Heavy Clutter Research Articles

Off-Grid direction of arrival estimation in the presence of measurement noise and heavy cluttered environment

In this paper, we focus on estimating Direction of Arrival (DOA) and removing heavy clutter embedded with measurement noise. A correlated Gaussian process is chosen to model destructive effects of clutter. Also, a white Gaussian process is selected to describe measurement noise caused by sensor array. After adding these distortions to the off-grid model, we utilize Sparse Bayesian Learning and principal component analysis (as a preprocessing stage) in order to remove these distortions as well as estimating of true DOAs. Finally, at the end we will show how ignorance of clutter from model or combine it with measurement noise degrade DOA estimation. This will be demonstrated by various numerical simulations.

Doppler Resilient Complementary Waveform Design for Active Sensing

Active sensing systems prefer probing waveforms with good auto-correlation properties. Faced with the difficulty in getting individual sequences with impulse-like aperiodic auto-correlation functions, we resort to the temporal waveform diversity based on complementary sets of sequences (CSS). It seems an ideal solution, but has rarely been used due to the extreme sensitivity of CSS to Doppler shifts. This paper is devoted to applying numerical methods to the design of Doppler resilient complementary waveforms (DRCW) whose ambiguity functions are almost free of range sidelobes along modest Doppler shifts. We formulate the problem as minimizing the worst-case peak sidelobe level (PSL) with low peak-to-average power ratio (PAR) constraints, and then approximate the min-max problem by a ${l}_{p}$ -norm minimization problem. A hierarchical strategy is developed to tackle the phase optimization and the amplitude-phase optimization respectively. Correspondingly, two algorithms based on phase-gradient and Majorization-Minimization are proposed, both of which can be efficiently computed via FFT. Numerical experiments show our method is effective and superior to existing ones. The DRCWs thus obtained have their range sidelobes at all lags suppressed below −60dB, and are expected to bring significant performance improvements in active sensing. Two application examples, detecting weak targets in heavy clutter and estimating the high-resolution range profile of an extended target by active radars, are given.

A Track Initiation Algorithm Using Residual Threshold for Shore-Based Radar in Heavy Clutter Environments

There is a large error in the actual radar trajectory tracking process. Track initiation is the primary problem in trajectory tracking and the first step in target tracking. The current track initiation algorithms are greatly affected by heavy clutter environments, so it is necessary to propose an algorithm to solve the problem of low track initiation efficiency. This paper presents a track initiation algorithm using a residual threshold in heavy clutter environments. The falling probability of measured value and decision threshold are used to determine the correlation window. The angle limiting condition is added to establish the track association, and the residual threshold is used to further eliminate the false tracks. The initial track experiment with the trajectory data in the sea near Rizhao Port shows that the algorithm is superior to the traditional logic method and Kalman filter method in track quality. The experiment uses the AIS buffer zone to calculate track initiation probability and uses the multi-region AIS trajectory data for verification. The experimental result shows that track initiation probability with the proposed algorithm in this paper can reach 92.31%.

Underwater Target Tracking in Uncertain Multipath Ocean Environments

In order to address the problem of 3-D localization of an underwater target using a 2-D active sonar with unknown oceanographic factors in a multipath environment with heavy clutter, a novel iterative framework based on Maximum Likelihood Probabilistic Data Association (ML-PDA), which considers ocean sound speed profile (SSP) uncertainty and utilizes multiple detections to realize 3-D position estimation with only bearing and time of flight (ToF) measurements, is proposed. ML-PDA is highly effective in low SNR target detection. However, it is limited by its assumption of at most one target-originated detection within a scan. To estimate the 3-D target state with multipath detections under weak observability conditions, we first extend the ML-PDA into a multipath ML-PDA by enumerating the combined association events formed from multiple detection patterns. In contrast to the situation in air target tracking, the water column is nonhomogeneous and the underwater sound speed profile varies, influenced by uncertain ocean factors, e.g., temperature, salinity, and pressure. The resultant acoustic signal travels in a curvilinear path instead of a straight line. In this article, an SSP-dependent ToF measurement model is derived for both the direct path and the surface-reflected path between two remote nodes, so that the SSP uncertainty can be addressed systematically. By adopting an iterative prediction-update methodology, we first propagate the SSP uncertainty into the modified measurement covariance with the help of the unscented sampling technique. Then, we formulate a new joint likelihood ratio (JLLR) function based on the modified measurement covariance within the multidetection ML-PDA framework. A hybrid optimization method with grid search and particle swarm optimization is applied to solve the complex JLLR objective function and to find the optimal target state estimate from a large surveillance region. Finally, a sequential update technique is used to update the SSP state with the estimated target state and sensor measurements. In subsequent iterations, a more accurate JLLR can be rebuilt based on the updated SSP state, which can help find a better parameter estimate eventually. In addition, the Cramér-Rao lower bound, which quantifies the best possible accuracy in the presence of SSP uncertainties, is derived and analyzed. Numerical simulations confirm the underwater target localization performance of the proposed method in the presence of heavy clutter in an unknown ocean environment with a realistic sound propagation model.

