Articulated objects are prevalent in daily life and robotic manipulation tasks. However, compared to rigid objects, pose tracking for articulated objects remains an underexplored problem due to their inherent kinematic constraints. To address these challenges, this work proposes a novel point-pair-based pose tracking framework, termed PPF-Tracker. The proposed framework first performs quasi-canonicalization of point clouds in the SE(3) Lie group space, and then models articulated objects using Point Pair Features (PPF) to predict pose voting parameters by leveraging the invariance properties of SE(3). Finally, semantic information of joint axes is incorporated to impose unified kinematic constraints across all parts of the articulated object. PPF-Tracker is systematically evaluated on both synthetic datasets and real-world scenarios, demonstrating strong generalization across diverse and challenging environments. Experimental results highlight the effectiveness and robustness of PPF-Tracker in multi-frame pose tracking of articulated objects. We believe this work can foster advances in robotics, embodied intelligence, and augmented reality.

Category-level articulated object pose perception-encompassing both static pose estimation and dynamic pose tracking-is critical for embodied AI systems interacting with complex environments. Due to the inherent complexity and diverse motion structures of articulated objects, existing methods often exhibit limitations in adequately modeling kinematic constraints, handling self-occlusions, and meeting optimization requirements. Building upon EfficientCAPER (Yu et al., 2024), this work introduces CAPER++, a unified framework addressing these limitations through three key innovations: first, a joint-centric hierarchical model decomposes objects into a root part and constrained parts linked by joints, explicitly embedding kinematic constraints for geometrically consistent pose recovery. Second, an SE(3) manifold formulation leverages Lie algebra in the tangent space for singularity-free rotation representation and stable optimization, replacing error-prone direct regression. Third, for tracking, a proxy canonicalization strategy reformulates pose updates as SE(3) increment predictions relative to keyframes, enhanced by a dynamic keyframe mechanism to suppress drift. Extensive experiments on synthetic (ArtImage, PM-Videos), semi-synthetic (ReArtMix, ReArt-Videos), and real-world (RobotArm, RobotArm-Videos) benchmarks demonstrate state-of-the-art accuracy and robustness. CAPER++ achieves real-time inference (50 FPS) without post-processing, significantly advancing category-level articulated perception for real-world applications.

As a cost-effective and robust technology, automotive radar has seen steady improvement during the last years. Radio frequency (RF) images, serving as a radar data format with rich semantic information, have attracted considerable interest in radar object detection. Previous RF-based models heavily rely on convolutional neural networks, leading to the high computational cost. To solve this problem, we propose a model called Mask-RadarNet to fully utilize the hierarchical semantic features from the RF image sequences. Mask-RadarNet exploits the combination of interleaved convolution and attention operations in the encoder. In addition, patch shift is introduced to Mask-RadarNet for efficient spatial-temporal feature learning. By shifting part of patches with a specific mosaic pattern in the temporal dimension, Mask-RadarNet achieves competitive performance while reducing the computational burden of the spatial-temporal modeling. In order to capture the spatial-temporal semantic contextual information, we design the class masking attention module (CMAM) in our encoder. Moreover, a lightweight auxiliary decoder is added to our model to aggregate prior maps generated from the CMAM. Experiments on the CRUW dataset demonstrate that the proposed Mask-RadarNet achieves state-of-the-art performance with relatively lower computational complexity and fewer parameters.

The generalized direction detection (GDD) problem involves determining the presence of a signal of interest within matrix-valued data, where the row and column spaces of the signal (if present) are known, but the specific coordinates are unknown. Many detectors have been proposed for GDD, yet there is a lack of analytical results regarding their statistical detection performance. This paper presents a theoretical analysis of two adaptive detectors for GDD in scenarios with known spatial steering vectors. Specifically, we establish their statistical distributions and develop closed-form expressions for both detection probability (PD) and false alarm probability (PFA). Simulation experiments are carried out to validate the theoretical results, demonstrating good agreement between theoretical and simulated results.

In this article, we address the problem of clutter covariance matrix estimation for radar adaptive detection. Traditional estimation methods are usually based on specific models. However, performance will experience degradation in the presence of model mismatch, which occurs commonly in reality. Therefore, we resort to the data-driven deep learning method and construct a network based on the convolutional neural network (CNN) to estimate the clutter covariance matrix. Besides, due to the unavailable ground truth of the covariance matrix of measured data, simulated data are usually applied for training as a compromise. We design a loss function according to the whitening metric, which makes it possible to train the network directly by measured data. Compared with traditional covariance matrix estimators, the proposed network estimator has higher whitening ability. Moreover, we exploit the obtained covariance matrix estimations to an adaptive detector to evaluate the detection performance. Results with the Intelligent Pixel (IPIX) datasets show that the detector applying the network covariance matrix estimator gains a higher probability of detection (PD).

