In target tracking with sensor network under decentralized architecture, the sensor network resources including sensing and communication resources are always constrained, meanwhile, the quality of measurements acquired by different sensor nodes toward the same target usually differs from each other. Both the selected sensor nodes and communication topology among them significantly affect not only the tracking accuracy but also network resource consumption. Thus, to improve resource utilization efficiency, this paper proposes a sensor network resource management method for target tracking under decentralized architecture, focusing on the selection of sensor nodes acquiring high-quality measurements and the optimization of their communication topology. First, this paper derives an iteration-adaptive decentralized PCRLB(IA-DPCRLB) as a tracking accuracy metric, and a tracking accuracy constraint function is established based on the IA-DPCRLB. Additionally, an objective function is constructed by modeling both the sensor scheduling cost and the communication cost. Finally, to solve the non-convex optimization problem, an enumeration-based genetic algorithm is employed. Simulation results demonstrate that the proposed algorithm can adaptively select sensor nodes and dynamically optimize the communication topology according to the target’s motion state, achieving the minimized system resource consumption while ensuring the predefined tracking accuracy, thereby significantly improving resource utilization efficiency.

In the past decade, object detection technology has developed rapidly. However, in the field of unmanned aerial vehicle (UAV) image object detection, challenges such as complex environments, numerous and dense small objects, and weak features make object detection from the UAV perspective a highly challenging task. To address these issues, this letter proposes a U-Shaped feature extraction and fusion network (U-ShapeNet). Specifically: first, to enhance the network’s feature extraction capability and improve the perception of small objects, we design a novel U-Shaped feature extraction network (U-SFEN) and introduce a tiny object detection head. Second, a large kernel feature selection module (LKFSM) is constructed to strengthen the network’s contextual information learning ability and effectively distinguish small objects from complex background noise. Third, a same-scale feature enhancement module (SFEM) is proposed to mitigate information decay by reusing same-scale feature maps. Experiments on the VisDrone2019 and HazyDet datasets demonstrate that U-ShapeNet outperforms current mainstream object detectors, achieving state-of-the-art performance.

In RGB-infrared aerial image object detection, fully utilizing the advantages of both RGB and infrared images for effective detection is a key challenge in this field. In response to the challenges outlined, an RGB-infrared multibranch progressive feature fusion method for object detection in aerial images is proposed. Specifically, since accurate detection of objects in aerial images usually requires extensive context information, considering the connection between objects and backgrounds, the global-local synergistic attention (GLSA) is constructed. To mitigate the impact of feature domain differences between RGB and infrared images during the complementary information filtering process, a multimodal complementary information filter (MCIF) for RGB and infrared images is designed on the basis of GLSA. To fully exploit the advantages of the two types of model images, a multibranch progressive feature fusion network is designed. The multibranch progressive feature fusion network progressively fuses the enhanced features from the GLSA with the filtered complementary information by the MCIF, resulting in a final fused feature that integrates the rich visual details of RGB images with the distinctive physical characteristics of infrared images. Results against publicly released datasets demonstrate that the proposed method achieves state-of-the-art detection performance.

Multimodal remote sensing image object detection enhances detection accuracy by fusing complementary information from multimodal image data. However, complex environments significantly affect the reliability and complementarity of multimodal data, and traditional methods struggle to dynamically adapt to environmental changes, leading to degraded detection performance. To address this challenge, in this paper, we propose a multimodal remote sensing object detection method based on a prior information-enhanced mixture-of-experts fusion network. Specifically, we first introduce a prior information-enhanced mixture-of-experts fusion network framework to achieve environment-adaptive multimodal image feature fusion. Secondly, we propose a dynamic gating network that combines prior information and multimodal image features to endow the system with environmental perception capabilities. This network is employed to dynamically allocate weights to sub-fusion experts optimized for different environmental conditions within the mixture-of-experts fusion network framework. Furthermore, to fully exploit the complementary information present in multimodal image features, we propose a frequency-decoupled feature fusion network as a sub-fusion expert within the mixture-of-experts fusion network framework. This utilizes wavelet transform to decouple the features of each modality and then develops personalized fusion strategies for each frequency subband. In addition, to enhance detection efficiency, we introduce a cross-scale feature channel interleaved fusion strategy, which significantly reduces computational cost while ensuring stable detection performance. Experimental results on the DroneVehicle and RGBT-Tiny datasets demonstrate that our method achieves competitive performance compared to state-of-the-art approaches. Code will be available at: https://github.com/LiuKewei0110/MDPMFN.

To improve target detection performance in SAR images, the YOLOV5 network is modified at four different parts, including the input, backbone, neck, and head modules. At the input end, the local binary patterns (LBP) feature is extracted and concatenated with the original SAR image to enhance low-level texture features. In the backbone network, the deformable convolution network (DCN) is utilized to modify the C3 module to enhance geometrical invariance. The content-aware reassembly of features (CARAFE) upsampling module is used in the neck network to improve the feature extraction performance. Finally, the adaptively spatial feature fusion (ASFF) module is adopted to emphasize key features, thus the prediction feature maps at different levels are further integrated. The experiment against the FaradSAR dataset validates the performance of the proposed method.