DETECTION OF TEXTURE-LESS OBJECTS BY LINE-BASED APPROACH

This paper proposes a method for tackling the problem of scalable object instance detection in the presence of clutter and occlusions. It gathers together advantages in respect of the state-of-the-art object detection approaches, being at the same time able to scale favorably with the number of models, computationally efficient and suited to texture-less objects as well. The proposed method has the following advantages: a) generality – it works for both texture-less and textured objects, b) scalability – it scales sub-linearly with the number of objects stored in the object database, and c) computational efficiency – it runs in near real-time. In contrast to the traditional affine-invariant detectors/descriptors which are local and not discriminative for texture-less objects, our method is based on line segments around which it computes semi-global descriptor by encoding gradient information in scale and rotation invariant manner. It relies on both texture and shape information and is, therefore, suited for both textured and texture-less objects. The descriptor is integrated into efficient object detection procedure which exploits the fact that the line segment determines scale, orientation and position of an object, by its two endpoints. This is used to construct several effective techniques for object hypotheses generation, scoring and multiple object reasoning; which are integrated in the proposed object detection procedure. Thanks to its ability to detect objects even if only one correct line match is found, our method allows detection of the objects under heavy clutter and occlusions. Extensive evaluation on several public benchmark datasets for texture-less and textured object detection, demonstrates its scalability and high effectiveness.

Object-Independent Grasping in Heavy Clutter

When grasping objects in a cluttered environment, a key challenge is to find appropriate poses to grasp effectively. Accordingly, several grasping algorithms based on artificial neural networks have been developed recently. However, these methods require large amounts of data for learning and high computational costs. Therefore, we propose a depth difference image-based bin-picking (DBP) algorithm that does not use a neural network. DBP predicts the grasp pose from the object and its surroundings, which are obtained through depth filtering and clustering. The object region is estimated by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and a depth difference image (DDI) that represents the depth difference between adjacent areas is defined. To validate the performance of the DBP scheme, bin-picking experiments were conducted on 45 different objects, along with bin-picking experiments in heavy clutters. DBP exhibited success rates of 78.6% and 83.3%, respectively. In addition, DBP required a computational time of approximately 1.4 s for each attempt.

Robust Detection of Presence of Individuals in an Indoor Environment Using IR-UWB Radar

In this paper, we propose a robust detection method to determine the presence of individuals in an indoor environment by exploiting an impulse-radio ultra-wideband (IR-UWB) radar. Detection of the presence of individuals in an indoor environment using IR-UWB is not a trivial problem because real indoor environments provide heavy clutter components and spurious ones due to multipath effects. The study mainly discusses two difficulties involved in detecting an individual in an indoor environment, namely, how to reduce the clutter in an indoor environment and how to detect a very low radar cross section (RCS) target, i.e., an individual lying down. To reduce clutter components in indoor environments, we investigated several clutter reduction techniques in terms of detecting a standing individual and an individual lying down. However, even after clutter reduction, detection of an individual lying down continues to pose a challenge. Thus, we devised a novel two-stage detection scheme that first involves detection in the range domain, and then in the frequency domain, thereby resulting in good detection performance in terms of a high detection rate and a low false alarm rate. The proposed method was demonstrated by experiments in indoor environments, and the results indicate that its performance is robust in various scenarios.

Infrared small target tracking algorithm based on temporal-spatial structure sparse Bayesian estimation

The tracking of infrared small target may be unstable due to the interference of background clutter and imaging noise. Moreover, the changing target appearance could degrade the tracking robustness. How to estimate the target spatial-temporal structure is the key to enhance the tracking stability for appearance changing small target under heavy clutter. This paper proposes a robust target tracking algorithm based on sparse representation and Bayesian inference that can estimate and predict the target spatial-temporal structure. Firstly, the small target signal is sparsely decomposed on the generalized Gaussian target over-complete dictionary (GGTOD). In this way the spatial structure information of small target is extracted from the noised and clutter contaminated infrared image. Then, according to the long term observations the long term temporal-spatial structure distribution of the target is established. Meanwhile the short term target temporal-spatial structure distribution is built according to the short term observations. Finally, the long term structure distribution and the short term structure distribution are combined by Bayesian inference to estimate and predict the target temporal-spatial structure in the next frame. By estimating the temporal change of target spatial structure, the proposed method achieves outstanding adaptability to the changing small target and robustness to clutter disturbance. Experiments on various infrared sequences show that the proposed method not only can estimate and predict the temporal-spatial structure of small target accurately but also can track the appearance changing small target stably under the inference of heavy clutter and noise.

