Presence Of Clutter Research Articles

Active reconfigurable intelligent surface‐aided multiple‐input‐multiple‐output radar detection in the presence of clutter

AbstractReconfigurable intelligent surface (RIS) refers to a two‐dimensional smart surface consisting of massive number of reflecting elements. Both active RIS (ARIS) and passive RIS (PRIS) are promising technologies that can adapt and reconfigure the wireless environment, enhancing the reliability and capacity of wireless networks. The authors focus on enhancing the detection ability of ARIS/PRIS‐aided multiple‐input‐multiple‐output radar system in the presence of signal‐dependent clutter. Constant‐envelope constraint is imposed on the sought radar waveforms to improve the practicability of the radar system. To tackle the resultant non‐convex problem, a cyclic method based on minorisation–maximisation and alternating direction method of multipliers is derived to iteratively optimise the receive filters, the transmit waveforms, and the RIS coefficients. The major finding is that the distance between the radar and the ARIS has little to no impact on the radar signal‐to‐interference‐plus‐noise ratio (SINR). For the PRIS‐aided radar system, a simplified model can significantly improve the operational efficiency and has little impact on the radar SINR. Numerous results verify the effectiveness of the proposed algorithm.

A Hybrid Integration Method Based on SMC-PHD-TBD for Multiple High-Speed and Highly Maneuverable Targets in Ubiquitous Radar

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method’s capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system.

Characterization of River Width Measurement Capability by Space Borne GNSS-Reflectometry

In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data are generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using this predictive relationship indicates that, for simplistic river scenes, the SNR over the river is predictive of the river width to within +/−5 m. The presence of clutter (surrounding water bodies) within ~500 m of a river causes perturbations in the SNR measured over the river, which can render the river width retrievals unreliable. The results of this study indicate that, for isolated, straight rivers, GNSS-R data are able to measure river widths as narrow as 160 m with ~3% error.

Drivers’ Ability to Detect Traffic Signs on Curves with the Presence of Clutter in the Visual Field: A Systematic Review

Road accidents are a leading cause of death worldwide. Driver performance and safety can be compromised by various factors, including those related to driver behavior, the road and driving environment, as well as the vehicle itself. Traffic signs play a vital role in informing drivers and increasing their situational awareness. Therefore, it is crucial to detect and understand traffic signs clearly without adding unnecessary workload or compromising driving safety and performance. However, when traffic signs are placed on curves or in visually cluttered environments, or both together, it can increase the demands of driving and potentially compromise safety. A review was conducted to explore recent research on drivers’ ability to detect traffic signs, driving performance, and visual search in curved environments with visual clutter. The analysis revealed that billboards, particularly electronic ones, are generally distracting and may adversely affect driving performance and safety. Additionally, a majority of the reviewed studies on driving on curves indicated decreased performance and a high mental workload. To conclude, this review aims to enhance understanding of driving safety and performance when navigating horizontal curves. It aims to offer recommendations to road engineers concerning the optimal placement of traffic signs and billboards, particularly around curved sections. Generally, it is recommended to minimize the placement of billboards and unnecessary signs on curves to reduce visual clutter and alleviate the driver’s workload when searching for the crucial signs, and thus minimize distractions.

Signal Design and Multiple Target Detection in Presence of Clutter in Pulsed FDA Radar

Signal Design and Multiple Target Detection in Presence of Clutter in Pulsed FDA Radar

Performance analysis of signal processing algorithms for wall clutter mitigation and contrast target detection

AbstractThe presence of clutter in through‐the‐wall images significantly degrades image quality and hampers the performance of algorithms used for data processing in tasks such as target detection, identification, or reconstruction. Real building walls exhibit inherent inhomogeneities, featuring varying frequency and spatial properties that defy the assumption of a smooth surface. Consequently, they produce nonuniform wall surface reflections at each scanning position. Furthermore, these walls often incorporate supply pipes that introduce substantial clutter, which, although stronger than the target responses, remains weaker than the wall surface clutter. Additionally, this clutter often exhibits signatures similar to those of the target. In real‐world scenarios, target reflections can manifest as wide, flat hyperbolas or nearly straight lines, and they may be positioned near the wall. Consequently, distinguishing targets from clutter becomes a complex challenge. Various clutter reduction methods have been proposed in recent years, showing varying degrees of success. However, the effectiveness of these methods in the literature is inconsistent and sometimes contradictory. To address this issue, a comprehensive investigation of well‐established clutter reduction methods was conducted to evaluate their performance under identical conditions. These methods were rigorously assessed using practical radar‐measured data acquired in the presence of actual building wall materials and contrasting targets. Evaluation criteria include the target‐to‐clutter ratio and peak signal‐to‐noise ratio. The results of the experiment revealed that independent component analysis outperformed other clutter reduction methods, demonstrating superior performance in mitigating clutter and enhancing target detection.