In passive bistatic radar, the computational efficiency of clutter suppression algorithms remains low, due to continuous increases in bandwidth for potential illuminators of opportunity and the use of multi-source detection frameworks. Accordingly, we propose a lightweight version of the extensive cancellation algorithm (ECA), which achieves clutter suppression performance comparable to that of ECA while reducing the computational and space complexities by at least one order of magnitude. This is achieved through innovative adjustments to the reference signal subspace matrix within the ECA framework, resulting in a redefined approach to the computation of the autocorrelation matrix and cross-correlation vector. This novel modification significantly simplifies the computational aspects. Furthermore, we introduce a dimension-expanding technique that streamlines clutter estimation. Overall, the proposed method replaces the computation-intensive aspects of the original ECA with fast Fourier transform (FFT) and inverse FFT operations, and eliminates the construction of the memory-intensive signal subspace. Comparing the proposed method with ECA and its batched version (ECA-B), the central advantages are more streamlined implementation and minimal storage requirements, all without compromising performance. The efficacy of this approach is demonstrated through both simulations and field experimental results.

The modeling of channel and temporal information is of crucial importance for action recognition tasks. To build a high-performance action recognition network by effectively capturing channel and temporal information, we propose CLS-Net: an action recognition algorithm based on channel-temporal information modeling. The proposed CLS-Net characterizes channel and temporal information by inserting multiple modules to an end-to-end backbone network, including a channel attention module (CA module) for modeling channel information, a long-term temporal module (LT module) and a short-term temporal module (ST module) for modeling temporal information. Specifically, the CA module extracts the correlation between feature channels so the network can learn to selectively strengthen the features containing useful information and suppress the useless features through global information. The LT module moves some channels in the temporal dimension to realize information interaction across time domains and model global temporal information. The ST module enhances the motion-sensitive features by calculating the feature-level frame difference information and realizes the representation of local motion information. Since the multi-module insertion mode directly affects the whole model’s final performance, we propose a novel multi-module insertion mode instead of a simple series or parallel connection to ensure that the multiple modules can complement one another and cooperate with each other more efficiently. CLS-Net achieves SOTA performance on the EgoGesture and Jester dataset in the same type of network and achieves competitive results on the Something-Something V2 dataset.

This article deals with the problem of electromagnetic source localization (ESL). An evolutionary particle filter, which is first used to make a decision on the positions of electromagnetic sources, has two characteristics. One characteristic is that the number of particles can be significantly reduced while the other characteristic is that the particle diversity can be well improved. On the basis of the estimated positions of electromagnetic sources, the position and velocity of the virtual leader can be determined. Then, an event-based fixed-time consensus control approach is proposed such that the positions and velocities of robots reach consensus with the virtual leader over a fixed-time interval while saving resource consumption by reducing the communication frequencies and updating times of control inputs. Finally, simulation and experimental results show the effectiveness of the proposed decision and event-based fixed-time consensus control approach for ESL.

One open issue of target detection for synthetic aperture radar (SAR) images is the capture effect from the clutter edge and the interfering outliers, including surrounding targets in the multitarget environment, sidelobes, and ghosts. To address this issue, a superpixel-level constant false-alarm rate (CFAR) detector is proposed based on the truncated Gamma statistics for the multilook intensity SAR data. Superpixel segmentation serves as a preprocessing procedure to divide the SAR image into meaningful patches. By automatic clutter truncation in the superpixel-level background clutter window, the real clutter samples are preserved. The experimental results with real SAR images demonstrate that the proposed method achieves better goodness-of-fit performance for the real clutter background with outlier exclusion, yielding a higher target detection rate in the multitarget environments.

To reduce the influence of speckle noise on the pixel-level target detection method, a novel ship detection method is proposed for polarimetric synthetic aperture radar (PoISAR) images based on superpixelwise local contrast measurement. In the proposed method, superpixels are firstly generated based on the improved simple linear iterative clustering (SLIC) method for polarimetric SAR image. Then the local contrast measurement is calculated between a certain superpixel and its neighborhood superpixels to enhance the discriminability between ship targets and clutter background. Thus, better target detection performance can be obtained. The effectiveness of the proposed method can be demonstrated by experiments on both quad-polarized and dual-polarized real SAR images.

