Heavy Clutter Research Articles

Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

ABSTRACTMoving target detection in a heavy clutter area is a challenging problem in the field of radar automatic target detection. Within the framework of track posture hypothesis testing, the radar‐blips‐oriented target detection algorithms named ErgSad and RanSaD are proposed for single target and formation targets detection, respectively in this paper. With the assumption that target motion is a linear Gaussian process in a short time, the algorithms use dual blips in different scans to generate the track hypothesis and test the hypothesis with residual blips in other scans to determine the existence of moving targets. To reduce time consumption and minimize the error of model estimation, the sampling rules and the supporting domain related to timestamps of track hypothesis models were designed. Simulation data experiments show that the proposed algorithms have more superior performance to detect targets than the state‐of‐the‐art algorithms in the serious clutter environment.

Variational Tracking and Redetection for Closely-Spaced Objects in Heavy Clutter

Variational Tracking and Redetection for Closely-Spaced Objects in Heavy Clutter

Bernoulli filter with signal coherent integration for target detection and tracking in clutter environments

In practical applications, the target maneuvers might lead to high-order range migration (RM) and Doppler frequency migration (DFM) effects, which seriously degrade the performance of long-time coherent integration. In this case, the target detection and tracking performance will deteriorate due to insufficient signal energy and heavy clutter. To address this problem, a recursive algorithm based on the Bernoulli filter with long-time coherent integration is proposed in this paper for maneuvering target detection and tracking in low signal-to-noise ratio environments. The Keystone transform is used to correct the linear RM. Then, the matched filtering is performed based on the state to eliminate the high-order RM and DFM. According to the focused peaks in the range-time azimuth-Doppler domain, the target existence probability and dynamic states are estimated using an amplitude-aided Gaussian mixture Bernoulli filter. Simulations are performed to demonstrate the effectiveness of the proposed algorithm. Compared with traditional coherent integration methods based on parameter searching, the proposed algorithm acquires close integration performance with less computational complexity, and the target detection and tracking performance is improved with the aid of integrated signal amplitude.

A group target track‐before‐detect approach using two‐stage strategy with maximum‐likelihood probabilistic data association

AbstractIn tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low‐observable environments by the increase in false alarm rates. The track‐before‐detect (TBD) is an advanced technology for detecting and tracking low‐observable targets, effectively mitigating data association problems by integrating multi‐frame echo data. However, the existing multi‐target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum‐likelihood probabilistic data association (GT‐ML‐PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low‐observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter.

Infrared small target detection using Homogeneity-weighted local patch saliency

Detecting infrared small targets (IRSTs) submerged in the complex back- grounds with heavy clutters is a challenging work of great significance in many fields. Inspired by representation capability of the superpixel, this paper presents a human visual system (HVS) based method called Homogeneity weighted local patch saliency (HWLPS). Firstly, the image is transformed into a superpixel graph and a compound flux field of the thermal intensity and gradient. Next, IRST component saliency and superpixel homogeneity are quantified by the morphing gradient direction diversity (GDD) metric and the inter-patch homogeneity (IPH) successively. Finally, the local contrast aggregation fuses the morphing GDD and the foreground saliency of image patches according to the superpixel graph. Experiments on various real IR image datasets show that, the proposed algorithm outperforms the state-of-the-art methods with robustness and stability against intricate back- grounds.

Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint.