As one of the automotive sensors that have emerged in recent years, 4D millimeter-wave radar has a higher resolution than conventional 3D radar and provides precise elevation measurements. But its point clouds are still sparse and noisy, making it challenging to meet the requirements of autonomous driving. Camera, as another commonly used sensor, can capture rich semantic information. As a result, the fusion of 4D radar and camera can provide an affordable and robust perception solution for autonomous driving systems. However, previous radar-camera fusion methods have not yet been thoroughly investigated, resulting in a large performance gap compared to LiDAR-based methods. Specifically, they ignore the feature-blurring problem and do not deeply interact with image semantic information. To this end, we present a simple but effective multi-stage sampling fusion (MSSF) network based on 4D radar and camera. On the one hand, we design a fusion block that can deeply interact point cloud features with image features, and can be applied to commonly used single-modal backbones in a plug-and-play manner. The fusion block encompasses two types, namely, simple feature fusion (SFF) and multi-scale deformable feature fusion (MSDFF). The SFF is easy to implement, while the MSDFF has stronger fusion abilities. On the other hand, we propose a semantic-guided head to perform foreground-background segmentation on voxels with voxel feature re-weighting, further alleviating the problem of feature blurring. Extensive experiments on the View-of-Delft (VoD) and TJ4DRadset datasets demonstrate the effectiveness of our MSSF. Notably, compared to state-of-the-art methods, MSSF achieves a 7.0% and 4.0% improvement in 3D mean average precision on the VoD and TJ4DRadSet datasets, respectively. It even surpasses classical LiDAR-based methods on the VoD dataset.

Instance segmentation is a fundamental research in computer vision, especially in autonomous driving. However, manual mask annotation for instance segmentation is quite time-consuming and costly. To address this problem, some prior works attempt to apply weakly supervised manner by exploring 2D or 3D boxes. However, no one has ever successfully segmented 2D and 3D instances simultaneously by only using 2D box annotations, which could further reduce the annotation cost by an order of magnitude. Thus, we propose a novel framework called Multimodal Weakly Supervised Instance Segmentation (MWSIS), which incorporates various fine-grained label correction modules for both 2D and 3D modalities, along with a new multimodal cross-supervision approach. In the 2D pseudo label generation branch, the Instance-based Pseudo Mask Generation (IPG) module utilizes predictions for self-supervised correction. Similarly, in the 3D pseudo label generation branch, the Spatial-based Pseudo Label Generation (SPG) module generates pseudo labels by incorporating the spatial prior information of the point cloud. To further refine the generated pseudo labels, the Point-based Voting Label Correction (PVC) module utilizes historical predictions for correction. Additionally, a Ring Segment-based Label Correction (RSC) module is proposed to refine the predictions by leveraging the depth prior information from the point cloud. Finally, the Consistency Sparse Cross-modal Supervision (CSCS) module reduces the inconsistency of multimodal predictions by response distillation. Particularly, transferring the 3D backbone to downstream tasks not only improves the performance of the 3D detectors, but also outperforms fully supervised instance segmentation with only 5% fully supervised annotations. On the Waymo dataset, the proposed framework demonstrates significant improvements over the baseline, especially achieving 2.59% mAP and 12.75% mAP increases for 2D and 3D instance segmentation tasks, respectively. The code is available at https://github.com/jiangxb98/mwsis-plugin.

In the case of distributed target detection in unknown Gaussian noise, training data are often limited, and signal mismatch is a common issue. To tackle these problems, we utilize the Bayesian theory by taking the noise covariance matrix as an inverse Wishart distribution. Our approach involves incorporating a fictitious determinist jamming signal in the signal-absence hypothesis to create a selective detector. Although this detector provides enhanced capability to reject mismatched signals, it comes at the cost of lower detection performance in the absence of signal mismatch. We propose a flexible Bayesian detector to tackle this limitation, wherein a customizable parameter can regulate the performance of mismatched signals. The tunable Bayesian detector is particularly robust to signal mismatch. In addition, it can achieve a higher probability of detection (PD) compared with existing methods when the tunable parameter is appropriately adjusted for matched signals. All the proposed Bayesian detectors can work with limited training data or even with no training data. The effectiveness of the proposed detectors is demonstrated through both simulated and actual data.

In this letter, the problem of detecting a multiple subspace-based target in the presence of deterministic interference is considered. To solve the problem, we utilize the Kullback-Leibler information criterion and model order selection rules to design detection schemes. The alternative hypothesis related to the most likely signal subspace is selected from multiple alternative hypotheses, and is tested versus the null hypothesis for target detection. Numerical examples verify the effectiveness of the proposed detection schemes, which can achieve the target detection and subspace-based target classification simultaneously.

With the development of hyperspectral imaging technology, the hyperspectral anomaly has attracted considerable attention due to its significant role in many applications. Hyperspectral images (HSIs) with two spatial dimensions and one spectral dimension are intrinsically three-order tensors. However, most of the existing anomaly detectors were designed after converting the 3-D HSI data into a matrix, which destroys the multidimension structure. To solve this problem, in this article, we propose a spatial invariant tensor self-representation (SITSR) hyperspectral anomaly detection algorithm, which is derived based on the tensor–tensor product (t-product) to preserve the multidimension structure and achieve a comprehensive description of the global correlation of HSIs. Specifically, we exploit the t-product to integrate spectral information and spatial information, and the background image of each band is represented as the sum of the t-product of all bands and their corresponding coefficients. Considering the directionality of the t-product, we utilize two tensor self-representation methods with different spatial modes to obtain a more balanced and informative model. To depict the global correlation of the background, we merge the unfolding matrices of two representative coefficients and constrain them to lie in a low-dimensional subspace. Moreover, the group sparsity of anomaly is characterized by$l_{2.1.1}$norm regularization to promote the separation of background and anomaly. Extensive experiments conducted on several real HSI datasets demonstrate the superiority of SITSR compared with state-of-the-art anomaly detectors.