Isolating Signals in Passive Non-Line-of-Sight Imaging using Spectral Content.

In real-life passive non-line-of-sight (NLOS) imaging there is an overwhelming amount of undesired scattered radiance, called clutter, that impedes reconstruction of the desired NLOS scene. This paper explores using the spectral domain of the scattered light field to separate the desired scattered radiance from the clutter. We propose two techniques: The first separates the multispectral scattered radiance into a collection of objects each with their own uniform color. The objects which correspond to clutter can then be identified and removed based on how well they can be reconstructed using NLOS imaging algorithms. This technique requires very few priors and uses off-the-shelf algorithms. For the second technique, we derive and solve a convex optimization problem assuming we know the desired signal's spectral content. This method is quicker and can be performed with fewer spectral measurements. We demonstrate both techniques using realistic scenarios. In the presence of clutter that is 50 times stronger than the desired signal, the proposed reconstruction of the NLOS scene is 23 times more accurate than typical reconstructions and 5 times more accurate than using the leading clutter rejection method.

Multi-Hypothesis Marginal Multi-Target Bayes Filter for a Heavy-Tailed Observation Noise

A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in the filter recursion. It uses the Student’s t distribution to model the heavy-tailed non-Gaussian observation noise, and employs the variational Bayes technique to acquire the approximate distributions of individual targets. K-best hypotheses, obtained by minimizing the negative log-generalized-likelihood ratio, are used to establish the existence probabilities and PDFs of targets in the filter recursion. Experimental results indicate that the proposed filter achieves better tracking performance than other filters.

Constant-Modulus Waveform Design for Dual-Function Radar-Communication Systems in the Presence of Clutter

We investigate the constant-modulus (CM) waveform design for dual-function radar communication systems in the presence of clutter. To minimize the interference power and enhance the target acquisition performance, we use the signal-to-interference-plus-noise-ratio as the design metric. In addition, to ensure the quality of the service for each communication user, we enforce a constraint on the synthesis error of every communication signals. An iterative algorithm, which is based on cyclic optimization, Dinkinbach's transform, and alternating direction of method of multipliers, is proposed to tackle the encountered non-convex optimization problem. Simulations illustrate that the CM waveforms synthesized by the proposed algorithm allow to suppress the clutter efficiently and control the synthesis error of communication signals to a low level.

Gaussian Process Gaussian Mixture PHD Filter for 3D Multiple Extended Target Tracking

This paper addresses the problem of tracking multiple extended targets in three-dimensional space. We propose the Gaussian process Gaussian mixture probability hypothesis density (GP-PHD) filter, which is capable of tracking multiple extended targets with complex shapes in the presence of clutter. Our approach combines the Gaussian process regression measurement model with the probability hypothesis density filter to estimate both the kinematic state and the shape of the targets. The shape of the extended target is described by a 3D radial function and is estimated recursively using the Gaussian process regression model. Furthermore, we transform the recursive Gaussian process regression problem into a state estimation problem by deriving a state space model such that the estimation of the extent can be integrated into the kinematic part. We derive the predicted likelihood function of the PHD filter and provide a closed-form Gaussian mixture implementation. To evaluate the performance of the proposed filter, we simulate a typical extended target tracking scenario and compare the GP-PHD filter with the traditional Gamma Gaussian Inverse-Wishart PHD (GGIW-PHD) filter. Our results demonstrate that the proposed algorithm outperforms the GGIW-PHD filter in terms of estimating both kinematic states and shape. We also investigate the impact of the measurement rates on both filters; it is observed that the proposed filter exhibits robustness across various measurement rates, while the GGIW-PHD filter suffers under low-measurement-rate conditions.