Transmit beamforming (TBF) provides the capability of focusing illuminating power in the desired directions while reducing the emitting power in undesired directions. It is significantly important in low-altitude and slow-speed small (LSS) radar, which usually suffers from heavy clutter and rapidly changing interference on the near-ground side. Due to nonideal factors such as an inaccurate target direction and array gain-phase error, the robustness of TBF is also necessary to consider in practical applications. In this paper, we provide a robust TBF method that enables sidelobe control in preset regions and possesses high transmit efficiency in virtue of the peak-to-average-power ratio (PAPR) constraint on transmit weights. To achieve robustness, a norm upper bound is introduced to limit the fluctuation of transmit weights, and the steering vector mismatch is also considered by using a spherical uncertainty set surrounding the nominal steering vector. As the proposed robust TBF is nonconvex because of the nonconvexity of both the objective function and constraints, we translate it into a series of convex subproblems via several kinds of convex relaxation schemes. In particular, based on the special structure of the objective function and constraints, the translation of the nonconvex problem into a tractable SOCP problem is realized by using the combination of the triangle inequality and Cauchy-Schwartz inequality. Numerical results demonstrate the improvement in the efficiency and robustness of the proposed TBF method in comparison with traditional TBF methods.

An Adaptive and Scalable Multi-Object Tracker Based on the Non-Homogeneous Poisson Process

This paper proposes a new adaptive framework for tracking multiple objects in the presence of data association uncertainty and heavy clutter, either with or without knowledge of the measurement rates and/or target shapes. Built upon an online Gibbs sequential Markov chain Monte Carlo sampling scheme, the adaptive tracker is Bayesian optimal and robust, requiring no additional approximations or measurement partition steps. With a non-homogeneous Poisson process measurement model, our tracker can tackle the data association task with linear computational complexity. Meanwhile, we study generalised inverse Gaussian and inverse Wishart distributions for modelling Poisson rates and object shapes, respectively; these prior models ensure closed-form full conditionals in our online Gibbs sampling steps, under which object states and shapes can be jointly estimated with associations and Poisson rates in a parallel fashion. Furthermore, a fast Rao-Blackwellisation scheme for linear Gaussian dynamics is designed and demonstrated to significantly improve both tracking efficiency and accuracy. We validate the efficacy of our method on real and simulated data.

Multiple-Kernelized-Correlation-Filter-Based Track-Before-Detect Algorithm for Tracking Weak and Extended Target in Marine Radar Systems

This article addresses the problem of tracking weak and extended targets in the clutter for marine radar systems. In the proposal, multiple kernelized correlation filters (MKCFs) incorporate a low-threshold constant false alarm rate and segmentation model under the principle of multiframe track before detect (MF-TBD). By setting the similarity score of the correlation filter as the test statistic, the maximization of the integration over frames can be solved by the essentially exhaustive searching of the MKCF. Since the correlation filter measures the similarity of the intensity distributions in the extended templates, the proposed method can differentiate the weak target from the surrounding clutter and other targets at a close range. Compared to other amplitude-based MF-TBD methods, it performs better in detecting and tracking extended targets in the real-radar data with heavy clutter caused by floating sea ice and the simulations with Rayleigh and $K$-distributed sea clutters.

Infrared small target detection using kernel low-rank approximation and regularization terms for constraints

The robust detection of infrared (IR) small targets remains a challenging task. When targets are submerged in heavy clutter or disturbed by strong edge, it is difficult to extract targets and suppress background clutter simultaneously. In this paper, we present a kernel robust principal component analysis model for IR small target detection. Firstly, to overcome the interference of heavy clutter and strong edge, the kernel low-rank approximation is proposed to estimate the background component; Then, to further improve detection performance, the lp-norm is adopted to recover the sparse component, the total variation regularization is employed to smooth the background, and the graph Laplacian regularization is utilized as a sparse constraint to constrain the homogeneous target region. Finally, we derive an effective method to solve the proposed model based on the alternating direction method of multipliers. Experimental results not only show the proposed method can robustly and effectively detect IR small targets even when the targets are submerged in strong clutter or seriously disturbed by strong edge, but also confirm the proposed method is competitive with the state-of-the-art methods.