This article deals with the problem of tracking targets with X-band marine radars in the complicated sea clutter background. By jointly exploiting the target’s kinematic and appearance information, we propose a novel dynamic model-based Doppler-adaptive correlation filter. The proposed tracker mainly consists of two modules. First, a Doppler-adaptive correlation filter is developed based on the kernel correlation filter. This filter can effectively represent the appearance patterns of the target and is adaptive to the motion Doppler by using a multi-frequency centered filter bank. Second, the Bernoulli filter is employed to represent the kinematic patterns of the target. Within this filter, the converted measurement Kalman filter with range rate is applied to overcome the inconsistency between coordinate systems of the motion and measurement models. By exploiting the hybrid measurement likelihood, the two modules are then fused within the Bayesian framework to achieve improved tracking performance in the complicated sea clutter background. Experimental results based on both the simulated and real radar data demonstrate that the proposed tracker outperforms its representative counterparts.

Image registration is the basis for joint utilization of multisource scene information. However, accurate automatic registration of multisource remote sensing images remains a challenging task, especially for optical and synthetic aperture radar (SAR) images. Due to the large geometric and intensity differences between images, many algorithms often fail to accurately register or even mismatch. In this article, we propose a novel coarse-to-fine method with stable high accuracy, which mainly consists of three steps. First, extract consistent features for robust coarse registration. Considering the differences in local gradient magnitudes between optical and SAR images, image intensity preprocessing is performed. Meanwhile, instead of the simple calculations via horizontal and vertical gradient operators, multidirectional gradient operators are defined in two scale spaces to obtain consistent local gradient information. Via multidirectional consistent gradients, highly repeatable keypoints are detected. On the multidirectional gradient maps, support regions with multiple scales are utilized to construct multiscale and multidirectional consistent cross-modal feature descriptors. Second, match the extracted features. Aiming to obtain a reliable alignment in coarse registration step, novel strategies are implemented for the first matching of a cascaded feature matching method. Sufficiently reliable initial matches are established via a new two-way matching strategy, and obvious outliers are removed by exploring the consistencies of both spatial scale and local neighborhood elements of the correct matches. Third, form distinctive pixelwise feature representations for accurate fine registration. In order to increase the distinctiveness of features to distinguish adjacent pixels, a new filter bank based on small receptive fields is defined. Fine features are constructed, appearing as thinner structures at the edges. Meanwhile, through multiscale and multidirectional convolutions, sufficient neighborhood information is mined. Therefore, more precise correspondences can be found to fine-tune the roughly corrected image pair. Overall, a combination method is proposed with a feature-based method and an area-based method for coarse registration and fine registration, respectively. On simulated and real image pairs, the above three steps and the two-stage framework are verified. Experimental results show that the proposed optical-to-SAR image registration method based on the designed multiscale, multidirectional consistent feature and multiscale, multidirectional fine feature (M2F2M) is superior to the current representative feature-based and area-based methods in robustness and accuracy.

Hyperspectral anomaly detection, which is aimed at distinguishing anomaly pixels from the surroundings in spatial features and spectral characteristics, has attracted considerable attention due to its various applications. In this article, we propose a novel hyperspectral anomaly detection algorithm based on adaptive low-rank transform, in which the input hyperspectral image (HSI) is divided into a background tensor, an anomaly tensor, and a noise tensor. To take full advantage of the spatial–spectral information, the background tensor is represented as the product of a transformed tensor and a low-rank matrix. The low-rank constraint is imposed on frontal slices of the transformed tensor to depict the spatial–spectral correlation of the HSI background. Besides, we initialize a matrix with predefined size and then minimize its$l_{2.1}$-norm to adaptively derive an appropriate low-rank matrix. The anomaly tensor is constrained with the$l_{2.1.1}$-norm to depict the group sparsity of anomalous pixels. We integrate all regularization terms and a fidelity term into a non-convex problem and develop a proximal alternating minimization (PAM) algorithm to solve it. Interestingly, the sequence generated by the PAM algorithm is proven to converge to a critical point. Experimental results conducted on four widely used datasets demonstrate the superiority of the proposed anomaly detector over several state-of-the-art methods.

We consider a mixed analog-to-digital converter (ADC) based architecture for direction of arrival (DOA) estimation using a uniform linear array (ULA). We derive the Cramér-Rao bound (CRB) of the DOA under the optimal time-varying threshold, and find that the asymptotic CRB is related to the arrangement of high-precision and one-bit ADCs for a fixed number of ADCs. Then, a new concept called "mixed-precision arrangement" is proposed. It is proven that better performance for DOA estimation is achieved when high-precision ADCs are distributed evenly around the edges of the ULA. This result can be extended to a more general case where the ULA is equipped with various precision ADCs. Simulation results show the validity of the asymptotic CRB and better performance under the optimal mixed-precision arrangement.

Abstract For adaptive multichannel radar detection in the framework of multiple alternative hypotheses, where either the target or jamming could be present, a kind of two‐stage detector and classifier is proposed. Precisely, in the first stage, a decision is made on whether a target or a jamming exists. In the second stage, the decision is determined whether it is a target or a jamming. The detector is chosen as subspace‐based adaptive matched filter (SAMF) or adaptive energy detector (AED), while the classifier is selected as the subspace‐based adaptive beamformer orthogonal rejection test (SABORT), whitened SABORT (W‐SABORT), or orthogonal subspace‐based generalised likelihood ratio test (OSGLRT). Among these detectors and classifiers, the OSGLRT is proposed specially for classification in the scheme. Numerical experiments indicate that the proposed methods can achieve better detection and classification performance.