Power Line Detection by Pyramidal Patch Classification

Obstacle recognition, especially power line detection, is very important for the safety of unmanned aerial vehicle flight. Current methods for power line detection mainly rely on the assistance of spatial context, such as tower-line correlation. These methods tend to produce low detection rates without auxiliaries while high false alarm rates due to heavy clutters caused by complicated backgrounds. In this paper, we propose a pyramidal patch classification framework that explicitly excludes the clutters without any extra auxiliaries. This framework enables good balance between detection precision and time-critical requirement; thanks to the proposed hierarchical patches partition and selection strategy. Accordingly, we design a new spatial grid pooling layer for our convolutional-neural-networks-based classifier, which is trained on the set of pyramidal patches. The final power lines are obtained by line detection in each patch of the smallest size, coupled with a line–line correlation procedure. Our experiments show that the proposed method can eliminate most false alarms and obtain a high detection rate with low computational cost.

Fast Detection Method for Low-Observable Maneuvering Target via Robust Sparse Fractional Fourier Transform

In this letter, a novel fast detection algorithm, known as robust sparse fractional Fourier transform (RSFRFT), is proposed for low-observable maneuvering target detection in a clutter background. The discrete FRFT (DFRFT)-based detection method is time-consuming for large data volumes and the detection performance of sparse FRFT (SFRFT)-based algorithm will be significantly degraded in a heavy clutter background. Using two levels of detection, the defects of DFRFT and SFRFT algorithms are overcome using the proposed algorithm. The first-level detection is performed on the subsampled spectrum to estimate the target frequencies. The second-level detection is carried out after reconstruction for target detection. The simulation analysis and experiments using marine radar data show that the proposed method can achieve a good detection performance for low-observable maneuvering target detection in the clutter background with lower computational complexity.

Tracking and Estimation of Multiple Cross-Over Targets in Clutter.

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations.

A Variable-Diameter-Arc-Helix Radon Transform for Detecting a Near Space Hypersonic Maneuvering Target

For detection and tracking of a near space hypersonic maneuvering target in a heavy clutter environment, a novel transform domain method called Variable-Diameter-Arc-Helix Radon Transform (VDAH-RT) is proposed as a tool to integrate observations along the maneuvering path. Considering the 3D maneuvering trajectory is like a variable diameter arc helix, the trajectory of near space hypersonic target can be modeled by a variable diameter arc helix. Since the maneuvering parameters are unknown, variable diameter arc helixes with different parameters can be used to match the maneuver track. Using the idea of Radon Transform(RT), the problem of maneuvering trajectory detection and tracking is transformed into the energy peak detection problem in VDAH-RT parameter domain. This Variable-Diameter-Arc-Helix Radon Transform (VDAH-RT) technique extends the RT or HT(Hough Transform) for straight-line features detection to an arbitrary curve trajectory. The results demonstrate that the proposed algorithm provides great advantage in the detection and tracking of a hypersonic maneuvering dim target.

Particle Filter for Tracking with Pulsed Doppler Radar in Heavy Clutter Environment

Particle Filter for Tracking with Pulsed Doppler Radar in Heavy Clutter Environment

Infrared Small Target Detection Based on Flux Density and Direction Diversity in Gradient Vector Field

The existing small target detection methods may suffer serious false alarm rate and low probability of detection in the situation of intricate background clutter. To cope with this problem, a novel small target detection method is proposed in this paper. Initially, the infrared image is transformed to the infrared gradient vector field (IGVF), where some new distinctive characters of the target and background clutter can be exploited. The small targets show as sink points, while the heavy clutter illustrates high direction coherence in IGVF. Then, the multiscale flux density (MFD) is proposed to quantify the extent of sink point character. In the MFD map, the small targets can be well enhanced and background clutters can be suppressed simultaneously. After that, by analyzing the coherence of heavy clutter shown in the IGVF, the gradient direction diversity (GDD) is presented. The residual noise caused by the heavy clutter in IGVF can be further suppressed by GDD. Finally, an adaptive threshold is adopted to separate the targets. Extensive experiments, including both real data and synthesized data, show that the proposed method outperforms other state-of-the-art methods, especially for infrared images with complex background clutter. Moreover, the experiments prove that the proposed method can work stably for different small target quantities, distances between adjacent targets, target shapes, and noise types with reasonable computational cost.