Entropy-Driven Morphological Top-Hat Transformation for Infrared Small Target Detection

Infrared small target detection is a key technique in an infrared system. In the past decade, many methods have concentrated on traditional top-hat transformation, which relies on the hand-crafted shape and value of structural elements. However, these methods are inevitably challenged by the following two aspects: first, the structural elements cannot suppress heavy clutter because the construction of structural elements is always according to the prior information of the target and unable to consider the feature of clutter. Second, adaptively extracting sufficient local feature information for background suppression is hard for the structural element. In this article, we propose an entropy-driven top-hat transformation with guided filter kernel for considering the features of both the clutters and background. First, we propose an entropy-driven top-hat transformation method with our proposed local mean entropy, which can be used to suppress clutter according to the local complex degree of clutter. Then, an adaptive structural element based on a guided filter kernel is further exploited to capture the local feature information of image for background suppression. Finally, an adaptive threshold is combined with our algorithm to achieve target detection in image sequences. The experimental results show that the proposed algorithm is not only robust for suppressing different kinds of backgrounds but can also obtain a higher value of the signal-to-clutter ratio gain and detection accuracy compared with some popular traditional baseline methods and related top-hat methods.

Multi-Target Track Initiation in Heavy Clutter

In the heavy clutter environment, the information capacity is large, the relationships among information are complicated, and track initiation often has a high false alarm rate or missing alarm rate. Obviously, it is a difficult task to get a high-quality track initiation in the limited measurement cycles. This paper studies the multi-target track initiation in heavy clutter. At first, a relaxed logic-based clutter filter algorithm is presented. In the algorithm, the raw measurement is filtered by using the relaxed logic method. We not only design a kind of incremental and adaptive filtering gate, but also add the angle extrapolation based on polynomial extrapolation. The algorithm eliminates most of the clutter and obtains the environment with high detection rate and less clutter. Then, we propose a fuzzy sequential Hough transform-based track initiation algorithm. The algorithm establishes a new meshing rule according to system noise to balance the relationship between the grid granularity and the track initiation quality. And a flexible superposition matrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0–1 voting method in traditional Hough transform. In addition, the algorithm allows the superposition matrixes of nonadjacent cycles to be associated to overcome the shortcoming that the track can’t be initiated in time when the measurements appear in an intermittent way. And a slope verification method is introduced to detect formation-intensive serial tracks. Last, the sliding window method is employed to feedback the track initiation results timely and confirm the track. Simulation results verify that the proposed algorithms can initiate the tracks accurately in heavy clutter.

Infrared Small UAV Target Detection Based on Residual Image Prediction via Global and Local Dilated Residual Networks

Thermal infrared imaging possesses the ability to monitor unmanned aerial vehicles (UAVs) in both day and night conditions. However, long-range detection of the infrared UAVs often suffers from small/dim targets, heavy clutter, and noise in the complex background. The conventional local prior-based and the nonlocal prior-based methods commonly have a high false alarm rate and low detection accuracy. In this letter, we propose a model that converts small UAV detection into a problem of predicting the residual image (i.e., background, clutter, and noise). Such novel reformulation allows us to directly learn a mapping from the input infrared image to the residual image. The constructed image-to-image network integrates the global and the local dilated residual convolution blocks into the U-Net, which can capture local and contextual structure information well and fuse the features at different scales both for image reconstruction. Additionally, subpixel convolution is utilized to upscale the image and avoid image distortion during upsampling. Finally, the small UAV target image is obtained by subtracting the residual image from the input infrared image. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art ones in detecting real-world infrared images with heavy clutter and dim targets.

Infrared Small Target Detection via Dynamic Image Structure Evolution

Infrared small target detection (IRSTD) is a challenging task, due to the scarce target feature, complex background interferences, and poor image quality. The existing studies handle the IRSTD problem by making specific distribution assumptions of target and background, which incurs two issues. First, the specific assumptions of background cannot always hold. Second, the distorted target distributions contaminated by heavy clutters and noise are difficult to model statically. This paper handles the problems from the discriminative and dynamic perspectives, and proposes an original mechanism named dynamic image structure evolution (DISE) and an DISE-derived single-frame IRSTD framework. First, we resolve IRSTD by a discriminative model without assuming specific background distributions, which is based on a mathematical definition of structure singularity modeling the ideal target structure. Second, to decouple the target distributions from interferences, DISE guides the distorted target to reveal potential structure singularity while suppress the interference signal through iterative procedures of structure collapse, intensity settlement, and collapse convergence. The three functional procedures of DISE perform their own duties regarding target enhancement and background suppression. Moreover, an innovative chain mechanism is introduced to propagate the structure field. Experiments on real data sets demonstrate the superiority of DISE against the state-of-the-art IRSTD methods.