In this letter, we investigate the problem of detecting compressed stochastic sparse signals with unknown sparsity degree under Bernoulli–Gaussian model. In addition to the generalized likelihood ratio test (GLRT) proposed in [1], the corresponding Rao test and Wald test are derived in this letter. By observing that obtaining their analytical performance is challenging, we further propose a new probability constraint estimator (PCE) of the unknown sparsity degree. Interestingly, by adopting the PCE, the GLRT, Rao and Wald tests are shown to be statistically equivalent and reduce to a new detector (i.e., the detector with PCE) with a simple structure. The analytical performance of the detector with PCE is thus derived, which is verified by Monte Carlo simulations. Finally, numerical experiments illustrate that the proposed Rao test and the detector with PCE outperform the original GLRT.

It is often difficult to obtain sufficient training data for adaptive signal detection, which is required to calculate the unknown noise covariance matrix. Additionally, interference is frequently present, which complicates the detecting issue. We provide a two-step method, termed interference cancellation before detection (ICBD), to address the issue of signal detection in the unknown Gaussian noise and subspace interference. The first involves projecting the test and training data to the interference-orthogonal subspace in order to suppress the interference. Utilizing traditional adaptive detector design ideas is the next stage. Due to the smaller dimension of the projected data, the ICBD-based detectors can function with little training data. The ICBD has two additional benefits over traditional detectors. Lower computational burden and proper operation with interference being in the training data are two additional benefits of ICBD-based detectors over conventional ones. We also give the statistical properties of the ICBD-based detectors and demonstrate their equivalence with the traditional ones in the special case of a large amount of training data containing no interference.

Automatic emotion recognition based on electroencephalogram (EEG) has attracted rapidly increasing interests. Due to large inter-subject variabilities, subject-independent emotion recognition faces great challenges. Recently, domain adaptation methods have been successfully applied in this field due to their ability to align features from different subjects. However, since EEG signals corresponding to some emotions have similar oscillation patterns, they are often confused and aligned to the wrong categories, which limits the generalization ability of the model across subjects. Besides, almost all methods only support offline applications, which require collecting a large number of samples of new subjects. To achieve online recognition, a simpler model is needed. In this paper, a novel Gated Recurrent Unit-Minimum Class Confusion (GRU-MCC) model is proposed. Specifically, a simple feature extractor based on gated recurrent unit (GRU) is firstly applied to model the spatial dependence of multiple electrodes and obtain high-level discriminative features. Then, during training, minimum class confusion (MCC) loss is introduced to reduce the confusion between the correct and ambiguous classes for the target subject and increase the transfer gains. We conduct both offline and online experiments on two public datasets: SEED and MPED. The results indicate that our method can obtain the superior performance.

Recently, a generalized likelihood ratio test (GLRT)-based multipixel target detector for hyperspectral imagery was proposed. With joint exploitation of the pixels occupied by a target of interest, the detection performance was significantly improved. However, it still faces a pixel selection problem in practice. In this paper, we address the pixel selection problem for the multipixel target detector in practice. First, we propose an adaptive target pixel selection method based on spectral similarity and spatial connectivity characteristics. Second, we propose a method to collect the pixels spatially closest to the target pixels as the training background pixels so that their residual background component share the same statistical characteristics with high probability. To exclude potential target pixels in the collected training background pixels, an iterative version of the GLRT-based multipixel target detector is proposed. It is easy to set the key parameters of the proposed method, which is attractive in practice. Experimental results on four real hyperspectral datasets show that the proposed method outperforms its counterparts in terms of detection performance.

In this paper, we consider the detection problem of marine radar targets embedded in correlated non-Gaussian sea clutter, which is modelled by a compound Gaussian model with lognormal texture (CG-LN) and unknown covariance matrices. In order to reduce the dependence of detectors on training data, the original radar data are transformed via exploiting the persymmetric structure of clutter covariance matrix. Then, four complex parameter suboptimum tests, which are the Rao, Wald, gradient, and Durbin tests, are utilized to design the adaptive persymmetric coherent detectors for radar targets. It is shown that the Gradient test and the Durbin test coincide with the Rao test for the problem of radar target detection in CG-LN sea clutter. We prove that the proposed persymmetric Rao detector with lognormal texture (PRAO-LND) and persymmetric WALD detector with lognormal texture (PWALD-LND) can ensure the constant false alarm rate with respect to the clutter power mean and the clutter speckle covariance matrix. Experimental results on simulated and measured radar data show that the proposed PRAO-LND performs better than its competitors, and is robust to the mismatched signals. Moreover, the proposed PWALD-LND has asymptotic performance with the proposed PRAO-LND, when the non-Gaussianity of sea clutter is weakened, and has good selectivity with signal mismatch.

This paper deals with the detection problem of a moving point-like target in correlated non-Gaussian sea clutter, which is modelled by a compound Gaussian model with a lognormal-distributed texture and an unknown covariance matrix. In order to improve the detection performance for radar targets in sample-starved environments where the number of secondary data is limited, the persymmetric structure is exploited to transform the original radar data. Based on the two-step generalized likelihood ratio test (GLRT) and its maximum a posterior version, we propose two adaptive persymmetric coherent detectors for radar target detection. Theoretical and experimental confirmations are provided to show that the proposed detectors guarantee the constant false alarm rate property with respect to the clutter covariance matrix structure and the clutter power mean. Experimental results on simulated and measured radar data demonstrate that two proposed detectors perform better than traditional ones, especially when the number of secondary data is small.