Influences of upsetting on the propagation characteristic of the elastic wave in the countersunk head riveted joints

The countersunk head riveted joints (CHRJ) have extensive use in the aerospace and marine industry. When testing the CHRJ with ultrasonic technology, the signal near the initial pulse cannot be used to detect near-surface defects due to the blind zone of the ultrasonic testing (UT). Moreover, the presence of clutters between the first and second bottom echoes can significantly affect the effectiveness of the UT. Therefore, it is crucial to investigate the propagation characteristic of ultrasonic waves in the CHRJ. The relationship between the interface stiffness and the upsetting length was obtained because of the multi-interface feature of the CHRJ. The propagation characteristic of ultrasonic waves in the CHRJ was investigated using the finite element simulation based on the spring model of the imperfect interface. The simulation results showed that shank echoes would generate at the surface of the rivet shank when the elastic wave propagates in the shank of the CHRJ. The shank echoes would propagate obliquely downward and reflect repeatedly on the surface of the rivet shank. Furthermore, the amplitude of the first shank echo would decrease with the increase of the upsetting length due to the increase of contact stress and deformation. To verify the simulation results, the experiments were carried out using the piezoelectric ultrasonic transducer, and the experimental results matched well with the simulation results. This paper can provide a reference for the ultrasonic testing of defects in the CHRJ.

Adaptive behavior in sonar prey tracking tasks: Empirical data and modeling

Echolocating bats can discriminate small differences in target range (Simmons, 1973) and show a clutter interference zone along the range axis of 8–9 cm (Simmons et al., 1988). Because bats use an active sensing system, they can employ various strategies to mitigate the effects of clutter. We studied clutter rejection behaviors of the big brown bat, Eptesicus fuscus, as it tracked a moving target under controlled conditions. We also created data-driven models using control theory and system identification techniques to evaluate oscillating target motion tracking strategies. We trained bats to rest on a stationary platform and track a moving tethered mealworm in two conditions (1) approaching and passing by stationary clutter objects at varying distances and angular offsets, and (2) moving in sinusoidal patterns in an open room. Data from the first experimental condition revealed adaptive echolocation call features and head movements that enabled target tracking in the presence of clutter. Under the oscillating target condition, we analyzed adaptive echolocation behavior to create a data-driven model that maps changes in target motion with sonar call rate. The results of this study demonstrate the bat’s use of active motor control that separates targets from clutter and to track complex motion trajectories.

Probabilistically Robust Design of Radar Waveform-Filter for Extended Target Detection in the Presence of Clutter

Probabilistically Robust Design of Radar Waveform-Filter for Extended Target Detection in the Presence of Clutter

Adaptive mixture approximation for target tracking in clutter

Target tracking is a state estimation problem common in many practical scenarios like air traffic control, autonomous vehicles, marine radar surveillance and so on. In a Bayesian perspective, when phenomena like clutter are present, most existing tracking algorithms must deal with association hypotheses which may grow in number over time. In that case, the posterior state distribution can quickly become computationally intractable, and approximations must necessarily be introduced. In this work, the impact of the number of hypotheses and of reduction procedures is investigated both in terms of computational resources and tracking performances. For this purpose, a recently developed adaptive mixture model reduction algorithm is considered in order to assess its performances when applied to the problem of single object tracking in the presence of clutter and to provide some interesting insights into the addressed problem.

Multiple extended target tracking based on gamma box particle and labeled random finite sets

A gamma box particle (GBP) implementation of the extended target generalized labeled multi-Bernoulli (ET-GLMB) filter is proposed in this paper, named the ET-GBP-GLMB filter. The filter can simultaneously estimate the unknown number, measurement rate states, kinematics states, extension states and tracks of extended targets in the presence of clutter, missed detection and data association uncertainty. In order to obtain the GLMB recursion of the ET-GBP-GLMB filter, an appropriate measurement likelihood function that comprehensively considers the measurement rate state, kinematics state and extension state of each extended target is introduced for a given measurement partition cell, and the specific implementation steps of the filter are derived with necessary interval operations and approximations. Further, an extended target GBP labeled multi-Bernoulli (ET-GBP-LMB) filter is proposed as an effective approximation of the ET-GBP-GLMB filter. The advantages and disadvantages of the two proposed filters are presented in simulation examples. The simulation results show that, compared with the gamma sequential Monte Carlo (GSMC) implementation of the ET-GLMB filter, the GBP implementation of the ET-GLMB filter can effectively reduce the number of particles and the amount of computation. Compared with the ET-GBP-GLMB filter, the ET-GBP-LMB filter can further reduce the computational burden. Compared with the extended target box particle probability hypothesis density (ET-BP-PHD) filter, the two proposed filters can obtain better estimation results.

SINR- and MI-Based Double-Robust Waveform Design.