Improved Ground Target Detecting System Based on Both Miniaturized FMCW Radar and Radiometer

The frequency-modulated continuous-wave (FMCW) radar is generally applied in ground target detecting devices mounted on small shells or projectiles because of its compact size. However, the FMCW radar often produces false alarms when detecting ground targets surrounded by heavy clutter. To overcome this problem, this paper proposes a ground target detection system based on both the miniaturized FMCW radar and the total power radiometer (TPR), consisting of the common millimeter-wave (MMW) front ends and an antenna. However, its intermediate frequency (IF) parts are separated using different frequency bands to miniaturize the entire system. The minimum detectable temperature (MDT) increases and the sensitivity is thus degraded because the TPR for the hybrid sensor inevitably includes an undesirable transmitter section owing to the widespread usage of the front ends with the radar. The proposed system employs optimization of the physical path delay and duplexing the IF band for the TPR and FMCW radar to improve the sensitivity of the TPR in the proposed hybrid sensors and reduce the system noise. The system includes a matching circuit and a voltage doubler to improve the sensitivity of the detector. Therefore, the MDT of TPR in the proposed hybrid system can be reduced to 47.5 K from 734.6 K in the initial design. The drop test demonstrates that the proposed hybrid sensor can reduce false alarms when compared to using either the FMCW radar or only the TPR.

An IR-UWB Multi-Sensor Approach for Collision Avoidance in Indoor Environments

This article aims to propose new techniques to detect and distinguish humans from moving machines in indoor environments. Although many research efforts have been already dedicated to humans’ indoor detection, most of the work has been focused on counting people and crowd measurement for consumer business applications. Our objective is to develop a reliable approach for humans’ indoor detection and localization aiming at avoiding collisions inside a mixed Industry 4.0 manned and unmanned environment to enhance personal and equipment safety and to prevent unwanted intrusions. An original aspect of our research is that we have worked on the real-time estimation of humans’ and moving machines’ positions while addressing the problems of multipath components and noise clutter detection. A multipulse constant false alarm rate detection algorithm is also proposed for removing the misdetections due to heavy clutter components in the indoor environment. Four impulse radio ultrawideband transceivers are placed in a specific geometry and data fusion is performed to reduce the influence of multipath and noise on the detection process. A convolutional neural network (CNN) is then used to extract the patterns corresponding to a moving machine and humans and classify them accordingly. Experiments have been carried out in two different indoor environments to demonstrate the performance of the proposed algorithms.

Infrared small target detection algorithm based on spatial dissimilarity weighted local contrast

To solve the problem that the local contrast algorithm is easily influenced by the heavy clutter background or strong noise and does not fully consider the spatial neighbourhood characteristics of the small target, a spatial dissimilarity weighted local contrast-based method (SDWLCM) for infrared small target detection is proposed in this paper. Firstly, the two-dimensional difference of the Gaussian filter with central excitation and lateral suppression characteristics is chosen to preprocess the original infrared image for removing the flat background and improving the signal-to-noise ratio (SNR) of the small target. Secondly, the spatial dissimilarity between the target and its surrounding backgrounds is designed for local contrast weighting to generate the contrast saliency map so that the heavy clutter background is greatly suppressed and the small target is further highlighted. Thirdly, the saliency map is segmented by the adaptive threshold to get the real targets. Experimental results show that, compared with other methods, the SDWLCM, which owns not only a higher SNR gain and a larger background suppression factor but also a higher detection rate and a lower false alarm rate, is confirmed to be an effective method to detect the small targets.