In this paper, we consider the problem of robustly and accurately estimating the shape parameters ofK-distributed sea clutter in the maritime radar industry. Outliers formed by non-sea-surface echoes have a significant negative impact on the estimation accuracy. To improve the estimation performance, we first propose a bipercentiles feedforward neural network for the shape parameter$\eta$(BP-FFNN-$\eta$), which utilizes a ratio of two percentiles and a two-hidden-layer feedforward neural network. The BP-FFNN-$\eta$can learn the mathematical relationship between the shape parameter and the ratio of two percentiles, and can work in environments where the number of outliers is approximately known. Moreover, to solve the case where the number of outliers is not known due to dynamic changes in the environment, we also design another neural network (referred to as MBP-FFNN-$\eta$), which consists of multiple BP-FFNNs-$\eta$and a multi-class classification network. The MBP-FFNN-$\eta$can perceive the change in the proportion of outliers, so an accurate estimate can be obtained from an unaffected BP-FFNN-$\eta$. Finally, training and test data are constructed to train and evaluate the proposed methods, respectively. Experimental results demonstrate that the BP-FFNN-$\eta$performs better than traditional moments-based estimators, and has almost the same performance as the tri-percentile estimator. Compared with the tri-percentile estimator, the BP-FFNN-$\eta$avoids table lookups, and produces a continuous estimate. The MBP-FFNN-$\eta$can achieve more than 97% overall classification accuracy on simulated and measured data, and thus an accurate estimate of the shape parameter can be obtained when the number of outliers varies.

Nowadays, vehicles with a high level of automation are being driven everywhere. With the apparent success of autonomous driving technology, we keep working to achieve fully autonomous vehicles on roads. Efficient and accurate vehicle detection is one of the essential tasks in the environment perception of an autonomous vehicle. Therefore, numerous algorithms for vehicle detection have been developed. However, their strengths in terms of performance have not been deeply assessed or highlighted yet. This work comprehensively reviews the existing methods and datasets for vehicle detection considering their performances and applications in the field of autonomous driving. First, we briefly describe tasks, evaluation criteria, and existing public datasets for vehicle detection in autonomous driving. Second, we provide a rigorous review of both classical and latest vehicle detection methods, including machine vision-based, mmWave radar-based, LiDAR-based, and sensor fusion-based methods. Finally, we analyze the pertinent challenges of autonomous vehicles and provide recommendations for future works concerning vehicle detection. The present review covers over 300 research works and aims to help researchers interested in autonomous driving, especially in vehicle detection.

Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented as graph-structured data and thus utilizing graph neural networks (GNNs) for social recommendation has become a promising research direction. However, existing graph-based methods fails to consider the bias offsets of users (items). For example, a low rating from a fastidious user may not imply a negative attitude toward this item because the user tends to assign low ratings in common cases. Such statistics should be considered into the graph modeling procedure. While some past work considers this bias, we argue that these proposed methods only treat the bias as a scalar and can not capture the complete bias information hidden in data. Besides, social connections between users should also be differentiable so that users with similar item preference would have more influence on each other. To this end, we propose Graph-Based Decentralized Collaborative Filtering for Social Recommendation (GDSRec). GDSRec treats the bias as vectors and fuses them into the process of learning user and item representations. The statistical bias offsets are captured by decentralized neighborhood aggregation while the social connection strength is defined according to the preference similarity and then incorporated into the model design. We conduct extensive experiments on two benchmark datasets to verify the effectiveness of the proposed model. Experimental results show that the proposed GDSRec achieves superior performance compared with state-of-the-art related baselines. Our implementations are available in https://github.com/MEICRS/GDSRec.

In hyperspectral imagery, target detection algorithms are usually based on the spectral signature information. Due to the advance of the spatial resolution of hyperspectral sensors, the ground sample distance may be much smaller than the size of targets. As a result, targets often occupy multiple consecutive pixels, which are referred to as multi-pixel targets. In this paper, we investigate the target detection problem for multi-pixel targets in hyperspectral imagery, when the target spectral signature is known. Jointly exploiting the pixels occupied by a target of interest, we propose a multi-pixel target detector resorting to the generalized likelihood ratio test criterion. Closed-form expressions for the probabilities of the false alarm and detection are derived, which are verified using Monte Carlo simulations. Experimental results on four real hyperspectral datasets show that the proposed detector outperforms its counterparts.

Anomaly detection in hyperspectral images (HSIs) has attracted considerable interest in the remote-sensing domain, which aims to identify pixels with different spectral and spatial features from their surroundings. Most of the existing anomaly detection methods convert the 3-D data cube to a 2-D matrix composed of independent spectral vectors, which destroys the intrinsic spatial correlation between the pixels and their surrounding pixels, thus leading to considerable degradation in detection performance. In this article, we develop a tensor-based anomaly detection algorithm that can effectively preserve the spatial–spectral information of the original data. We first separate the 3-D HSI data into a background tensor and an anomaly tensor. Then the tensor nuclear norm based on the tensor singular value decomposition (SVD) is exploited to characterize the global low rank existing in both the spectral and spatial directions of the background tensor. In addition, the total variation (TV) regularization is incorporated due to the piecewise smoothness. For the anomaly component, the$l_{2.1}$norm is exploited to promote the group sparsity of anomalous pixels. In order to improve the ability of the algorithm to distinguish the anomaly from the background, we design a robust background dictionary. We first split the HSI data into local clusters by leveraging their spectral similarity and spatial distance. Then we develop a simple but effective way based on the SVD to select representative pixels as atoms. The constructed background dictionary can effectively represent the background materials and eliminate anomalies. Experimental results obtained using several real hyperspectral datasets demonstrate the superiority of the proposed method compared with some state-of-the-art anomaly detection algorithms.

