between radar and camera is established using a fusion data acquisition platform and includes intersections, highways, roads, and playgrounds in schools during the day and at night. The traditional radar signal algorithm, the Faster R-CNN model and our proposed fusion network model, called RCF-Faster R-CNN, are evaluated in this dataset. The experimental results indicate that the mAP(mean Average Precision) of our network is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.

Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications.

In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper. The CVMD is extended from variational mode decomposition (VMD), which is a recently introduced technique for adaptive signal decomposition, limited to only dealing with the real signal. Thus, the VMD is extended from the real domain to the complex domain firstly. Then, the optimal effective order of singular value is obtained by singular value decomposition (SVD) to solve the problem of under-decomposition or over-decomposition caused by unreasonable choice of decomposition layer, it is more accurate than detrended fluctuation analysis (DFA) and empirical mode decomposition (EMD). Finally, the strongly correlated modes and weakly correlated modes are judged by calculating the Mahalanobis distance between the band-limited intrinsic mode functions (BLIMFs) and the original signal, which is more robust than the correlation judgment methods such as computing cross-correlation, Euclidean distance, Bhattachryya distance and Hausdorff distance. After the weak correlation modes are eliminated, the signal is reconstructed locally, and the separation of the micro-motion signal is realized. The experimental results show that the proposed method can filter out the strong clutter component and the fuselage component from radar echo more effectively than the local mean decomposition (LMD), empirical mode decomposition and moving target indicator (MTI) filter.