Owing to cognitive radar breaking the open-loop receiving-transmitting mode of traditional radar, adaptive waveform design for cognitive radar has become a central issue in radar system research. In this paper, the method of radar transmitted waveform design in the presence of clutter is studied. Since exact characterizations of the target and clutter spectra are uncommon in practice, a single-robust transmitted waveform design method is introduced to solve the problem of the imprecise target spectrum or the imprecise clutter spectrum. Furthermore, considering that radar cannot simultaneously obtain precise target and clutter spectra, a novel double-robust transmitted waveform design method is proposed. In this method, the signal-to-interference-plus-noise ratio and mutual information are used as the objective functions, and the optimization models for the double-robust waveform are established under the transmitted energy constraint. The Lagrange multiplier method was used to solve the optimal double-robust transmitted waveform. The simulation results show that the double-robust transmitted waveform can maximize SINR and MI in the worst case; the performance of SINR and MI will degrade if other transmitted waveforms are employed in the radar system.

Improvement in the Tracking Performance of a Maneuvering Target in the Presence of Clutter.

The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method’s root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest.

Pose prediction of textureless objects for robot bin picking with deep learning approach

Six dimensional pose prediction of objects for robotic manipulation has received much attention in industrial applications. Despite considerable research on improving the performance of pose estimation, the problem remains challenging for industrial objects because of their textureless and mostly homogeneous properties in the presence of clutter and occlusion. This article proposes deep learning-based pose estimation by jointly detecting, segmenting and pose predicting. The binary classification branch as an attention module is suggested to improve the accuracy of instance segmentation. Using the instance information, the initial pose estimation network is designed by fusing the depth information into the grayscale image to strengthen the geometric features. Then, to obtain a highly accurate pose, an iterative network is constructed with point clouds as inputs to refine the initial pose. The networks are implemented to predict the pose of textureless object on the synthetic and real scenes. Experimental results indicate the pose estimation method is efficient and robust to pose prediction of textureless objects in cluttered and occluded scenes.

Clutter Substantially Reduces Selectivity for Peripheral Faces in the Macaque Brain.

According to a prominent view in neuroscience, visual stimuli are coded by discrete cortical networks that respond preferentially to specific categories, such as faces or objects. However, it remains unclear how these category-selective networks respond when viewing conditions are cluttered, i.e., when there is more than one stimulus in the visual field. Here, we asked three questions: (1) Does clutter reduce the response and selectivity for faces as a function of retinal location? (2) Is the preferential response to faces uniform across the visual field? And (3) Does the ventral visual pathway encode information about the location of cluttered faces? We used fMRI to measure the response of the face-selective network in awake, fixating macaques (two female, five male). Across a series of four experiments, we manipulated the presence and absence of clutter, as well as the location of the faces relative to the fovea. We found that clutter reduces the response to peripheral faces. When presented in isolation, without clutter, the selectivity for faces is fairly uniform across the visual field, but, when clutter is present, there is a marked decrease in the selectivity for peripheral faces. We also found no evidence of a contralateral visual field bias when faces were presented in clutter. Nonetheless, multivariate analyses revealed that the location of cluttered faces could be decoded from the multivoxel response of the face-selective network. Collectively, these findings demonstrate that clutter blunts the selectivity of the face-selective network to peripheral faces, although information about their retinal location is retained.SIGNIFICANCE STATEMENT Numerous studies that have measured brain activity in macaques have found visual regions that respond preferentially to faces. Although these regions are thought to be essential for social behavior, their responses have typically been measured while faces were presented in isolation, a situation atypical of the real world. How do these regions respond when faces are presented with other stimuli? We report that, when clutter is present, the preferential response to foveated faces is spared but preferential response to peripheral faces is reduced. Our results indicate that the presence of clutter changes the response of the face-selective network.

Event-driven proto-object based saliency in 3D space to attract a robot’s attention

To interact with its environment, a robot working in 3D space needs to organise its visual input in terms of objects or their perceptual precursors, proto-objects. Among other visual cues, depth is a submodality used to direct attention to visual features and objects. Current depth-based proto-object attention models have been implemented for standard RGB-D cameras that produce synchronous frames. In contrast, event cameras are neuromorphic sensors that loosely mimic the function of the human retina by asynchronously encoding per-pixel brightness changes at very high temporal resolution, thereby providing advantages like high dynamic range, efficiency (thanks to their high degree of signal compression), and low latency. We propose a bio-inspired bottom-up attention model that exploits event-driven sensing to generate depth-based saliency maps that allow a robot to interact with complex visual input. We use event-cameras mounted in the eyes of the iCub humanoid robot to directly extract edge, disparity and motion information. Real-world experiments demonstrate that our system robustly selects salient objects near the robot in the presence of clutter and dynamic scene changes, for the benefit of downstream applications like object segmentation, tracking and robot interaction with external objects.