In this letter, we consider the problem of detecting a signal in a kind of nonhomogeneity environment caused by random unknown interference. We propose an effective detector according to the detector design criterion of the two-step Durbin test or the two-step Wald test. The simulation results show that the proposed detector can provide slightly superior performance than that of existing detectors while maintaining approximately the same computational load, both under hypothetical interference conditions and other interference conditions.

We consider the detection problem of maritime radar targets in the training-sample-starved and non-Gaussian sea clutter environment. The performance of conventional detectors for radar targets is seriously degraded due to both the starvation of training samples for estimating the clutter covariance matrix and the non-Gaussianity of sea clutter. In this letter, we adopt the inverse Gaussian distribution and the inverse complex Wishart distribution to model the texture and speckle covariance matrix of sea clutter, respectively. Then an adaptive Bayesian detector is developed based on the two-step generalized likelihood ratio test and the maximum posterior estimates of clutter parameters. Finally, the experimental results on simulated and measured data demonstrate the performance superiority of the proposed detector over its competitors, especially when the training samples are starved.

In this letter, we consider the problem of target detection in unknown Gaussian noise for a colocated multi-input multi-output (MIMO) radar. To improve the detection performance, we adopt the training data, which were not utilized in existing references for the considered problem. We derive the generalized likelihood ratio test (GLRT) and Rao and Wald tests. Moreover, the corresponding analytical expressions for the probabilities of detection (PDs) and probabilities of false alarm (PFAs), which indicate that the proposed detectors have constant false alarm rate (CFAR) properties. Simulation results show that the proposed detectors can provide higher PDs than the existing detectors which do not utilize training data.

In this letter, we propose a frequency diverse array multiple-input multiple-output radar detection architecture for distributed targets embedded in Gaussian interference with unknown but stochastic covariance matrix. At the design stage, we model distributed targets within one range cell as a linear combination of several contributions and assume that the interference covariance matrix obeys the inverse complex Wishart distribution. Then, we devise an adaptive decision rule by jointly exploiting the maximum likelihood approach and the Bayesian framework. Unlike existing contributions in this context, the proposed detector does not require the conventional set of training data to estimate the interference covariance matrix. The numerical examples validate the effectiveness of the proposed method also in comparison with suitable counterparts.

In this letter, we consider the problem of detecting a multichannel subspace signal in the presence of deterministic interference and structure nonhomogeneity. We derive the gradient test, Durbin test, and their two-step (2S) variants. The gradient test and its 2S variant have the same form as the existing generalized likelihood ratio test for the same detection problem, whereas the Durbin test and its 2S variant are new detectors. Numerical examples show that the two proposed new detectors, i.e., the Durbin test and its 2S variant, can provide better detection performance in some scenarios. In particular, they are robust to signal mismatch, and can perform well when the structure nonhomogeneity is not serious.

To solve the problem of adaptive detection in partially homogeneous environment with outliers and limited samples, a class of two-step detectors are designed based on geometric barycenters. The first step is to construct a data selector based on generalized inner product and eliminate sample data containing outliers. The second step is to construct detection statistics of the adaptive coherence estimator using covariance matrix estimators, which are based on geometric barycenters. The detectors utilize geometric barycenters of the positive definite matrix space without any knowledge of prior probability distribution of sample data. The performance of the proposed two-step detectors is evaluated in terms of the probabilities of correct outliers excision, false alarm, and detection. Experiment results, based on simulated and real data, show that the proposed approach has better detection performance than the existing ones based on traditional covariance estimator.

This article deals with the problem of detecting a marine target with coherent radars in a correlated heavy-tailed sea clutter background. The heavy-tailed sea clutter is modeled by a compound-Gaussian model, and the clutter texture is characterized by the lognormal distribution with a new parameterization form. We develop an adaptive coherent detector on the basis of the two-step generalized likelihood ratio test. The proposed detector can achieve adaptation to sea clutter characteristics by using the maximum a posterior estimate of the clutter texture, the constrained approximate maximum likelihood estimator of the speckle covariance matrix, and the proposed negative- and positive-fractional moment estimate of amplitude parameters of sea clutter. Remarkably, the proposed detector inherently ensures a constant false alarm rate with respect to the clutter power mean and the speckle covariance matrix. Finally, numerical experiments using simulated data and real radar data demonstrate that the proposed estimator and adaptive coherent detector outperform their respective competitors.