A Track Initiation Method for the Underwater Target Tracking Environment

A novel efficient track initiation method is proposed for the harsh underwater target tracking environment (heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method (TSEPM). Track initiation demands that the method should determine the existence and initial state of a target quickly and correctly. Heavy clutter and large measurement errors certainly pose additional difficulties and challenges, which deteriorate and complicate the track initiation in the harsh underwater target tracking environment. There are three primary shortcomings for the current track initiation methods to initialize a target: (a) they cannot eliminate the turbulences of clutter effectively; (b) there may be a high false alarm probability and low detection probability of a track; (c) they cannot estimate the initial state for a new confirmed track correctly. Based on the multiple hypotheses tracking principle and modified logic-based track initiation method, in order to increase the detection probability of a track, track splitting creates a large number of tracks which include the true track originated from the target. And in order to decrease the false alarm probability, based on the evaluation mechanism, track pruning and track merging are proposed to reduce the false tracks. TSEPM method can deal with the track initiation problems derived from heavy clutter and large measurement errors, determine the target’s existence and estimate its initial state with the least squares method. What's more, our method is fully automatic and does not require any kind manual input for initializing and tuning any parameter. Simulation results indicate that our new method improves significantly the performance of the track initiation in the harsh underwater target tracking environment.

Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing

This paper proposes a spectrum zoom processing based target detection algorithm for detecting the weak echo of low-altitude and slow-speed small (LSS) targets in heavy ground clutter environments, which can be used to retrofit the existing radar systems. With the existing range-Doppler frequency images, the proposed method firstly concatenates the data from the same Doppler frequency slot of different images and then applies the spectrum zoom processing. After performing the clutter suppression, the target detection can be finally implemented. Through the theoretical analysis and real data verification, it is shown that the proposed algorithm can obtain a preferable spectrum zoom result and improve the signal-to-clutter ratio (SCR) with a very low computational load.

Online object tracking by sparse and structural model

The objects tracking often meets the phenomenon, such as part or heavy occlusion, illumination variation, clutter background and scale variation and so on, during the tracking process, and which are the main challenge for object tracking. In this work, an online object tracking algorithm using local structural model with overlapped patches is proposed, the target is represented local structural feature with sparsity, furthermore, the sparse coefficients are pooled by the \(L_{2}\)-pooling method, and the target can be located more accurately and the occlusion problem can be better handled with this strategy, In addition, the paper develops an adaptive updating strategy based on increment subspace learning and sparse representation, this not only helps weaken the influence of illumination but also reduces the possibility of drifting. Numerous experiments demonstrate that the proposed algorithm performs more robustly and effectively against several state-of-the-art algorithms.

Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and non-local priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background non-local self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entry-wise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the element-wise reweighted higher-order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.

Improving RPCA-Based Clutter Suppression in GPR Detection of Antipersonnel Mines

Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often obscures the APM response. In this letter, a novel method combining migration imaging with the low-rank and sparse representation method to suppress clutter and extract target image is presented. The proposed method first focuses and strengthens the target response with migration imaging. Then, since the focused target response and clutter, respectively, constitute the sparse component and the low-rank component of the recorded data, the recently proposed robust principal component analysis (RPCA) can be applied to the recorded data to separate the target response (sparse component) from the clutter (low-rank component). Numerical simulation and experiments with real GPR systems are conducted. Results demonstrate the effectiveness of the proposed method in improving signal-to-clutter ratio and retrieving geometrical information of the target, which permits a better APM identification in heavy clutter environment.

Efficient TR‐TBD algorithm for slow‐moving weak multi‐targets in heavy clutter environment

In this study, the authors present an efficient time-dimension-reduced track-before-detect (TR-TBD) processor for slow-moving weak multi-targets detection in strong clutter environment. In their proposed framework, they elaborate observations from multiple frames (or scans) and resample them in time direction, then distinguish the slow-moving targets from the clutter in the radon parameter domain by exploiting the fact that different velocities of targets have different skewing angles corresponding to their tracks in the range–time (range–pulse) plane. To further enlarge the skewing angles differences between the slow-moving targets and the clutter, TR-TBD is proposed by incorporating the time-dimension reduction operator. This is very helpful to amplify the skewing angle of slow-moving targets, while the improvement is very small for the clutter. Therefore, it is much convenient to figure out the slow-moving weak targets from heavy clutter environment based on their amplified skewing angle differences by setting proper threshold. After detecting the targets, CLEAN-based track recovery method is proposed to eliminate the false tracks and recover the true tracks. Experimental results on real-data demonstrate that the proposed algorithm can detect the closely spaced targets and eliminate the false tracks under low signal-to-noise ratio and signal-to-clutter ratio.