Analysis and Evaluation of Increasing the Throughput of Processors by Eliminating the Lobe’s Disorder

In modern radars, the design is such that radar vulnerability to noise disturbance is significantly reduced. This dramatic decrease is due to the fact that radars are always trying to increase their capability against the heavy clutter they receive from the ground. Radars use antenna sidelobes to reduce clutter. By reducing the antenna sidelobes, the vulnerability of the radar to the heavy clutches of the sidelobes is reduced, which also reduces the vulnerability to sidelobe disturbance. In this article, we try to examine the reduction of clutter causing the reduction of vulnerability to disorder.

Range‐ambiguity suppression for noise radar in cluttered environment via sparse recovery

Range ambiguity suppression is a significant problem in traditional pulse-Doppler radar systems, especially when the pulse repetition frequency is set high to avoid Doppler ambiguity. One possible way to deal with such problem is to apply the noise radar system, by transmitting pulse-agile waveforms and using a group of matched filters as the receiver to accumulate the power of unambiguous echoes while dispersing the range-ambiguous ones. However, when strong targets or heavy clutters exist, the dispersed ambiguous power is still high, which leads to high range-Doppler sidelobes and overwhelms the weak targets. This paper explores a new way to deal with the range-ambiguity suppression problem in noise radar. The range-ambiguity suppression as a sparse recovery problem is formulated, and then both the unambiguous and ambiguous targets (and/or clutters) can be simultaneously reconstructed. The proposed method can enhance the range-ambiguity suppression performance and synthetic an ambiguity-free range-Doppler map with a low sidelobe level, which ensures the weak target recovery performance even in a cluttered environment.

Detection and tracking of the trajectories of dynamic UAVs in restricted and cluttered environment

The unidentified number of unmanned aerial vehicles (UAVs) can execute aggressive maneuvers in the restricted and the cluttered environment. Therefore, it is difficult to detect and track the uncertain motion of the UAV target in such complex environment. In addition, multi-target tracking (MTT) algorithms such as joint data association approach faces various computational complexities that could exceeds the available computation resources. This paper develops a novel smoothing data association idea in a linear multi-target (LM) tracking based on integrated probabilistic data association (sLM-IPDA) algorithm that acts like a single target tracker in the MTT situation. The significant detection and tracking performance of a UAV are validated without a-prior information of the UAV’s initial position. The forward and backward tracks are initialized separately using sensor measurements received in each scan. The sLM-IPDA estimates the backward multi-tracks simultaneously associating backward tracks in a subsequent predicted forward track for fusion. Thus, a forward track state estimate is obtain using the smoothing (fusion) measurements. This significantly improves estimation accuracy for large number of cross-over targets in heavy clutter. Numerical assessments of the sLM-IPDA are verified using both simulation and experiment to demonstrate the application of the proposed algorithm.

Generalized likelihood ratio test for optical subpixel objects’ detection with hypothesis-dependent background covariance matrix

Much interest has arisen in the problem of detecting weak optical subpixel objects in a sequence of images immersed in a heavy homogeneous Gaussian clutter background. In optical systems, the presence of the objects changes the background plus the channel noise covariance matrix. Hence, this matrix may be different under null and alternative hypotheses. Because the maximum likelihood estimate of the background covariance matrix is not available when using the primary data set, we use an approximate estimate of a certain number of eigenvalues of the background covariance matrix that are available for estimation. We derived the general likelihood ratio test for the problem at hand and obtained the modified spectrum matching detector (MSpMD). Using the simulation results, we contrast it with well-known spectrum matching detectors and the hypothesis-independent matched subspace detectors. We also present experimental results of small subpixel objects detection on an agitated sea surface, using digital optical experiments that demonstrate the validation of the simulating results. We show the advantage of the MSpMD that allows to detect small subpixel floating objects on an agitated sea surface even with the same means and variances of the reflected signals from the sea surface and the object.