Non-Gaussian sea clutter causes conventional detectors designed in Gaussian clutter to suffer detection performance degradation, and some nuisance parameters cause the uniformly most powerful test to be unavailable. To improve the detection performance of maritime radar targets, we investigate the design of adaptive detectors in correlated non-Gaussian sea clutter via using suboptimal tests. The non-Gaussian sea clutter is modelled as a product of lognormal-distributed texture and complex Gaussian speckle. Two adaptive radar target detectors are developed by using the suboptimal two-step Wald and Rao tests. Specifically, a non-adaptive detector is derived by the Wald or Rao test when the clutter texture and speckle covariance matrix are assumed to be known in the first step; then the clutter parameters known in the first step are estimated, and the true parameters of the detector obtained in the first step are replaced with the estimated values. Theoretical proof and experimental verification indicate that the two proposed detectors have the constant false alarm property with regard to the clutter speckle covariance matrix and the clutter average power. Numerical results on simulated and measured radar data show that the proposed Rao-based detector outperforms its competitors, and has the stronger robustness to the signal mismatch compared to the proposed Wald-based detector.

This article addresses the detection problem of radar targets embedded in nonhomogeneous and non-Gaussian sea clutter. Nonhomogeneity leads to insufficiency of secondary data for estimating the clutter speckle covariance matrix, and non-Gaussianity causes sea clutter to become spiky. In this article, the persymmetry of the clutter covariance matrix is adopted to alleviate the requirement of secondary data, and the prior distribution of clutter texture is exploited to tackle the clutter non-Gaussianity. Based on such clutter knowledge, three adaptive detectors are proposed according to the principles of the generalized likelihood ratio test, the Wald test, and the Rao test. It is proven that three detectors ensure constant false alarm rate (CFAR) properties with respect to both the clutter speckle covariance matrix and the clutter power mean. Simulation experiments show that three detectors outperform their competitors.

In this article, anomaly detection is considered for hyperspectral imagery in the Gaussian background with an unknown covariance matrix. The anomaly to be detected occupies multiple pixels with an unknown pattern. Two adaptive detectors are proposed based on the generalized likelihood ratio test design procedure and ad hoc modification of it. Surprisingly, it turns out that the two proposed detectors are equivalent. Analytical expressions are derived for the probability of false alarm of the proposed detector, which exhibits a constant false alarm rate against the noise covariance matrix. Numerical examples using simulated data reveal how some system parameters (e.g., the background data size and pixel number) affect the performance of the proposed detector. Experiments are conducted on five real hyperspectral data sets, demonstrating that the proposed detector achieves better detection performance than its counterparts.

In this paper, we investigate the anomaly detection problem for multi-pixel targets in hyperspectral imagery when training data are available. We derive the generalized likelihood ratio test and obtain its analytical expressions of the probability of false alarm and probability of detection. The performance of the proposed detector is evaluated by using simulated and real data. The results demonstrate that this training data assisted detector outperforms its counterpart without training data.

In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets' parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference-plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.

The spread spectrum signal detection problem is examined in colored noise with an unknown covariance matrix. When the receiver is equipped with a symmetrically spaced linear array, persymmetry exists in the received data. We exploit the persymmetric structures to design adaptive detectors according to the principles of generalized likelihood ratio test (GLRT), Wald test, and Rao test. It turns out that the proposed GLRT has the same form as the proposed Wald test, and the Rao test does not exist. We prove that the proposed detector exhibits a constant false alarm rate against the unknown noise covariance matrix. Numerical examples demonstrate that the proposed detector has better performance than its non-persymmetric counterpart.

In this letter, we focus on the statistical modeling of sea clutter amplitudes. Due to its non-Gaussian nature, the existing statistical models are sometimes difficult to represent well the heavy-tailed portion of amplitude distribution. To address this problem, we propose a compound Gaussian (CG) model with a generalized inverse Gaussian (GIG) texture to describe sea clutter amplitudes. In this regard, the probability density function and the cumulative distribution function of the clutter amplitudes for the proposed model are derived. Moreover, we provide an approach to estimate the unknown parameters of the proposed CG-GIG distribution. The experimental results indicate that the CG-GIG distribution is more suitable to describe the amplitudes of non-Gaussian sea clutter than its competitors.

In this letter, the problem of environment classification in the radar context is addressed. Specifically, adaptive architectures are conceived to classify training data, used for covariance estimation, as either homogeneous or heterogeneous. Such architectures are based upon the generalized likelihood ratio test criterion and exploit three covariance matrix structures (i.e., Hermitian, persymmetric, and symmetric structures). Numerical examples based on both synthetic and real data confirm the effectiveness of the proposed algorithms. It is important to highlight that the proposed architectures might represent a preliminary stage whose decisions can be used to select a suitable covariance estimate for target detection purposes.

The problem of detecting a subspace signal is studied in colored Gaussian noise with an unknown covariance matrix. In the subspace model, the target signal belongs to a known subspace, but with unknown coordinates. We propose a modified Rao test (MRT) by introducing a tunable parameter. The MRT is more general, which includes the Rao test and the generalized likelihood ratio test as special cases. Moreover, closed-form expressions for the probabilities of false alarm and detection of the MRT are derived. Numerical results demonstrate that the MRT can offer the flexibility of being adjustable in the mismatched case where the target signal deviates from the presumed signal subspace. In particular, the MRT provides better mismatch rejection capacities as the tunable parameter increases.

We propose a low complexity robust beamforming method for the general-rank signal model, to combat against mismatches of the desired signal array response and the received signal covariance matrix. The proposed beamformer not only considers the norm bounded uncertainties in the desired and received signal covariance matrices, but also includes an additional positive semidefinite constraint on the desired signal covariance matrix. Based on the worst-case performance optimization criterion, a computationally simple closed-form weight vector is obtained. Simulation results verify the validity and robustness of the proposed beamforming method.