Online learning for imbalanced streaming data is an important and challenging problem for many classification tasks in the machine learning research field. Traditional online learning algorithms are mainly focused on classification tasks with balanced data, and with little consideration about the characteristics of imbalanced streaming data. In this paper, we propose a novel online learning algorithm called Truncated Gradient Confidence-Weighted (TGCW), which integrate the truncated gradient algorithm with the confidence weighted algorithm together to improve the feature selection ability while reducing the dimensions of imbalanced streaming data effectively. We study a number of classification tasks with various imbalance data ratio including the pedestrian detection application and compare the performance of the TGCW algorithm with traditional online learning algorithms, and empirical results show that the TGCW algorithm can achieve better performance consistently than other baseline approaches.

The paper presents a passivity-based control law for an antagonistic tendon-driven mechanism. It is proven, by using the passivity theorem, that the proposed control law is able to achieve two properties such as the passivity of interconnected subsystems when the external torque is applied and the global asymptotic stability during free motion when the external force is absent. The proposed controller is simple to be implemented for a complex tendon-driven mechanism because it requires only gravity compensation. In addition, it brings a robustness to the entire control system. And finally, the control strategy can be treated as one of the impedance control schemes so as to achieve the desired performance efficiently.

In order to solve the problem of multi-target tracking in clutter with unknown density, a Gaussian mixture probability hypothesis density (GMPHD) based multi-scan clutter sparsity estimation (MCSE) algorithm is proposed. First, the GMPHD filter is used to estimate the cardinality and state of the target with the clutter density in last step. Then all Measurements originated from the targets are eliminated online, which helps to reduce the effects on the clutter density estimation of target-originated measurements. Last, a multi-scan clutter sparsity estimation algorithm is proposed to update the current clutter density. Simulation results verify the effectiveness of the proposed algorithm.

Model set adaptation (MSA) plays a key role in the variable structure estimation approach (VSMM). In this paper, we adopt the error-ambiguity decomposition (EAD) principle into the VSMM framework and derive the optimal EAD-MSA criteria. By proposing some approximation methods, an EAD variable structure interactive multiple model algorithm (EAD-VSIMM) is constructed. We test the EAD-VSIMM algorithm in a maneuvering target tracking scenario and the results demonstrate that, compared to two benchmark MM algorithms, the proposed EAD-VSIMM algorithm can achieve more robust and accurate estimation results.

Bearing-only multi-target localization (BOMTL) using multiple sensors is generally required to solve the sophisticated data association problem which determines a designated sensor measurement originated from a particular target. In this paper, a novel spatial sparse representation based BOMTL method is proposed by fully utilizing a wireless array network structure. With array spatial features, the BOMTL problem can be formulated as a binary sparse vector recovery problem using the converted “pseudo-measurements” in frequency domain. The proposed method transforms the source location estimation problem into a spatial sparse representation (SSR) framework, which avoids dealing with the conventional data association. With orthogonal matching pursuit (OMP) exploiting the binary property of the sparse vector to be estimated, we develop a BOMTL-OMP algorithm to reconstruct the sparse vector. The numerical simulations demonstrate the performance of the proposed method.

To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter (MVRPF) is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

The theory of fuzzy sets has been used to deal with image enhancement problems for degraded images in which the image edges are uncertain and inaccurate. For those kinds of images, to some extent, the good enhancement effect can be obtained using the fuzzy sets-based image enhancement method instead of the traditional image enhancement approaches. The gray level maximum has not been changed in the classical fuzzy enhancement method proposed by S. K. Pal, so this method is not fit for the enhancement problem of degraded images with less gray levels and low contrasts; the fact that the range of membership function of gray levels is not normalization form, i.e. [0,1], is another disadvantage of the traditional fuzzy enhancement approach. To deal with the problems mentioned above, a generalized iterative fuzzy enhancement algorithm is proposed in this paper. A new image quality assessment criterion is suggested on the basis of the statistical features of the gray-level histogram of images to control the iterative procedure of the proposed image enhancement algorithm. Computer simulation results showed that this new enhancement method is more suitable than fuzzy enhancement and gray-level transformation for handling the enhancement problems of images with less gray levels and low contrasts.

On the basis of the generalized image enhancement algorithm using fuzzy sets and improved labeling method, a new recognizing method for planar objects is proposed in this paper. Firstly, a generalized iterative fuzzy enhancement algorithm is proposed which consists of a three-stage procedure, i.e., image filtering, fuzzy enhancement and gray-level transformation. A canonical form of membership function in the stage of fuzzy enhancement is proposed which remains the advantages of the original fuzzy enhancement and the gray level transformation while transforming the membership function of the gray scale to, and therefore is suitable for handling the enhancement problems of the images that have less gray levels and low contrast. Secondly, a new objective image quality assessment criterion is suggested according to the statistical features of the gray-level histogram of images to control the iterative procedure of the proposed image enhancement algorithm. Thirdly, an improved labeling method for image segmentation is given. Using this novel labeling method in image segmentation, it is not necessary to determine an equivalence table that needs to be listed in the usual sequential component algorithm. Computer simulation results for a degraded gray image show that this proposed recognizing method is efficient.