Besides noise and potential targets, there usually exists jamming, which can significantly degrade detection performance of a detector. In this letter, we analyze the statistical performance of the adaptive orthogonal rejection detector (AORD), recently proposed for the case of completely unknown jamming. We derive closed-form expressions for the probabilities of detection and false alarm and show how the jamming affects the detection performance. The theoretical results are verified by Monte Carlo (MC) simulations.

This paper considers the problem of passive detection with a multistatic radar system involving a non-cooperative illuminator of opportunity (IO) and multiple receive platforms. An unknown source signal is transmitted by the IO, which illuminates a target of interest. These receive platforms are geographically dispersed, and collect independent target echoes due to the illumination by the same IO. We propose a generalized likelihood ratio test (GLRT) detector to deal with the passive detection problem in the case of unknown noise power. Moreover, a closed-form expression for the probability of false alarm of this GLRT detector is given. Numerical simulations demonstrate that the proposed GLRT detector generally outperforms its natural counterparts.

Our aim in this work is to analyze the detection performance of a constant false alarm rata matched filter (CFAR-MF) which was developed for the detection problem in white Gaussian noise with unknown noise power. An exact expression for the detection probability of the CFAR-MF is derived in the mismatched case where mismatch exists between the actual signal steering vector and the nominal one. This theoretical expression can be used to facilitate the performance evaluation of the CFAR-MF in real-world scenarios when signal mismatch cannot be neglected.

It is known that the probabilities of false alarm (PFAs) of several celebrated adaptive detectors including the adaptive matched filter (AMF) and the adaptive coherence estimator (ACE) can be expressed as integral forms. Nevertheless, it is inconvenient to set the detection thresholds by using these integral expressions. Here, we propose two computationally efficient schemes to calculate the thresholds of the AMF and ACE. In the first method, approximate expressions, in forms of elementary functions, for the PFAs of the AMF and ACE are derived. The thresholds of the AMF and ACE can be numerically computed by using these elementary expressions instead of the integrals, for reducing computational complexity. In the second approach, further approximations are employed to lead to highly simple expressions for the thresholds of the AMF and ACE, which enable us to directly compute the thresholds for a given PFA. Compared to the first one, the second scheme is more computationally efficient, but at the cost of a slight loss in accuracy. Numerical results verify the effectiveness of the two proposed schemes.

In this paper, we introduce a new max-margin discriminant projection method, which takes advantage of the latent variable representation for support vector machine (SVM) as the classification criterion. Specifically, the proposed model jointly learns the discriminative subspace and classifier in a Bayesian framework by conditioning on augmented variables. Moreover, an extended nonlinear model is developed based on the kernel trick, where the similar model can be used in this setting with few modifications. To explore the sparsity in the kernel expansion, we use the spike-and-slab prior to seek basis vectors (BVs) from the corresponding candidates. Unlike existing methods, which employ BVs to approximate the original feature space, in our method BVs are sought to associate the final classification task. Thanks to the conditionally conjugate property, the parameters in our models can be inferred via the simple and efficient Gibbs sampler. Finally, we test our methods on synthesized and real-world data, including large-scale data sets to demonstrate their efficiency and effectiveness.

We consider the problem of distributed detection of a mean parameter corrupted by Gaussian noise in wireless sensor networks, where a large number of sensor nodes jointly detect the presence of a weak unknown signal. To circumvent power/bandwidth constraints, a multilevel quantizer is employed in each sensor to quantize the original observation. The quantized data are transmitted through binary symmetric channels to a fusion center where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. The asymptotic performance analysis of the multibit GLRT detector is provided, showing that the detection probability is monotonically increasing with respect to the Fisher information (FI) of the unknown signal parameter. We propose a quantizer design approach by maximizing the FI with respect to the quantization thresholds. Since the FI is a nonlinear and nonconvex function of the quantization thresholds, we employ the particle swarm optimization algorithm for FI maximization. Numerical results demonstrate that with 2- or 3-bit quantization, the GLRT detector can provide detection performance very close to that of the unquantized GLRT detector, which uses the original observations without quantization.

We jointly design the transmit and receive beamforming based on a-priori information on the locations of target and interferences in an active array, where each transmit element emits the same waveform up to a complex scalar. A sequential optimization algorithm is proposed to maximize the output signal-to-interference-plus-noise ratio (SINR). Numerical results demonstrate that a significant gain in the output SINR can be achieve in this active array, compared to the conventional phased-array radar and omnidirectional multiple-input-multiple-output (MIMO) radar.

Based on a diversely polarized antenna, we address the problems of adaptive detection and performance enhancement in partially homogeneous environments where the test and training data samples share the same noise covariance matrix up to an unknown scaling factor. The matched subspace detector and adaptive subspace detector are employed to handle the detection problem for the cases of known and unknown noise covariance matrix structure, respectively. The performance of the two detectors is evaluated in terms of their probabilities of false alarm and detection. In particular, a waveform design algorithm to enhance the detection performance of the two detectors is proposed. A numerical analysis is presented to demonstrate the potential performance improvement obtained with this algorithm.

In the above-named letter [ibid., vol. 14, no. 11, pp. 864-866, Nov. 2007], Nadarajah derived explicit expressions for the probabilities of false alarm and detection of the normalized adaptive matched filter (NAMF) in Gaussian clutter. However, there is an error in the derivation of the expression for the probability of false alarm. The purpose of this comment is to correct the error.